Home Blog Design Understanding Data Presentations (Guide + Examples)

Understanding Data Presentations (Guide + Examples)

Cover for guide on data presentation by SlideModel

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

  • Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
  • Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
  • Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
  • Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
  • Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
  • Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
  • Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Presentation of the data through bar charts

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

  • Product A: Men’s Shoes
  • Product B: Women’s Apparel
  • Product C: Electronics
  • Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

  • Men’s Shoes (Product A): Yellow
  • Women’s Apparel (Product B): Orange
  • Electronics (Product C): Violet
  • Home Decor (Product D): Blue

Accurate bar chart representation of data with a color coded legend

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

  • January: $45,000
  • February: $55,000
  • March: $45,000
  • April: $60,000
  • May: $ 70,000
  • June: $65,000
  • July: $62,000
  • August: $68,000
  • September: $81,000
  • October: $76,000
  • November: $87,000
  • December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Line graph in data presentation

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Data analysis presentation example

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

  • Top-level rectangle: Total Budget
  • Second-level rectangles: Departments (Engineering, Marketing, Sales)
  • Third-level rectangles: Projects within each department
  • Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Treemap used for presenting data

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

  • Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
  • Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

  • Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
  • Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
  • Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
  • Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

Pie chart template in data presentation

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

3D pie chart in data presentation

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.


After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

  • 60-69: 1 student (Kate)
  • 70-79: 4 students (David, Emma, Grace, Jack)
  • 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
  • 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

Histogram in Data Presentation

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

Participant IDDaily Hours of Screen TimeSleep Quality Rating

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

Scatter plot in data presentation

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

presentation of data conclusion

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

presentation of data conclusion

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

presentation of data conclusion

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

presentation of data conclusion

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

Canvas Shape Tree Diagram Template

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

presentation of data conclusion

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

presentation of data conclusion

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

presentation of data conclusion

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

presentation of data conclusion

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

presentation of data conclusion

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

presentation of data conclusion

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

presentation of data conclusion

Like this article? Please share

Data Analysis, Data Science, Data Visualization Filed under Design

Related Articles

How To Make a Graph on Google Slides

Filed under Google Slides Tutorials • June 3rd, 2024

How To Make a Graph on Google Slides

Creating quality graphics is an essential aspect of designing data presentations. Learn how to make a graph in Google Slides with this guide.

How to Make a Presentation Graph

Filed under Design • March 27th, 2024

How to Make a Presentation Graph

Detailed step-by-step instructions to master the art of how to make a presentation graph in PowerPoint and Google Slides. Check it out!

All About Using Harvey Balls

Filed under Presentation Ideas • January 6th, 2024

All About Using Harvey Balls

Among the many tools in the arsenal of the modern presenter, Harvey Balls have a special place. In this article we will tell you all about using Harvey Balls.

Leave a Reply

presentation of data conclusion


30 Examples: How to Conclude a Presentation (Effective Closing Techniques)

By Status.net Editorial Team on March 4, 2024 — 9 minutes to read

Ending a presentation on a high note is a skill that can set you apart from the rest. It’s the final chance to leave an impact on your audience, ensuring they walk away with the key messages embedded in their minds. This moment is about driving your points home and making sure they resonate. Crafting a memorable closing isn’t just about summarizing key points, though that’s part of it, but also about providing value that sticks with your listeners long after they’ve left the room.

Crafting Your Core Message

To leave a lasting impression, your presentation’s conclusion should clearly reflect your core message. This is your chance to reinforce the takeaways and leave the audience thinking about your presentation long after it ends.

Identifying Key Points

Start by recognizing what you want your audience to remember. Think about the main ideas that shaped your talk. Make a list like this:

  • The problem your presentation addresses.
  • The evidence that supports your argument.
  • The solution you propose or the action you want the audience to take.

These key points become the pillars of your core message.

Contextualizing the Presentation

Provide context by briefly relating back to the content of the whole presentation. For example:

  • Reference a statistic you shared in the opening, and how it ties into the conclusion.
  • Mention a case study that underlines the importance of your message.

Connecting these elements gives your message cohesion and makes your conclusion resonate with the framework of your presentation.

30 Example Phrases: How to Conclude a Presentation

  • 1. “In summary, let’s revisit the key takeaways from today’s presentation.”
  • 2. “Thank you for your attention. Let’s move forward together.”
  • 3. “That brings us to the end. I’m open to any questions you may have.”
  • 4. “I’ll leave you with this final thought to ponder as we conclude.”
  • 5. “Let’s recap the main points before we wrap up.”
  • 6. “I appreciate your engagement. Now, let’s turn these ideas into action.”
  • 7. “We’ve covered a lot today. To conclude, remember these crucial points.”
  • 8. “As we reach the end, I’d like to emphasize our call to action.”
  • 9. “Before we close, let’s quickly review what we’ve learned.”
  • 10. “Thank you for joining me on this journey. I look forward to our next steps.”
  • 11. “In closing, I’d like to thank everyone for their participation.”
  • 12. “Let’s conclude with a reminder of the impact we can make together.”
  • 13. “To wrap up our session, here’s a brief summary of our discussion.”
  • 14. “I’m grateful for the opportunity to present to you. Any final thoughts?”
  • 15. “And that’s a wrap. I welcome any final questions or comments.”
  • 16. “As we conclude, let’s remember the objectives we’ve set today.”
  • 17. “Thank you for your time. Let’s apply these insights to achieve success.”
  • 18. “In conclusion, your feedback is valuable, and I’m here to listen.”
  • 19. “Before we part, let’s take a moment to reflect on our key messages.”
  • 20. “I’ll end with an invitation for all of us to take the next step.”
  • 21. “As we close, let’s commit to the goals we’ve outlined today.”
  • 22. “Thank you for your attention. Let’s keep the conversation going.”
  • 23. “In conclusion, let’s make a difference, starting now.”
  • 24. “I’ll leave you with these final words to consider as we end our time together.”
  • 25. “Before we conclude, remember that change starts with our actions today.”
  • 26. “Thank you for the lively discussion. Let’s continue to build on these ideas.”
  • 27. “As we wrap up, I encourage you to reach out with any further questions.”
  • 28. “In closing, I’d like to express my gratitude for your valuable input.”
  • 29. “Let’s conclude on a high note and take these learnings forward.”
  • 30. “Thank you for your time today. Let’s end with a commitment to progress.”

Summarizing the Main Points

When you reach the end of your presentation, summarizing the main points helps your audience retain the important information you’ve shared. Crafting a memorable summary enables your listeners to walk away with a clear understanding of your message.

Effective Methods of Summarization

To effectively summarize your presentation, you need to distill complex information into concise, digestible pieces. Start by revisiting the overarching theme of your talk and then narrow down to the core messages. Use plain language and imagery to make the enduring ideas stick. Here are some examples of how to do this:

  • Use analogies that relate to common experiences to recap complex concepts.
  • Incorporate visuals or gestures that reinforce your main arguments.

The Rule of Three

The Rule of Three is a classic writing and communication principle. It means presenting ideas in a trio, which is a pattern that’s easy for people to understand and remember. For instance, you might say, “Our plan will save time, cut costs, and improve quality.” This structure has a pleasing rhythm and makes the content more memorable. Some examples include:

  • “This software is fast, user-friendly, and secure.”
  • Pointing out a product’s “durability, affordability, and eco-friendliness.”

Reiterating the Main Points

Finally, you want to circle back to the key takeaways of your presentation. Rephrase your main points without introducing new information. This reinforcement supports your audience’s memory and understanding of the material. You might summarize key takeaways like this:

  • Mention the problem you addressed, the solution you propose, and the benefits of this solution.
  • Highlighting the outcomes of adopting your strategy: higher efficiency, greater satisfaction, and increased revenue.

Creating a Strong Conclusion

The final moments of your presentation are your chance to leave your audience with a powerful lasting impression. A strong conclusion is more than just summarizing—it’s your opportunity to invoke thought, inspire action, and make your message memorable.

Incorporating a Call to Action

A call to action is your parting request to your audience. You want to inspire them to take a specific action or think differently as a result of what they’ve heard. To do this effectively:

  • Be clear about what you’re asking.
  • Explain why their action is needed.
  • Make it as simple as possible for them to take the next steps.

Example Phrases:

  • “Start making a difference today by…”
  • “Join us in this effort by…”
  • “Take the leap and commit to…”

Leaving a Lasting Impression

End your presentation with something memorable. This can be a powerful quote, an inspirational statement, or a compelling story that underscores your main points. The goal here is to resonate with your audience on an emotional level so that your message sticks with them long after they leave.

  • “In the words of [Influential Person], ‘…'”
  • “Imagine a world where…”
  • “This is more than just [Topic]; it’s about…”

Enhancing Audience Engagement

To hold your audience’s attention and ensure they leave with a lasting impression of your presentation, fostering interaction is key.

Q&A Sessions

It’s important to integrate a Q&A session because it allows for direct communication between you and your audience. This interactive segment helps clarify any uncertainties and encourages active participation. Plan for this by designating a time slot towards the end of your presentation and invite questions that promote discussion.

  • “I’d love to hear your thoughts; what questions do you have?”
  • “Let’s dive into any questions you might have. Who would like to start?”
  • “Feel free to ask any questions, whether they’re clarifications or deeper inquiries about the topic.”

Encouraging Audience Participation

Getting your audience involved can transform a good presentation into a great one. Use open-ended questions that provoke thought and allow audience members to reflect on how your content relates to them. Additionally, inviting volunteers to participate in a demonstration or share their experiences keeps everyone engaged and adds a personal touch to your talk.

  • “Could someone give me an example of how you’ve encountered this in your work?”
  • “I’d appreciate a volunteer to help demonstrate this concept. Who’s interested?”
  • “How do you see this information impacting your daily tasks? Let’s discuss!”

Delivering a Persuasive Ending

At the end of your presentation, you have the power to leave a lasting impact on your audience. A persuasive ending can drive home your key message and encourage action.

Sales and Persuasion Tactics

When you’re concluding a presentation with the goal of selling a product or idea, employ carefully chosen sales and persuasion tactics. One method is to summarize the key benefits of your offering, reminding your audience why it’s important to act. For example, if you’ve just presented a new software tool, recap how it will save time and increase productivity. Another tactic is the ‘call to action’, which should be clear and direct, such as “Start your free trial today to experience the benefits first-hand!” Furthermore, using a touch of urgency, like “Offer expires soon!”, can nudge your audience to act promptly.

Final Impressions and Professionalism

Your closing statement is a chance to solidify your professional image and leave a positive impression. It’s important to display confidence and poise. Consider thanking your audience for their time and offering to answer any questions. Make sure to end on a high note by summarizing your message in a concise and memorable way. If your topic was on renewable energy, you might conclude by saying, “Let’s take a leap towards a greener future by adopting these solutions today.” This reinforces your main points and encourages your listeners to think or act differently when they leave.

Frequently Asked Questions

What are some creative strategies for ending a presentation memorably.

To end your presentation in a memorable way, consider incorporating a call to action that engages your audience to take the next step. Another strategy is to finish with a thought-provoking question or a surprising fact that resonates with your listeners.

Can you suggest some powerful quotes suitable for concluding a presentation?

Yes, using a quote can be very effective. For example, Maya Angelou’s “People will forget what you said, people will forget what you did, but people will never forget how you made them feel,” can reinforce the emotional impact of your presentation.

What is an effective way to write a conclusion that summarizes a presentation?

An effective conclusion should recap the main points succinctly, highlighting what you want your audience to remember. A good way to conclude is by restating your thesis and then briefly summarizing the supporting points you made.

As a student, how can I leave a strong impression with my presentation’s closing remarks?

To leave a strong impression, consider sharing a personal anecdote related to your topic that demonstrates passion and conviction. This helps humanize your content and makes the message more relatable to your audience.

How can I appropriately thank my audience at the close of my presentation?

A simple and sincere expression of gratitude is always appropriate. You might say, “Thank you for your attention and engagement today,” to convey appreciation while also acknowledging their participation.

What are some examples of a compelling closing sentence in a presentation?

A compelling closing sentence could be something like, “Together, let’s take the leap towards a greener future,” if you’re presenting on sustainability. This sentence is impactful, calls for united action, and leaves your audience with a clear message.

  • How to Build Rapport: Effective Techniques
  • Active Listening (Techniques, Examples, Tips)
  • Effective Nonverbal Communication in the Workplace (Examples)
  • What is Problem Solving? (Steps, Techniques, Examples)
  • 2 Examples of an Effective and Warm Letter of Welcome
  • 8 Examples of Effective Interview Confirmation Emails

A Guide to Effective Data Presentation

Key objectives of data presentation, charts and graphs for great visuals, storytelling with data, visuals, and text, audiences and data presentation, the main idea in data presentation, storyboarding and data presentation, additional resources, data presentation.

Tools for effective data presentation

Financial analysts are required to present their findings in a neat, clear, and straightforward manner. They spend most of their time working with spreadsheets in MS Excel, building financial models , and crunching numbers. These models and calculations can be pretty extensive and complex and may only be understood by the analyst who created them. Effective data presentation skills are critical for being a world-class financial analyst .

Data Presentation

It is the analyst’s job to effectively communicate the output to the target audience, such as the management team or a company’s external investors. This requires focusing on the main points, facts, insights, and recommendations that will prompt the necessary action from the audience.

One challenge is making intricate and elaborate work easy to comprehend through great visuals and dashboards. For example, tables, graphs, and charts are tools that an analyst can use to their advantage to give deeper meaning to a company’s financial information. These tools organize relevant numbers that are rather dull and give life and story to them.

Here are some key objectives to think about when presenting financial analysis:

  • Visual communication
  • Audience and context
  • Charts, graphs, and images
  • Focus on important points
  • Design principles
  • Storytelling
  • Persuasiveness

For a breakdown of these objectives, check out Excel Dashboards & Data Visualization course to help you become a world-class financial analyst.

Charts and graphs make any financial analysis readable, easy to follow, and provide great data presentation. They are often included in the financial model’s output, which is essential for the key decision-makers in a company.

The decision-makers comprise executives and managers who usually won’t have enough time to synthesize and interpret data on their own to make sound business decisions. Therefore, it is the job of the analyst to enhance the decision-making process and help guide the executives and managers to create value for the company.

When an analyst uses charts, it is necessary to be aware of what good charts and bad charts look like and how to avoid the latter when telling a story with data.

Examples of Good Charts

As for great visuals, you can quickly see what’s going on with the data presentation, saving you time for deciphering their actual meaning. More importantly, great visuals facilitate business decision-making because their goal is to provide persuasive, clear, and unambiguous numeric communication.

For reference, take a look at the example below that shows a dashboard, which includes a gauge chart for growth rates, a bar chart for the number of orders, an area chart for company revenues, and a line chart for EBITDA margins.

To learn the step-by-step process of creating these essential tools in MS Excel, watch our video course titled “ Excel Dashboard & Data Visualization .”  Aside from what is given in the example below, our course will also teach how you can use other tables and charts to make your financial analysis stand out professionally.

Financial Dashboard Screenshot

Learn how to build the graph above in our Dashboards Course !

Example of Poorly Crafted Charts

A bad chart, as seen below, will give the reader a difficult time to find the main takeaway of a report or presentation, because it contains too many colors, labels, and legends, and thus, will often look too busy. It also doesn’t help much if a chart, such as a pie chart, is displayed in 3D, as it skews the size and perceived value of the underlying data. A bad chart will be hard to follow and understand.

bad data presentation

Aside from understanding the meaning of the numbers, a financial analyst must learn to combine numbers and language to craft an effective story. Relying only on data for a presentation may leave your audience finding it difficult to read, interpret, and analyze your data. You must do the work for them, and a good story will be easier to follow. It will help you arrive at the main points faster, rather than just solely presenting your report or live presentation with numbers.

The data can be in the form of revenues, expenses, profits, and cash flow. Simply adding notes, comments, and opinions to each line item will add an extra layer of insight, angle, and a new perspective to the report.

Furthermore, by combining data, visuals, and text, your audience will get a clear understanding of the current situation,  past events, and possible conclusions and recommendations that can be made for the future.

The simple diagram below shows the different categories of your audience.

audience presentation

  This chart is taken from our course on how to present data .

Internal Audience

An internal audience can either be the executives of the company or any employee who works in that company. For executives, the purpose of communicating a data-filled presentation is to give an update about a certain business activity such as a project or an initiative.

Another important purpose is to facilitate decision-making on managing the company’s operations, growing its core business, acquiring new markets and customers, investing in R&D, and other considerations. Knowing the relevant data and information beforehand will guide the decision-makers in making the right choices that will best position the company toward more success.

External Audience

An external audience can either be the company’s existing clients, where there are projects in progress, or new clients that the company wants to build a relationship with and win new business from. The other external audience is the general public, such as the company’s external shareholders and prospective investors of the company.

When it comes to winning new business, the analyst’s presentation will be more promotional and sales-oriented, whereas a project update will contain more specific information for the client, usually with lots of industry jargon.

Audiences for Live and Emailed Presentation

A live presentation contains more visuals and storytelling to connect more with the audience. It must be more precise and should get to the point faster and avoid long-winded speech or text because of limited time.

In contrast, an emailed presentation is expected to be read, so it will include more text. Just like a document or a book, it will include more detailed information, because its context will not be explained with a voice-over as in a live presentation.

When it comes to details, acronyms, and jargon in the presentation, these things depend on whether your audience are experts or not.

Every great presentation requires a clear “main idea”. It is the core purpose of the presentation and should be addressed clearly. Its significance should be highlighted and should cause the targeted audience to take some action on the matter.

An example of a serious and profound idea is given below.

the main idea

To communicate this big idea, we have to come up with appropriate and effective visual displays to show both the good and bad things surrounding the idea. It should put emphasis and attention on the most important part, which is the critical cash balance and capital investment situation for next year. This is an important component of data presentation.

The storyboarding below is how an analyst would build the presentation based on the big idea. Once the issue or the main idea has been introduced, it will be followed by a demonstration of the positive aspects of the company’s performance, as well as the negative aspects, which are more important and will likely require more attention.

Various ideas will then be suggested to solve the negative issues. However, before choosing the best option, a comparison of the different outcomes of the suggested ideas will be performed. Finally, a recommendation will be made that centers around the optimal choice to address the imminent problem highlighted in the big idea.


This storyboard is taken from our course on how to present data .

To get to the final point (recommendation), a great deal of analysis has been performed, which includes the charts and graphs discussed earlier, to make the whole presentation easy to follow, convincing, and compelling for your audience.

CFI offers the Business Intelligence & Data Analyst (BIDA)® certification program for those looking to take their careers to the next level. To keep learning and developing your knowledge base, please explore the additional relevant resources below:

  • Investment Banking Pitch Books
  • Excel Dashboards
  • Financial Modeling Guide
  • Startup Pitch Book
  • See all business intelligence resources
  • Share this article

Excel Fundamentals - Formulas for Finance

Create a free account to unlock this Template

Access and download collection of free Templates to help power your productivity and performance.

Already have an account? Log in

Supercharge your skills with Premium Templates

Take your learning and productivity to the next level with our Premium Templates.

Upgrading to a paid membership gives you access to our extensive collection of plug-and-play Templates designed to power your performance—as well as CFI's full course catalog and accredited Certification Programs.

Already have a Self-Study or Full-Immersion membership? Log in

Access Exclusive Templates

Gain unlimited access to more than 250 productivity Templates, CFI's full course catalog and accredited Certification Programs, hundreds of resources, expert reviews and support, the chance to work with real-world finance and research tools, and more.

Already have a Full-Immersion membership? Log in

Data presentation: A comprehensive guide

Learn how to create data presentation effectively and communicate your insights in a way that is clear, concise, and engaging.

Raja Bothra

Building presentations

team preparing data presentation

Hey there, fellow data enthusiast!

Welcome to our comprehensive guide on data presentation.

Whether you're an experienced presenter or just starting, this guide will help you present your data like a pro. We'll dive deep into what data presentation is, why it's crucial, and how to master it. So, let's embark on this data-driven journey together.

What is data presentation?

Data presentation is the art of transforming raw data into a visual format that's easy to understand and interpret. It's like turning numbers and statistics into a captivating story that your audience can quickly grasp. When done right, data presentation can be a game-changer, enabling you to convey complex information effectively.

Why are data presentations important?

Imagine drowning in a sea of numbers and figures. That's how your audience might feel without proper data presentation. Here's why it's essential:

  • Clarity : Data presentations make complex information clear and concise.
  • Engagement : Visuals, such as charts and graphs, grab your audience's attention.
  • Comprehension : Visual data is easier to understand than long, numerical reports.
  • Decision-making : Well-presented data aids informed decision-making.
  • Impact : It leaves a lasting impression on your audience.

Types of data presentation:

Now, let's delve into the diverse array of data presentation methods, each with its own unique strengths and applications. We have three primary types of data presentation, and within these categories, numerous specific visualization techniques can be employed to effectively convey your data.

1. Textual presentation

Textual presentation harnesses the power of words and sentences to elucidate and contextualize your data. This method is commonly used to provide a narrative framework for the data, offering explanations, insights, and the broader implications of your findings. It serves as a foundation for a deeper understanding of the data's significance.

2. Tabular presentation

Tabular presentation employs tables to arrange and structure your data systematically. These tables are invaluable for comparing various data groups or illustrating how data evolves over time. They present information in a neat and organized format, facilitating straightforward comparisons and reference points.

3. Graphical presentation

Graphical presentation harnesses the visual impact of charts and graphs to breathe life into your data. Charts and graphs are powerful tools for spotlighting trends, patterns, and relationships hidden within the data. Let's explore some common graphical presentation methods:

  • Bar charts: They are ideal for comparing different categories of data. In this method, each category is represented by a distinct bar, and the height of the bar corresponds to the value it represents. Bar charts provide a clear and intuitive way to discern differences between categories.
  • Pie charts: It excel at illustrating the relative proportions of different data categories. Each category is depicted as a slice of the pie, with the size of each slice corresponding to the percentage of the total value it represents. Pie charts are particularly effective for showcasing the distribution of data.
  • Line graphs: They are the go-to choice when showcasing how data evolves over time. Each point on the line represents a specific value at a particular time period. This method enables viewers to track trends and fluctuations effortlessly, making it perfect for visualizing data with temporal dimensions.
  • Scatter plots: They are the tool of choice when exploring the relationship between two variables. In this method, each point on the plot represents a pair of values for the two variables in question. Scatter plots help identify correlations, outliers, and patterns within data pairs.

The selection of the most suitable data presentation method hinges on the specific dataset and the presentation's objectives. For instance, when comparing sales figures of different products, a bar chart shines in its simplicity and clarity. On the other hand, if your aim is to display how a product's sales have changed over time, a line graph provides the ideal visual narrative.

Additionally, it's crucial to factor in your audience's level of familiarity with data presentations. For a technical audience, more intricate visualization methods may be appropriate. However, when presenting to a general audience, opting for straightforward and easily understandable visuals is often the wisest choice.

In the world of data presentation, choosing the right method is akin to selecting the perfect brush for a masterpiece. Each tool has its place, and understanding when and how to use them is key to crafting compelling and insightful presentations. So, consider your data carefully, align your purpose, and paint a vivid picture that resonates with your audience.

What to include in data presentation?

When creating your data presentation, remember these key components:

  • Data points : Clearly state the data points you're presenting.
  • Comparison : Highlight comparisons and trends in your data.
  • Graphical methods : Choose the right chart or graph for your data.
  • Infographics : Use visuals like infographics to make information more digestible.
  • Numerical values : Include numerical values to support your visuals.
  • Qualitative information : Explain the significance of the data.
  • Source citation : Always cite your data sources.

How to structure an effective data presentation?

Creating a well-structured data presentation is not just important; it's the backbone of a successful presentation. Here's a step-by-step guide to help you craft a compelling and organized presentation that captivates your audience:

1. Know your audience

Understanding your audience is paramount. Consider their needs, interests, and existing knowledge about your topic. Tailor your presentation to their level of understanding, ensuring that it resonates with them on a personal level. Relevance is the key.

2. Have a clear message

Every effective data presentation should convey a clear and concise message. Determine what you want your audience to learn or take away from your presentation, and make sure your message is the guiding light throughout your presentation. Ensure that all your data points align with and support this central message.

3. Tell a compelling story

Human beings are naturally wired to remember stories. Incorporate storytelling techniques into your presentation to make your data more relatable and memorable. Your data can be the backbone of a captivating narrative, whether it's about a trend, a problem, or a solution. Take your audience on a journey through your data.

4. Leverage visuals

Visuals are a powerful tool in data presentation. They make complex information accessible and engaging. Utilize charts, graphs, and images to illustrate your points and enhance the visual appeal of your presentation. Visuals should not just be an accessory; they should be an integral part of your storytelling.

5. Be clear and concise

Avoid jargon or technical language that your audience may not comprehend. Use plain language and explain your data points clearly. Remember, clarity is king. Each piece of information should be easy for your audience to digest.

6. Practice your delivery

Practice makes perfect. Rehearse your presentation multiple times before the actual delivery. This will help you deliver it smoothly and confidently, reducing the chances of stumbling over your words or losing track of your message.

A basic structure for an effective data presentation

Armed with a comprehensive comprehension of how to construct a compelling data presentation, you can now utilize this fundamental template for guidance:

In the introduction, initiate your presentation by introducing both yourself and the topic at hand. Clearly articulate your main message or the fundamental concept you intend to communicate.

Moving on to the body of your presentation, organize your data in a coherent and easily understandable sequence. Employ visuals generously to elucidate your points and weave a narrative that enhances the overall story. Ensure that the arrangement of your data aligns with and reinforces your central message.

As you approach the conclusion, succinctly recapitulate your key points and emphasize your core message once more. Conclude by leaving your audience with a distinct and memorable takeaway, ensuring that your presentation has a lasting impact.

Additional tips for enhancing your data presentation

To take your data presentation to the next level, consider these additional tips:

  • Consistent design : Maintain a uniform design throughout your presentation. This not only enhances visual appeal but also aids in seamless comprehension.
  • High-quality visuals : Ensure that your visuals are of high quality, easy to read, and directly relevant to your topic.
  • Concise text : Avoid overwhelming your slides with excessive text. Focus on the most critical points, using visuals to support and elaborate.
  • Anticipate questions : Think ahead about the questions your audience might pose. Be prepared with well-thought-out answers to foster productive discussions.

By following these guidelines, you can structure an effective data presentation that not only informs but also engages and inspires your audience. Remember, a well-structured presentation is the bridge that connects your data to your audience's understanding and appreciation.

Do’s and don'ts on a data presentation

  • Use visuals : Incorporate charts and graphs to enhance understanding.
  • Keep it simple : Avoid clutter and complexity.
  • Highlight key points : Emphasize crucial data.
  • Engage the audience : Encourage questions and discussions.
  • Practice : Rehearse your presentation.


  • Overload with data : Less is often more; don't overwhelm your audience.
  • Fit Unrelated data : Stay on topic; don't include irrelevant information.
  • Neglect the audience : Ensure your presentation suits your audience's level of expertise.
  • Read word-for-word : Avoid reading directly from slides.
  • Lose focus : Stick to your presentation's purpose.

Summarizing key takeaways

  • Definition : Data presentation is the art of visualizing complex data for better understanding.
  • Importance : Data presentations enhance clarity, engage the audience, aid decision-making, and leave a lasting impact.
  • Types : Textual, Tabular, and Graphical presentations offer various ways to present data.
  • Choosing methods : Select the right method based on data, audience, and purpose.
  • Components : Include data points, comparisons, visuals, infographics, numerical values, and source citations.
  • Structure : Know your audience, have a clear message, tell a compelling story, use visuals, be concise, and practice.
  • Do's and don'ts : Do use visuals, keep it simple, highlight key points, engage the audience, and practice. Don't overload with data, include unrelated information, neglect the audience's expertise, read word-for-word, or lose focus.

FAQ's on a data presentation

1. what is data presentation, and why is it important in 2024.

Data presentation is the process of visually representing data sets to convey information effectively to an audience. In an era where the amount of data generated is vast, visually presenting data using methods such as diagrams, graphs, and charts has become crucial. By simplifying complex data sets, presentation of the data may helps your audience quickly grasp much information without drowning in a sea of chart's, analytics, facts and figures.

2. What are some common methods of data presentation?

There are various methods of data presentation, including graphs and charts, histograms, and cumulative frequency polygons. Each method has its strengths and is often used depending on the type of data you're using and the message you want to convey. For instance, if you want to show data over time, try using a line graph. If you're presenting geographical data, consider to use a heat map.

3. How can I ensure that my data presentation is clear and readable?

To ensure that your data presentation is clear and readable, pay attention to the design and labeling of your charts. Don't forget to label the axes appropriately, as they are critical for understanding the values they represent. Don't fit all the information in one slide or in a single paragraph. Presentation software like Prezent and PowerPoint can help you simplify your vertical axis, charts and tables, making them much easier to understand.

4. What are some common mistakes presenters make when presenting data?

One common mistake is trying to fit too much data into a single chart, which can distort the information and confuse the audience. Another mistake is not considering the needs of the audience. Remember that your audience won't have the same level of familiarity with the data as you do, so it's essential to present the data effectively and respond to questions during a Q&A session.

5. How can I use data visualization to present important data effectively on platforms like LinkedIn?

When presenting data on platforms like LinkedIn, consider using eye-catching visuals like bar graphs or charts. Use concise captions and e.g., examples to highlight the single most important information in your data report. Visuals, such as graphs and tables, can help you stand out in the sea of textual content, making your data presentation more engaging and shareable among your LinkedIn connections.

Create your data presentation with prezent

Prezent can be a valuable tool for creating data presentations. Here's how Prezent can help you in this regard:

  • Time savings : Prezent saves up to 70% of presentation creation time, allowing you to focus on data analysis and insights.
  • On-brand consistency : Ensure 100% brand alignment with Prezent's brand-approved designs for professional-looking data presentations.
  • Effortless collaboration : Real-time sharing and collaboration features make it easy for teams to work together on data presentations.
  • Data storytelling : Choose from 50+ storylines to effectively communicate data insights and engage your audience.
  • Personalization : Create tailored data presentations that resonate with your audience's preferences, enhancing the impact of your data.

In summary, Prezent streamlines the process of creating data presentations by offering time-saving features, ensuring brand consistency, promoting collaboration, and providing tools for effective data storytelling. Whether you need to present data to clients, stakeholders, or within your organization, Prezent can significantly enhance your presentation-making process.

So, go ahead, present your data with confidence, and watch your audience be wowed by your expertise.

Thank you for joining us on this data-driven journey. Stay tuned for more insights, and remember, data presentation is your ticket to making numbers come alive! Sign up for our free trial or book a demo ! ‍

More zenpedia articles

presentation of data conclusion

Sales territory plan presentation: A comprehensive guide

presentation of data conclusion

How to stop stuttering when public speaking and deliver compelling presentations?

presentation of data conclusion

How to present SWOT analysis presentation: Tips & templates

Get the latest from Prezent community

Join thousands of subscribers who receive our best practices on communication, storytelling, presentation design, and more. New tips weekly. (No spam, we promise!)


Logo for British Columbia/Yukon Open Authoring Platform

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

74 Drawing Conclusions From Your Data

As we mentioned earlier, it is important to not just state the results of your statistical analyses. You should interpret the meanings, because this will enable you to answer your research questions. At the end of your analysis, you should be able to conclude whether your hypotheses are confirmed or rejected. To ensure you are able to draw conclusions from your analyses, we offer the following suggestions:

  • Highlight key findings from the data. ​
  • Making generalized comparisons​
  • Assess the right strength of the claim. Are hypotheses supported? To what extent? ​To what extent do generalizations hold?​
  • Examine the goodness of fit.

Your conclusions could be framed in statements such as:

“Most respondents …..​”

“Group A (e.g., Young adults) were more likely to ___than group B (older adults)

“Given the low degree of fit, other variables/factors might explain the relationship discovered”

Box 10.10 – Statistical Analysis Checklist

Access and Organize the Dataset

  • I have checked whether an Institutional Ethics Review is needed. If it is needed, I have obtained it.
  • I have recorded all the ways that I manipulated the data
  • I have inspected the data set and have noted the limitations (e.g., sampling, non-response, measurement, coverage) and have inspected it for reliability and validity.
  • I have inspected the data to ensure that it meets the requirements and assumptions of the statistical techniques that I wish to perform

Cleaning, Coding, and Recoding

  • I have re-coded variables as appropriate.
  • I have cleaned and processed the data set to make sure it is ready for analysis.

Research Design

  • If it is secondary data I am using, my methodology has documented their method for deriving the data.
  • My methodology documented the procedures for the quantitative data analysis.
  • I have highlighted my research questions and how my findings relate to them

Statistical Analysis

  • I have reported on the goodness of fit measures such as r2 and chi-square for the likelihood ratio test in order to show that your model fits the data well.
  • I have not interpreted coefficients for models that do not fit the data.
  • I have not merely provided statistical results, I have also interpreted the results.
  • You must test relationships. Univariate statistics are not enough for quantitative research.​ Make some inferences supported by tests of significance.​ Correlations, Chi-square, ANOVAs, Regressions (Linear and Logistics) etc. ​
  • I have stored all my statistical results in a central file which I can use to write up my results.

Statistical Presentation

  • My tables and figures conform to the referencing styles that I am using.
  • Report both statistically significant and non-statistically significant results.​ Do not be tempted to ignore the non-statistically significant results. They also tell a story.
  • I have avoided generalizations that my statistics cannot make.
  • I have discussed all of the relevant demographics

Practicing and Presenting Social Research Copyright © 2022 by Oral Robinson and Alexander Wilson is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

Share This Book


  • The Magazine
  • Newsletters
  • Managing Yourself
  • Managing Teams
  • Work-life Balance
  • The Big Idea
  • Data & Visuals
  • Reading Lists
  • Case Selections
  • HBR Learning
  • Topic Feeds
  • Account Settings
  • Email Preferences

Present Your Data Like a Pro

  • Joel Schwartzberg

presentation of data conclusion

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

presentation of data conclusion

  • JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

Partner Center

Presentation of Data

Class Registration Banner

Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely primary data and secondary data. If the information is collected by the investigator with a definite objective in their mind, then the data obtained is called the primary data. If the information is gathered from a source, which already had the information stored, then the data obtained is called secondary data. Once the data is collected, the presentation of data plays a major role in concluding the result. Here, we will discuss how to present the data with many solved examples.

What is Meant by Presentation of Data?

As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual method, tabular method and graphical method.

Presentation of Data Examples

Now, let us discuss how to present the data in a meaningful way with the help of examples.

Consider the marks given below, which are obtained by 10 students in Mathematics:

36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

Find the range for the given data.

Given Data: 36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

The data given is called the raw data.

First, arrange the data in the ascending order : 25, 36, 42, 55, 60, 62, 73, 75, 78, 95.

Therefore, the lowest mark is 25 and the highest mark is 95.

We know that the range of the data is the difference between the highest and the lowest value in the dataset.

Therefore, Range = 95-25 = 70.

Note: Presentation of data in ascending or descending order can be time-consuming if we have a larger number of observations in an experiment.

Now, let us discuss how to present the data if we have a comparatively more number of observations in an experiment.

Consider the marks obtained by 30 students in Mathematics subject (out of 100 marks)

10, 20, 36, 92, 95, 40, 50, 56, 60, 70, 92, 88, 80, 70, 72, 70, 36, 40, 36, 40, 92, 40, 50, 50, 56, 60, 70, 60, 60, 88.

In this example, the number of observations is larger compared to example 1. So, the presentation of data in ascending or descending order is a bit time-consuming. Hence, we can go for the method called ungrouped frequency distribution table or simply frequency distribution table . In this method, we can arrange the data in tabular form in terms of frequency.

For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data.

Therefore, the presentation of data is given as below:



























The following example shows the presentation of data for the larger number of observations in an experiment.

Consider the marks obtained by 100 students in a Mathematics subject (out of 100 marks)

95, 67, 28, 32, 65, 65, 69, 33, 98, 96,76, 42, 32, 38, 42, 40, 40, 69, 95, 92, 75, 83, 76, 83, 85, 62, 37, 65, 63, 42, 89, 65, 73, 81, 49, 52, 64, 76, 83, 92, 93, 68, 52, 79, 81, 83, 59, 82, 75, 82, 86, 90, 44, 62, 31, 36, 38, 42, 39, 83, 87, 56, 58, 23, 35, 76, 83, 85, 30, 68, 69, 83, 86, 43, 45, 39, 83, 75, 66, 83, 92, 75, 89, 66, 91, 27, 88, 89, 93, 42, 53, 69, 90, 55, 66, 49, 52, 83, 34, 36.

Now, we have 100 observations to present the data. In this case, we have more data when compared to example 1 and example 2. So, these data can be arranged in the tabular form called the grouped frequency table. Hence, we group the given data like 20-29, 30-39, 40-49, ….,90-99 (As our data is from 23 to 98). The grouping of data is called the “class interval” or “classes”, and the size of the class is called “class-size” or “class-width”.

In this case, the class size is 10. In each class, we have a lower-class limit and an upper-class limit. For example, if the class interval is 30-39, the lower-class limit is 30, and the upper-class limit is 39. Therefore, the least number in the class interval is called the lower-class limit and the greatest limit in the class interval is called upper-class limit.

Hence, the presentation of data in the grouped frequency table is given below:

20 – 29


30 – 39


40 – 49


50 – 59


60 – 69


70 – 79


80 – 89


90 – 99


Hence, the presentation of data in this form simplifies the data and it helps to enable the observer to understand the main feature of data at a glance.

Practice Problems

  • The heights of 50 students (in cms) are given below. Present the data using the grouped frequency table by taking the class intervals as 160 -165, 165 -170, and so on.  Data: 161, 150, 154, 165, 168, 161, 154, 162, 150, 151, 162, 164, 171, 165, 158, 154, 156, 172, 160, 170, 153, 159, 161, 170, 162, 165, 166, 168, 165, 164, 154, 152, 153, 156, 158, 162, 160, 161, 173, 166, 161, 159, 162, 167, 168, 159, 158, 153, 154, 159.
  • Three coins are tossed simultaneously and each time the number of heads occurring is noted and it is given below. Present the data using the frequency distribution table. Data: 0, 1, 2, 2, 1, 2, 3, 1, 3, 0, 1, 3, 1, 1, 2, 2, 0, 1, 2, 1, 3, 0, 0, 1, 1, 2, 3, 2, 2, 0.

To learn more Maths-related concepts, stay tuned with BYJU’S – The Learning App and download the app today!

MATHS Related Links

Leave a Comment Cancel reply

Your Mobile number and Email id will not be published. Required fields are marked *

Request OTP on Voice Call

Post My Comment

presentation of data conclusion

Register with BYJU'S & Download Free PDFs

Register with byju's & watch live videos.

MBA Notes

  • Graphical Presentation of Data

Table of Contents

Graphical presentation of data is an essential tool for researchers and decision-makers to convey complex information in a clear and concise manner. It involves using different types of charts, graphs, and diagrams to represent numerical data visually. In this blog, we will explore the different types of graphical representation and their applications in research.

Types of Graphs and Charts

  • Bar Graphs: Used to compare discrete values, such as sales figures for different products.
  • Line Graphs: Used to show trends over time, such as stock prices over a period.
  • Pie Charts: Used to represent parts of a whole, such as the percentage of revenue by product category.
  • Scatter Plots: Used to show the relationship between two variables, such as the correlation between temperature and ice cream sales.
  • Heat Maps: Used to show the density of data, such as the concentration of customer complaints by region.

Choosing the Right Graphical Representation

The choice of graphical representation depends on the nature of the data and the purpose of the analysis. Some factors to consider include:

  • Data type (discrete or continuous)
  • Data distribution (normal or skewed)
  • Number of variables
  • Audience preferences

Best Practices for Graphical Presentation of Data

  • Keep it simple and uncluttered
  • Use appropriate scales and axes labels
  • Use colors and patterns judiciously
  • Avoid 3D effects and unnecessary embellishments
  • Provide clear titles and captions
  • Use appropriate fonts and font sizes
  • Ensure readability for colorblind individuals

Graphical presentation of data is a powerful tool for visualizing complex information and communicating insights effectively. By selecting the appropriate chart or graph for the data and following best practices for presentation, researchers and decision-makers can make informed decisions and gain a deeper understanding of their data.

How useful was this post?

Click on a star to rate it!

Average rating 0 / 5. Vote count: 0

No votes so far! Be the first to rate this post.

We are sorry that this post was not useful for you! 😔

Let us improve this post!

Tell us how we can improve this post?

Research Methodology for Management Decisions

1 Research Methodology: An Overview

  • Meaning of Research
  • Research Methodology
  • Research Method
  • Business Research Method
  • Types of Research
  • Importance of business research
  • Role of research in important areas

2 Steps for Research Process

  • Research process
  • Define research problems
  • Research Problem as Hypothesis Testing
  • Extensive literature review in research
  • Development of working hypothesis
  • Preparing the research design
  • Collecting the data
  • Analysis of data
  • Preparation of the report or the thesis

3 Research Designs

  • Functions and Goals of Research Design
  • Characteristics of a Good Design
  • Different Types of Research Designs
  • Exploratory Research Design
  • Descriptive Research Design
  • Experimental Research Design
  • Types of Experimental Designs

4 Methods and Techniques of Data Collection

  • Primary and Secondary Data
  • Methods of Collecting Primary Data
  • Merits and Demerits of Different Methods of Collecting Primary Data
  • Designing a Questionnaire
  • Pretesting a Questionnaire
  • Editing of Primary Data
  • Technique of Interview
  • Collection of Secondary Data
  • Scrutiny of Secondary Data

5 Attitude Measurement and Scales

  • Attitudes, Attributes and Beliefs
  • Issues in Attitude Measurement
  • Scaling of Attitudes
  • Deterministic Attitude Measurement Models: The Guttman Scale
  • Thurstone’s Equal-Appearing Interval Scale
  • The Semantic Differential Scale
  • Summative Models: The Likert Scale
  • The Q-Sort Technique
  • Multidimensional Scaling
  • Selection of an Appropriate Attitude Measurement Scale
  • Limitations of Attitude Measurement Scales

6 Questionnaire Designing

  • Introductory decisions
  • Contents of the questionnaire
  • Format of the questionnaire
  • Steps involved in the questionnaire
  • Structure and Design of Questionnaire
  • Management of Fieldwork
  • Ambiguities in the Questionnaire Methods

7 Sampling and Sampling Design

  • Advantage of Sampling Over Census
  • Simple Random Sampling
  • Sampling Frame
  • Probabilistic As pects of Sampling
  • Stratified Random Sampling
  • Other Methods of Sampling
  • Sampling Design
  • Non-Probability Sampling Methods

8 Data Processing

  • Editing of Data
  • Coding of Data
  • Classification of Data
  • Statistical Series
  • Tables as Data Presentation Devices

9 Statistical Analysis and Interpretation of Data: Nonparametric Tests

  • One Sample Tests
  • Two Sample Tests
  • K Sample Tests

10 Multivariate Analysis of Data

  • Regression Analysis
  • Discriminant Analysis
  • Factor Analysis

11 Ethics in Research

  • Principles of research ethics
  • Advantages of research ethics
  • Limitations of the research ethics
  • Steps involved in ethics
  • What are research misconducts?

12 Substance of Reports

  • Research Proposal
  • Categories of Report
  • Reviewing the Draft

13 Formats of Reports

  • Parts of a Report
  • Cover and Title Page
  • Introductory Pages
  • Reference Section
  • Typing Instructions
  • Copy Reading
  • Proof Reading

14 Presentation of a Report

  • Communication Dimensions
  • Presentation Package
  • Audio-Visual Aids
  • Presenter’s Poise

Talk to our experts


  • Presentation of Data


Data Presenting for Clearer Reference

Imagine the statistical data without a definite presentation, will be burdensome! Data presentation is one of the important aspects of Statistics. Presenting the data helps the users to study and explain the statistics thoroughly. We are going to discuss this presentation of data and know-how information is laid down methodically. 

In this context, we are going to present the topic - Presentation of Data which is to be referred to by the students and the same is to be studied in regard to the types of presentations of data. 

Presentation of Data and Information

Statistics is all about data. Presenting data effectively and efficiently is an art. You may have uncovered many truths that are complex and need long explanations while writing. This is where the importance of the presentation of data comes in. You have to present your findings in such a way that the readers can go through them quickly and understand each and every point that you wanted to showcase. As time progressed and new and complex research started happening, people realized the importance of the presentation of data to make sense of the findings.

Define Data Presentation

Data presentation is defined as the process of using various graphical formats to visually represent the relationship between two or more data sets so that an informed decision can be made based on them.

Types of Data Presentation

Broadly speaking, there are three methods of data presentation:


Textual Ways of Presenting Data

Out of the different methods of data presentation, this is the simplest one. You just write your findings in a coherent manner and your job is done. The demerit of this method is that one has to read the whole text to get a clear picture. Yes, the introduction, summary, and conclusion can help condense the information.

Tabular Ways of Data Presentation and Analysis

To avoid the complexities involved in the textual way of data presentation, people use tables and charts to present data. In this method, data is presented in rows and columns - just like you see in a cricket match showing who made how many runs. Each row and column have an attribute (name, year, sex, age, and other things like these). It is against these attributes that data is written within a cell.

Diagrammatic Presentation: Graphical Presentation of Data in Statistics

This kind of data presentation and analysis method says a lot with dramatically short amounts of time.

Diagrammatic Presentation has been divided into further categories:

Geometric Diagram

When a Diagrammatic presentation involves shapes like a bar or circle, we call that a Geometric Diagram. Examples of Geometric Diagram

Bar Diagram

Simple Bar Diagram

Simple Bar Diagram is composed of rectangular bars. All of these bars have the same width and are placed at an equal distance from each other. The bars are placed on the X-axis. The height or length of the bars is used as the means of measurement. So, on the Y-axis, you have the measurement relevant to the data. 

Suppose, you want to present the run scored by each batsman in a game in the form of a bar chart. Mark the runs on the Y-axis - in ascending order from the bottom. So, the lowest scorer will be represented in the form of the smallest bar and the highest scorer in the form of the longest bar.

Multiple Bar Diagram

(Image will be uploaded soon)

In many states of India, electric bills have bar diagrams showing the consumption in the last 5 months. Along with these bars, they also have bars that show the consumption that happened in the same months of the previous year. This kind of Bar Diagram is called Multiple Bar Diagrams.

Component Bar Diagram

(image will be uploaded soon)

Sometimes, a bar is divided into two or more parts. For example, if there is a Bar Diagram, the bars of which show the percentage of male voters who voted and who didn’t and the female voters who voted and who didn’t. Instead of creating separate bars for who did and who did not, you can divide one bar into who did and who did not.

A pie chart is a chart where you divide a pie (a circle) into different parts based on the data. Each of the data is first transformed into a percentage and then that percentage figure is multiplied by 3.6 degrees. The result that you get is the angular degree of that corresponding data to be drawn in the pie chart. So, for example, you get 30 degrees as the result, on the pie chart you draw that angle from the center.

Frequency Diagram

Suppose you want to present data that shows how many students have 1 to 2 pens, how many have 3 to 5 pens, how many have 6 to 10 pens (grouped frequency) you do that with the help of a Frequency Diagram. A Frequency Diagram can be of many kinds:

Where the grouped frequency of pens (from the above example) is written on the X-axis and the numbers of students are marked on the Y-axis. The data is presented in the form of bars.

Frequency Polygon

When you join the midpoints of the upper side of the rectangles in a histogram, you get a Frequency Polygon

Frequency Curve

When you draw a freehand line that passes through the points of the Frequency Polygon, you get a Frequency Curve.


Suppose 2 students got 0-20 marks in maths, 5 students got 20-30 marks and 4 students got 30-50 marks in Maths. So how many students got less than 50 marks? Yes, 5+2=7. And how many students got more than 20 marks? 5+4=9. This type of more than and less than data are represented in the form of the ogive. The meeting point of the less than and more than line will give you the Median.

Arithmetic Line Graph

If you want to see the trend of Corona infection vs the number of recoveries from January 2020 to December 2020, you can do that in the form of an Arithmetic Line Graph. The months should be marked on the X-axis and the number of infections and recoveries are marked on the Y-axis. You can compare if the recovery is greater than the infection and if the recovery and infection are going at the same rate or not with the help of this Diagram.

Did You Know?

Sir Ronald Aylmer Fisher is known as the father of modern statistics.


FAQs on Presentation of Data

1. What are the 4 types of Tabular Presentation?

The tabular presentation method can be further divided into 4 categories:



Qualitative classification is done when the attributes in the table are some kind of ‘quality’ or feature. Suppose you want to make a table where you would show how many batsmen made half-centuries and how many batsmen made centuries in IPL 2020. Notice that the data would have only numbers - no age, sex, height is needed. This type of tabulation is called quantitative tabulation.

If you want to make a table that would inform which year’s world cup, which team won. The classifying variable, here, is year or time. This kind of classification is called Temporal classification.

If you want to list the top 5 coldest places in the world. The classifying variable here would be a place in each case. This kind of classification is called Spatial Classification.

2. Are bar charts and histograms the Same?

No, they are not the same. With a histogram, you measure the frequency of quantitative data. With bar charts, you compare categorical data.

3. What is the definition of Data Presentation?

When research work is completed, the data gathered from it can be quite large and complex. Organizing the data in a coherent, easy-to-understand, quick to read and graphical way is called data presentation.

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

  • Advanced Search
  • Journal List
  • An Bras Dermatol
  • v.89(2); Mar-Apr 2014

Presenting data in tables and charts *

Rodrigo pereira duquia.

1 Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA) - Porto Alegre (RS), Brazil.

João Luiz Bastos

2 Universidade Federal de Santa Catarina (UFSC) - Florianópolis (SC) Brazil.

Renan Rangel Bonamigo

David alejandro gonzález-chica, jeovany martínez-mesa.

3 Latin American Cooperative Oncology Group (LACOG) - Porto Alegre (RS) Brazil.

The present paper aims to provide basic guidelines to present epidemiological data using tables and graphs in Dermatology. Although simple, the preparation of tables and graphs should follow basic recommendations, which make it much easier to understand the data under analysis and to promote accurate communication in science. Additionally, this paper deals with other basic concepts in epidemiology, such as variable, observation, and data, which are useful both in the exchange of information between researchers and in the planning and conception of a research project.


Among the essential stages of epidemiological research, one of the most important is the identification of data with which the researcher is working, as well as a clear and synthetic description of these data using graphs and tables. The identification of the type of data has an impact on the different stages of the research process, encompassing the research planning and the production/publication of its results. For example, the use of a certain type of data impacts the amount of time it will take to collect the desired information (throughout the field work) and the selection of the most appropriate statistical tests for data analysis.

On the other hand, the preparation of tables and graphs is a crucial tool in the analysis and production/publication of results, given that it organizes the collected information in a clear and summarized fashion. The correct preparation of tables allows researchers to present information about tens or hundreds of individuals efficiently and with significant visual appeal, making the results more easily understandable and thus more attractive to the users of the produced information. Therefore, it is very important for the authors of scientific articles to master the preparation of tables and graphs, which requires previous knowledge of data characteristics and the ability of identifying which type of table or graph is the most appropriate for the situation of interest.


Before evaluating the different types of data that permeate an epidemiological study, it is worth discussing about some key concepts (herein named data, variables and observations):

Data - during field work, researchers collect information by means of questions, systematic observations, and imaging or laboratory tests. All this gathered information represents the data of the research. For example, it is possible to determine the color of an individual's skin according to Fitzpatrick classification or quantify the number of times a person uses sunscreen during summer. 1 , 2 All the information collected during research is generically named "data." A set of individual data makes it possible to perform statistical analysis. If the quality of data is good, i.e., if the way information was gathered was appropriate, the next stages of database preparation, which will set the ground for analysis and presentation of results, will be properly conducted.

Observations - are measurements carried out in one or more individuals, based on one or more variables. For instance, if one is working with the variable "sex" in a sample of 20 individuals and knows the exact amount of men and women in this sample (10 for each group), it can be said that this variable has 20 observations.

Variables - are constituted by data. For instance, an individual may be male or female. In this case, there are 10 observations for each sex, but "sex" is the variable that is referred to as a whole. Another example of variable is "age" in complete years, in which observations are the values 1 year, 2 years, 3 years, and so forth. In other words, variables are characteristics or attributes that can be measured, assuming different values, such as sex, skin type, eye color, age of the individuals under study, laboratory results, or the presence of a given lesion/disease. Variables are specifically divided into two large groups: (a) the group of categorical or qualitative variables, which is subdivided into dichotomous, nominal and ordinal variables; and (b) the group of numerical or quantitative variables, which is subdivided into continuous and discrete variables.

Categorical variables

  • Dichotomous variables, also known as binary variables: are those that have only two categories, i.e., only two response options. Typical examples of this type of variable are sex (male and female) and presence of skin cancer (yes or no).
  • Ordinal variables: are those that have three or more categories with an obvious ordering of the categories (whether in an ascending or descending order). For example, Fitzpatrick skin classification into types I, II, III, IV and V. 1
  • Nominal variables: are those that have three or more categories with no apparent ordering of the categories. Example: blood types A, B, AB, and O, or brown, blue or green eye colors.

Numerical variables

  • Discrete variables: are observations that can only take certain numerical values. An example of this type of variable is subjects' age, when assessed in complete years of life (1 year, 2 years, 3 years, 4 years, etc.) and the number of times a set of patients visited the dermatologist in a year.
  • Continuous variables: are those measured on a continuous scale, i.e., which have as many decimal places as the measuring instrument can record. For instance: blood pressure, birth weight, height, or even age, when measured on a continuous scale.

It is important to point out that, depending on the objectives of the study, data may be collected as discrete or continuous variables and be subsequently transformed into categorical variables to suit the purpose of the research and/or make interpretation easier. However, it is important to emphasize that variables measured on a numerical scale (whether discrete or continuous) are richer in information and should be preferred for statistical analyses. Figure 1 shows a diagram that makes it easier to understand, identify and classify the abovementioned variables.

An external file that holds a picture, illustration, etc.
Object name is abd-89-02-0280-g01.jpg

Types of variables


Firstly, it is worth emphasizing that every table or graph should be self-explanatory, i.e., should be understandable without the need to read the text that refers to it refers.

Presentation of categorical variables

In order to analyze the distribution of a variable, data should be organized according to the occurrence of different results in each category. As for categorical variables, frequency distributions may be presented in a table or a graph, including bar charts and pie or sector charts. The term frequency distribution has a specific meaning, referring to the the way observations of a given variable behave in terms of its absolute, relative or cumulative frequencies.

In order to synthesize information contained in a categorical variable using a table, it is important to count the number of observations in each category of the variable, thus obtaining its absolute frequencies. However, in addition to absolute frequencies, it is worth presenting its percentage values, also known as relative frequencies. For example, table 1 expresses, in absolute and relative terms, the frequency of acne scars in 18-year-old youngsters from a population-based study conducted in the city of Pelotas, Southern Brazil, in 2010. 3

Absolute and relative frequencies of acne scar in 18- year-old adolescents (n = 2.414). Pelotas, Brazil, 2010


The same information from table 1 may be presented as a bar or a pie chart, which can be prepared considering the absolute or relative frequency of the categories. Figures 2 and ​ and3 3 illustrate the same information shown in table 1 , but present it as a bar chart and a pie chart, respectively. It can be observed that, regardless of the form of presentation, the total number of observations must be mentioned, whether in the title or as part of the table or figure. Additionally, appropriate legends should always be included, allowing for the proper identification of each of the categories of the variable and including the type of information provided (absolute and/or relative frequency).

An external file that holds a picture, illustration, etc.
Object name is abd-89-02-0280-g02.jpg

Absolute frequencies of acne scar in 18-year-old adolescents (n = 2.414). Pelotas, Brazil, 2010

An external file that holds a picture, illustration, etc.
Object name is abd-89-02-0280-g03.jpg

Relative frequencies of acne scar in 18-year-old adolescents (n = 2.414). Pelotas, Brazil, 2010

Presentation of numerical variables

Frequency distributions of numerical variables can be displayed in a table, a histogram chart, or a frequency polygon chart. With regard to discrete variables, it is possible to present the number of observations according to the different values found in the study, as illustrated in table 2 . This type of table may provide a wide range of information on the collected data.

Educational level of 18-year-old adolescents (n = 2,199). Pelotas, Brazil, 2010

Educational level (in years of education)Absolute frequency (n)Relative frequency (%)Cumulative relative frequency (%)

Table 2 shows the distribution of educational levels among 18-year-old youngsters from Pelotas, Southern Brazil, with absolute, relative, and cumulative relative frequencies. In this case, absolute and relative frequencies correspond to the absolute number and the percentage of individuals according to their distribution for this variable, respectively, based on complete years of education. It should be noticed that there are 450 adolescents with 8 years of education, which corresponds to 20.5% of the subjects. Tables may also present the cumulative relative frequency of the variable. In this case, it was found that 50.6% of study subjects have up to 8 years of education. It is important to point that, although the same data were used, each form of presentation (absolute, relative or cumulative frequency) provides different information and may be used to understand frequency distribution from different perspectives.

When one wants to evaluate the frequency distribution of continuous variables using tables or graphs, it is necessary to transform the variable into categories, preferably creating categories with the same size (or the same amplitude). However, in addition to this general recommendation, other basic guidelines should be followed, such as: (1) subtracting the highest from the lowest value for the variable of interest; (2) dividing the result of this subtraction by the number of categories to be created (usually from three to ten); and (3) defining category intervals based on this last result.

For example, in order to categorize height (in meters) of a set of individuals, the first step is to identify the tallest and the shortest individual of the sample. Let us assume that the tallest individual is 1.85m tall and the shortest, 1.55m tall, with a difference of 0.3m between these values. The next step is to divide this difference by the number of categories to be created, e.g., five. Thus, 0.3m divided by five equals 0.06m, which means that categories will have exactly this range and will be numerically represented by the following range of values: 1st category - 1.55m to 1.60m; 2nd category - 1.61m to 1.66m; 3rd category - 1.67m to 1.72m; 4th category - 1.73m to 1.78m; 5th category - 1.79m to 1.85m.

Table 3 illustrates weight values at 18 years of age in kg (continuous numerical variable) obtained in a study with youngsters from Pelotas, Southern Brazil. 4 , 5 Figure 4 shows a histogram with the variable weight categorized into 20-kg intervals. Therefore, it is possible to observe that data from continuous numerical variables may be presented in tables or graphs.

Weight distribution among 18-year-old young male sex (n = 2.194). Pelotas, Brazil, 2010

 40.5 to 59.9 554 25.25
 60.0 to 65.8 543 24.75
 65.9 to 74.6 551 25.11
 74.7 to 147.8 546 24.89

An external file that holds a picture, illustration, etc.
Object name is abd-89-02-0280-g04.jpg

Weight distribution at 18 years of age among youngsters from the city of Pelotas. Pelotas (n = 2.194), Brazil, 2010

Assessing the relationship between two variables

The forms of data presentation that have been described up to this point illustrated the distribution of a given variable, whether categorical or numerical. In addition, it is possible to present the relationship between two variables of interest, either categorical or numerical.

The relationship between categorical variables may be investigated using a contingency table, which has the purpose of analyzing the association between two or more variables. The lines of this type of table usually display the exposure variable (independent variable), and the columns, the outcome variable (dependent variable). For example, in order to study the effect of sun exposure (exposure variable) on the development of skin cancer (outcome variable), it is possible to place the variable sun exposure on the lines and the variable skin cancer on the columns of a contingency table. Tables may be easier to understand by including total values in lines and columns. These values should agree with the sum of the lines and/or columns, as appropriate, whereas relative values should be in accordance with the exposure variable, i.e., the sum of the values mentioned in the lines should total 100%.

It is such a display of percentage values that will make it possible for risk or exposure groups to be compared with each other, in order to investigate whether individuals exposed to a given risk factor show higher frequency of the disease of interest. Thus, table 4 shows that 75.0%, 9.0%, and 0.3% of individuals in the study sample who had been working exposed to the sun for 20 years or more, for less than 20 years, and had never been working exposed to the sun, respectively, developed non-melanoma skin cancer. Another way of interpreting this table is observing that 25.0%, 91%,.0%, and 99.7% of individuals who had been working exposed to the sun for 20 years of more, for less than 20 years, and had never been working exposed to the sun did not develop non-melanoma skin cancer. This form of presentation is one of the most used in the literature and makes the table easier to read.

Sun exposure during work and non-melanoma skin cancer (hypothetical data).

Work exposed to the sun Non-melanoma skin cancer  Total
Yes No
N % N % N %
20 or more years3075.01025.040100
<20 years99.09091.099100

The relationship between two numerical variables or between one numerical variable and one categorical variable may be assessed using a scatter diagram, also known as dispersion diagram. In this diagram, each pair of values is represented by a symbol or a dot, whose horizontal and vertical positions are determined by the value of the first and second variables, respectively. By convention, vertical and horizontal axes should correspond to outcome and exposure variables, respectively. Figure 5 shows the relationship between weight and height among 18-year-old youngsters from Pelotas, Southern Brazil, in 2010. 3 , 4 The diagram presented in figure 5 should be interpreted as follows: the increase in subjects' height is accompanied by an increase in their weight.

An external file that holds a picture, illustration, etc.
Object name is abd-89-02-0280-g05.jpg

Point diagram for the relationship between weight (kg) and height (cm) among 18-year-old youngsters from the city of Pelotas (n = 2.194). Pelotas, Brazil, 2010.


Ideally, every table should:

  • Be self-explanatory;
  • Present values with the same number of decimal places in all its cells (standardization);
  • Include a title informing what is being described and where, as well as the number of observations (N) and when data were collected;
  • Have a structure formed by three horizontal lines, defining table heading and the end of the table at its lower border;
  • Not have vertical lines at its lateral borders;
  • Provide additional information in table footer, when needed;
  • Be inserted into a document only after being mentioned in the text; and
  • Be numbered by Arabic numerals.

Similarly to tables, graphs should:

  • Include, below the figure, a title providing all relevant information;
  • Be referred to as figures in the text;
  • Identify figure axes by the variables under analysis;
  • Quote the source which provided the data, if required;
  • Demonstrate the scale being used; and
  • Be self-explanatory.

The graph's vertical axis should always start with zero. A usual type of distortion is starting this axis with values higher than zero. Whenever it happens, differences between variables are overestimated, as can been seen in figure 6 .

An external file that holds a picture, illustration, etc.
Object name is abd-89-02-0280-g06.jpg

Figure showing how graphs in which the Y-axis does not start with zero tend to overestimate the differences under analysis. On the left there is a graph whose Y axis does not start with zero and on the right a graph reproducing the same data but with the Y axis starting with zero.

Understanding how to classify the different types of variables and how to present them in tables or graphs is an essential stage for epidemiological research in all areas of knowledge, including Dermatology. Mastering this topic collaborates to synthesize research results and prevents the misuse or overuse of tables and figures in scientific papers.

Conflict of Interest: None

Financial Support: None

How to cite this article: Duquia RP, Bastos JL, Bonamigo RR, González-Chica DA, Martínez-Mesa J. Presenting data in tables and charts. An Bras Dermatol. 2014;89(2):280-5.

* Work performed at the Dermatology service, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Departamento de Saúde Pública e Departamento de Nutrição da UFSC.

We use essential cookies to make Venngage work. By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts.

Manage Cookies

Cookies and similar technologies collect certain information about how you’re using our website. Some of them are essential, and without them you wouldn’t be able to use Venngage. But others are optional, and you get to choose whether we use them or not.

Strictly Necessary Cookies

These cookies are always on, as they’re essential for making Venngage work, and making it safe. Without these cookies, services you’ve asked for can’t be provided.

Show cookie providers

  • Google Login

Functionality Cookies

These cookies help us provide enhanced functionality and personalisation, and remember your settings. They may be set by us or by third party providers.

Performance Cookies

These cookies help us analyze how many people are using Venngage, where they come from and how they're using it. If you opt out of these cookies, we can’t get feedback to make Venngage better for you and all our users.

  • Google Analytics

Targeting Cookies

These cookies are set by our advertising partners to track your activity and show you relevant Venngage ads on other sites as you browse the internet.

  • Google Tag Manager
  • Infographics
  • Daily Infographics
  • Popular Templates
  • Accessibility
  • Graphic Design
  • Graphs and Charts
  • Data Visualization
  • Human Resources
  • Beginner Guides

Blog Marketing How To End A Presentation & Leave A Lasting Impression

How To End A Presentation & Leave A Lasting Impression

Written by: Krystle Wong Aug 09, 2023

How To End A Presentation

So you’ve got an exciting presentation ready to wow your audience and you’re left with the final brushstroke — how to end your presentation with a bang. 

Just as a captivating opening draws your audience in, creating a well-crafted presentation closing has the power to leave a profound and lasting impression that resonates long after the lights dim and the audience disperses.

In this article, I’ll walk you through the art of crafting an impactful conclusion that resonates with 10 effective techniques and ideas along with real-life examples to inspire your next presentation. Alternatively, you could always jump right into creating your slides by customizing our professionally designed presentation templates . They’re fully customizable and require no design experience at all! 

Click to jump ahead:

Why is it important to have an impactful ending for your presentation?

10 effective presentation closing techniques to leave a lasting impression, 7 things to put on a conclusion slide.

  • 5 real-life exceptional examples of how to end a presentation

6 mistakes to avoid in concluding a presentation

Faqs on how to end a presentation, how to create a memorable presentation with venngage.

presentation of data conclusion

People tend to remember the beginning and end of a presentation more vividly than the middle, making the final moments your last chance to make a lasting impression. 

An ending that leaves a lasting impact doesn’t merely mark the end of a presentation; it opens doors to further exploration. A strong conclusion is vital because it:

  • Leaves a lasting impression on the audience.
  • Reinforces key points and takeaways.
  • Motivates action and implementation of ideas.
  • Creates an emotional connection with the audience.
  • Fosters engagement, curiosity and reflection.

Just like the final scene of a movie, your presentation’s ending has the potential to linger in your audience’s minds long after they’ve left the room. From summarizing key points to engaging the audience in unexpected ways, make a lasting impression with these 10 ways to end a presentation:

1. The summary

Wrap up your entire presentation with a concise and impactful summary, recapping the key points and main takeaways. By doing so, you reinforce the essential aspects and ensure the audience leaves with a crystal-clear understanding of your core message.

presentation of data conclusion

2. The reverse story

Here’s a cool one: start with the end result and then surprise the audience with the journey that led you to where you are. Share the challenges you conquered and the lessons you learned, making it a memorable and unique conclusion that drives home your key takeaways.

Alternatively, customize one of our cool presentation templates to capture the attention of your audience and deliver your message in an engaging and memorable way

3. The metaphorical prop

For an added visual touch, bring a symbolic prop that represents your message. Explain its significance in relation to your content, leaving the audience with a tangible and unforgettable visual representation that reinforces your key concepts.

4. The audience engagement challenge

Get the audience involved by throwing them a challenge related to your informational presentation. Encourage active participation and promise to share the results later, fostering their involvement and motivating them to take action.

presentation of data conclusion

5. The memorable statistic showcase

Spice things up with a series of surprising or intriguing statistics, presented with attention-grabbing visual aids. Summarize your main points using these impactful stats to ensure the audience remembers and grasps the significance of your data, especially when delivering a business presentation or pitch deck presentation .

Transform your data-heavy presentations into engaging presentations using data visualization tools. Venngage’s chart and graph tools help you present information in a digestible and visually appealing manner. Infographics and diagrams can simplify complex concepts while images add a relatable dimension to your presentation. 

presentation of data conclusion

6. The interactive story creation

How about a collaborative story? Work with the audience to create an impromptu tale together. Let them contribute elements and build the story with you. Then, cleverly tie it back to your core message with a creative presentation conclusion.

7. The unexpected guest speaker

Introduce an unexpected guest who shares a unique perspective related to your presentation’s theme. If their story aligns with your message, it’ll surely amp up the audience’s interest and engagement.

8. The thought-provoking prompt

Leave your audience pondering with a thought-provoking question or prompt related to your topic. Encourage reflection and curiosity, sparking a desire to explore the subject further and dig deeper into your message.

9. The empowering call-to-action

Time to inspire action! Craft a powerful call to action that motivates the audience to make a difference. Provide practical steps and resources to support their involvement, empowering them to take part in something meaningful.

presentation of data conclusion

10. The heartfelt expression

End on a warm note by expressing genuine gratitude and appreciation for the audience’s time and attention. Acknowledge their presence and thank them sincerely, leaving a lasting impression of professionalism and warmth.

Not sure where to start? These 12 presentation software might come in handy for creating a good presentation that stands out. 

Remember, your closing slides for the presentation is your final opportunity to make a strong impact on your audience. However, the question remains — what exactly should be on the last slide of your presentation? Here are 7 conclusion slide examples to conclude with a high note:

1. Key takeaways

Highlight the main points or key takeaways from your presentation. This reinforces the essential information you want the audience to remember, ensuring they leave with a clear understanding of your message with a well summarized and simple presentation .

presentation of data conclusion

2. Closing statement

Craft a strong closing statement that summarizes the overall message of your presentation and leaves a positive final impression. This concluding remark should be impactful and memorable.

3. Call-to-action

Don’t forget to include a compelling call to action in your final message that motivates the audience to take specific steps after the presentation. Whether it’s signing up for a newsletter, trying a product or conducting further research, a clear call to action can encourage engagement.

presentation of data conclusion

4. Contact information

Provide your contact details, such as email address or social media handles. That way, the audience can easily reach out for further inquiries or discussions. Building connections with your audience enhances engagement and opens doors for future opportunities.

presentation of data conclusion

Use impactful visuals or graphics to deliver your presentation effectively and make the conclusion slide visually appealing. Engaging visuals can captivate the audience and help solidify your key points.

Visuals are powerful tools for retention. Use Venngage’s library of icons, images and charts to complement your text. You can easily upload and incorporate your own images or choose from Venngage’s library of stock photos to add depth and relevance to your visuals.

6. Next steps

Outline the recommended next steps for the audience to take after the presentation, guiding them on what actions to pursue. This can be a practical roadmap for implementing your ideas and recommendations.

presentation of data conclusion

7. Inspirational quote

To leave a lasting impression, consider including a powerful and relevant quote that resonates with the main message of your presentation. Thoughtful quotes can inspire and reinforce the significance of your key points.

presentation of data conclusion

Whether you’re giving an in-person or virtual presentation , a strong wrap-up can boost persuasiveness and ensure that your message resonates and motivates action effectively. Check out our gallery of professional presentation templates to get started.

5 real-life exceptional examples of how to end a presentation 

When we talk about crafting an exceptional closing for a presentation, I’m sure you’ll have a million questions — like how do you end a presentation, what do you say at the end of a presentation or even how to say thank you after a presentation. 

To get a better idea of how to end a presentation with style — let’s delve into five remarkable real-life examples that offer valuable insights into crafting a conclusion that truly seals the deal: 

1. Sheryl Sandberg 

In her TED Talk titled “Why We Have Too Few Women Leaders,” Sheryl Sandberg concluded with an impactful call to action, urging men and women to lean in and support gender equality in the workplace. This motivational ending inspired the audience to take action toward a more inclusive world.

2. Elon Musk

Elon Musk often concludes with his vision for the future and how his companies are working towards groundbreaking advancements. His passion and enthusiasm for pushing the boundaries of technology leave the audience inspired and eager to witness the future unfold.

3. Barack Obama

President Obama’s farewell address concluded with an emotional and heartfelt expression of gratitude to the American people. He thanked the audience for their support and encouraged them to stay engaged and uphold the values that define the nation.

4. Brené Brown 

In her TED Talk on vulnerability, Brené Brown ended with a powerful quote from Theodore Roosevelt: “It is not the critic who counts… The credit belongs to the man who is actually in the arena.” This quote reinforced her message about the importance of embracing vulnerability and taking risks in life.

5. Malala Yousafzai

In her Nobel Peace Prize acceptance speech, Malala Yousafzai ended with a moving call to action for education and girls’ rights. She inspired the audience to stand up against injustice and to work towards a world where every child has access to education.

For more innovative presentation ideas , turn ordinary slides into captivating experiences with these 15 interactive presentation ideas that will leave your audience begging for more.

So, we talked about how a good presentation usually ends. As you approach the conclusion of your presentation, let’s go through some of the common pitfalls you should avoid that will undermine the impact of your closing:

1. Abrupt endings

To deliver persuasive presentations, don’t leave your audience hanging with an abrupt conclusion. Instead, ensure a smooth transition by providing a clear closing statement or summarizing the key points to leave a lasting impression.

2. New information

You may be wondering — can I introduce new information or ideas in the closing? The answer is no. Resist the urge to introduce new data or facts in the conclusion and stick to reinforcing the main content presented earlier. By introducing new content at the end, you risk overshadowing your main message.

3. Ending with a Q&A session

While Q&A sessions are valuable , don’t conclude your presentation with them. Opt for a strong closing statement or call-to-action instead, leaving the audience with a clear takeaway.

4. Overloading your final slide

Avoid cluttering your final slide with too much information or excessive visuals. Keep it clean, concise and impactful to reinforce your key messages effectively.

5. Forgetting the call-to-action

Most presentations fail to include a compelling call-to-action which can diminish the overall impact of your presentation. To deliver a persuasive presentation, encourage your audience to take specific steps after the talk, driving engagement and follow-through.

6. Ignoring the audience

Make your conclusion audience-centric by connecting with their needs and interests. Avoid making it solely about yourself or your achievements. Instead, focus on how your message benefits the audience.

presentation of data conclusion

What should be the last slide of a presentation?

The last slide of a presentation should be a conclusion slide, summarizing key takeaways, delivering a strong closing statement and possibly including a call to action.

How do I begin a presentation?

Grabbing the audience’s attention at the very beginning with a compelling opening such as a relevant story, surprising statistic or thought-provoking question. You can even create a game presentation to boost interactivity with your audience. Check out this blog for more ideas on how to start a presentation . 

How can I ensure a smooth transition from the body of the presentation to the closing? 

To ensure a smooth transition, summarize key points from the body, use transition phrases like “In conclusion,” and revisit the main message introduced at the beginning. Bridge the content discussed to the themes of the closing and consider adjusting tone and pace to signal the transition.

How long should the conclusion of a presentation be?

The conclusion of a presentation should typically be around 5-10% of the total presentation time, keeping it concise and impactful.

Should you say thank you at the end of a presentation?

Yes, saying thank you at the end of a PowerPoint presentation is a courteous way to show appreciation for the audience’s time and attention.

Should I use presentation slides in the concluding part of my talk? 

Yes, using presentation slides in the concluding part of your talk can be effective. Use concise slides to summarize key takeaways, reinforce your main points and deliver a strong closing statement. A final presentation slide can enhance the impact of your conclusion and help the audience remember your message.

Should I include a Q&A session at the end of the presentation?

Avoid Q&A sessions in certain situations to ensure a well-structured and impactful conclusion. It helps prevent potential time constraints and disruptions to your carefully crafted ending, ensuring your core message remains the focus without the risk of unanswered or off-topic questions diluting the presentation’s impact.

Is it appropriate to use humor in the closing of a presentation?

Using humor in the closing of a presentation can be appropriate if it aligns with your content and audience as it can leave a positive and memorable impression. However, it’s essential to use humor carefully and avoid inappropriate or offensive jokes.

How do I manage nervousness during the closing of a presentation?

To manage nervousness during the closing, focus on your key points and the main message you want to convey. Take deep breaths to calm your nerves, maintain eye contact and remind yourself that you’re sharing valuable insights to enhance your presentation skills.

presentation of data conclusion

Creating a memorable presentation is a blend of engaging content and visually captivating design. With Venngage, you can transform your ideas into a dynamic and unforgettable presentation in just 5 easy steps: 

  • Choose a template from Venngage’s library: Pick a visually appealing template that fits your presentation’s theme and audience, making it easy to get started with a professional look.
  • Craft a compelling story or outline: Organize your content into a clear and coherent narrative or outline the key points to engage your audience and make the information easy to follow.
  • Customize design and visuals: Tailor the template with your brand colors, fonts and captivating visuals like images and icons, enhancing your presentation’s visual appeal and uniqueness. You can also use an eye-catching presentation background to elevate your visual content. 
  • Incorporate impactful quotes or inspiring elements: Include powerful quotes or elements that resonate with your message, evoking emotions and leaving a lasting impression on your audience members
  • Utilize data visualization for clarity: Present data and statistics effectively with Venngage’s charts, graphs and infographics, simplifying complex information for better comprehension.

Additionally, Venngage’s real-time collaboration tools allow you to seamlessly collaborate with team members to elevate your presentation creation process to a whole new level. Use comments and annotations to provide feedback on each other’s work and refine ideas as a group, ensuring a comprehensive and well-rounded presentation.

Well, there you have it—the secrets of how to conclude a presentation. From summarizing your key message to delivering a compelling call to action, you’re now armed with a toolkit of techniques that’ll leave your audience in awe.

Now go ahead, wrap it up like a pro and leave that lasting impression that sets you apart as a presenter who knows how to captivate, inspire and truly make a mark.

Discover popular designs

presentation of data conclusion

Infographic maker

presentation of data conclusion

Brochure maker

presentation of data conclusion

White paper online

presentation of data conclusion

Newsletter creator

presentation of data conclusion

Flyer maker

presentation of data conclusion

Timeline maker

presentation of data conclusion

Letterhead maker

presentation of data conclusion

Mind map maker

presentation of data conclusion

Ebook maker

  • Increase Font Size

46 Presentation of data II – Graphical representation

Pa . Raajeswari

Graphical representation is the visual display of data using plots and charts. It is used in many academic and professional disciplines but most widely so in the fields of mathematics, medicine and sciences. Graphical representation helps to quantify, sort and present data in a method that is understandable to a large variety of audiences. A graph is the representation of data by using graphical symbols such as lines, bars, pie slices, dots etc. A graph does represent a numerical data in the form of a qualitative structure and provides important information.

Statistical surveys and experiments provides valuable information about numerical scores. For better understanding and making conclusions and interpretations, the data should be managed and organized in a systematic form.

Graphs also enable in studying both time series and frequency distribution as they give clear account and precise picture of problem. Above all graphs are also easy to understand and eye catching and can create a storing impact on memory.

General Principles of Graphic Representation:

There are some algebraic principles which apply to all types of graphic representation of data. In a graph there are two lines called coordinate axes. One is vertical known as Y axis and the other is horizontal called X axis. These two lines are perpendicular to each other. Where these two lines intersect each other is called ‘0’ or the Origin. On the X axis the distances right to the origin have positive value and distances left to the origin have negative value. On the Y axis distances above the origin have a positive value and below the origin have a negative value.


The various types of graphical representations of the data are

  • Circle Graph
  • Histogram and Frequency Polygon

1. Dot Plots

The dot plot is one of the most simplest ways of graphical representation of the statistical data. As the name itself suggests, a dot plot uses the dots. It is a graphic display which usually compares frequency within different categories. The dot plot is composed of dots that are to be plotted on a graph paper.

In the dot plot, every dot denotes a specific number of observations belonging to a data set. One dot usually represents one observation. These dots are to be marked in the form of a column for each category. In this way, the height of each column shows the corresponding frequency of some category. The dot plots are quite useful when there are small amount of data is given within the small number of categories.

2. Bar Graph

A bar graph is a very frequently used graph in statistics as well as in media. A bar graph is a type of graph which contains rectangles or rectangular bars. The lengths of these bars should be proportional to the numerical values represented by them. In bar graph, the bars may be plotted either horizontally or vertically. But a vertical bar graph (also known as column bar graph) is used more than a horizontal one.

A vertical bar graph is shown below:

Number of students went to different countries for study:

The rectangular bars are separated by some distance in order to distinguish them from one another. The bar graph shows comparison among the given categories.

Mostly, horizontal axis of the graph represents specific categories and vertical axis shows the discrete numerical values.

3.Line Graph

A line graph is a kind of graph which represents data in a way that a series of points are to be connected by segments of straight lines. In a line graph, the data points are plotted on a graph and they are joined together with straight line.

A   sample   line   graph   is    illustrated    in    the   following   diagram:

The line graphs are used in the science, statistics and media. Line graphs are very easy to create. These are quite popular in comparison with other graphs since they visualize characteristics revealing data trends very clearly. A line graph gives a clear visual comparison between two variables which are represented on X-axis and Y-axis.

4.Circle Graph

A circle graph is also known as a pie graph or pie chart. It is called so since it is similar to slice of a “pie”. A pie graph is defined as a graph which contains a circle which is divided into sectors. These sectors illustrate the numerical proportion of the data.

A pie chart are shown in the following diagram:

The arc lengths of the sectors, in pie chart, are proportional to the numerical value they represent.Circle graphs are quite commonly seen in mass media as well as in business world.

5. Histogram and Frequency Polygon

The histograms and frequency polygons are very common graphs in statistics. A histogram is defined as a graphical representation of the mutually exclusive events. A histogram is quite similar to the bar graph. Both are made up of rectangular bars. The difference is that there is no gap between any two bars in the histogram. The histogram is used to represent the continuous data.

A histogram may look like the following graph:

The frequency polygon is a type of graphical representation which gives us better understanding of the shape of given distribution. Frequency polygons serve almost the similar purpose as histograms do. But the frequency polygon is quite helpful for the purpose of comparing two or more sets of data. The frequency polygons are said to be the extension of the histogram. When the midpoints of tops of the rectangular bars are joined together, the frequency polygon is made.

Few   examples    of    graphical    representation    of    statistical    data    are    given    below:

Example 1: Draw a dot plot for the following data.

Solution: The pie graph of the above data is:

Methods to Represent a Frequency Distribution:

Generally four methods are used to represent a frequency distribution graphically. These are Histogram, Smoothed frequency graph and Ogive or Cumulative frequency graph and pie diagram.

1. Histogram:

Histogram is a non-cumulative frequency graph, it is drawn on a natural scale in which the representative frequencies of the different class of values are represented through vertical rectangles drawn closed to each other. Measure of central tendency, mode can be easily determined with the help of this graph.

How to draw a Histogram:

Represent the class intervals of the variables along the X axis and their frequencies along the Y-axis on natural scale.

Start X axis with the lower limit of the lowest class interval. When the lower limit happens to be a distant score from the origin give a break in the X-axis n to indicate that the vertical axis has been moved in for convenience.

Now draw rectangular bars in parallel to Y axis above each of the class intervals with class units as base: The areas of rectangles must be proportional to the frequencies of the corresponding classes.

In this graph we shall take class intervals in the X axis and frequencies in the Y axis. Before plotting the graph we have to convert the class into their exact limits.

Advantages of histogram:

1.  It is easy to draw and simple to understand.

2.  It helps us to understand the distribution easily and quickly.

3.  It is more precise than the polygene.

Limitations of histogram:

1.  It is not possible to plot more than one distribution on same axes as histogram.

2.  Comparison of more than one frequency distribution on the same axes is not possible.

3.  It is not possible to make it smooth.

Uses of histogram:

1.Represents the data in graphic form.

2.Provides the knowledge of how the scores in the group are distributed. Whether the scores are piled up at the lower or higher end of the distribution or are evenly and regularly distributed throughout the scale.

3.Frequency Polygon. The frequency polygon is a frequency graph which is drawn by joining the coordinating points of the mid-values of the class intervals and their corresponding fre-quencies.

How to draw a frequency polygon:

Draw a horizontal line at the bottom of graph paper named ‘OX’ axis. Mark off the exact limits of the class intervals along this axis. It is better to start with i. of lowest value. When the lowest score in the distribution is a large number we cannot show it graphically if we start with the origin. Therefore put a break in the X axis to indicate that the vertical axis has been moved in for convenience. Two additional points may be added to the two extreme ends.

Draw a vertical line through the extreme end of the horizontal axis known as OY axis. Along this line mark off the units to represent the frequencies of the class intervals. The scale should be chosen in such a way that it will make the largest frequency (height) of the polygon approximately 75 percent of the width of the figure.

Plot the points at a height proportional to the frequencies directly above the point on the horizontal axis representing the mid-point of each class interval.

After plotting all the points on the graph join these points by a series of short straight lines to form the frequency polygon. In order to complete the figure two additional intervals at the high end and low end of the distribution should be included. The frequency of these two intervals will be zero.

Illustration: No. 7.3:

Draw a frequency polygon from the following data:

Advantages of frequency polygon:

2.  It is possible to plot two distributions at a time on same axes.

3.  Comparison of two distributions can be made through frequency polygon.

4.  It is possible to make it smooth.

Limitations of frequency polygon:

1.  It is less precise.

2.  It is not accurate in terms of area the frequency upon each interval.

Uses of frequency polygon:

1. When two or more distributions are to be compared the frequency polygon is used.

2. It represents the data in graphic form.

3. It provides knowledge of how the scores in one or more group are distributed. Whether the scores are piled up at the lower or higher end of the distribution or are evenly and regularly distributed throughout the scale.

2. Smoothed Frequency Polygon:

When the sample is very small and the frequency distribution is irregular the polygon is very jig-jag. In order to wipe out the irregularities and “also get a better notion of how the figure might  look if the data were more numerous, the frequency polygon may be smoothed.”

In this process to adjust the frequencies we take a series of ‘moving’ or ‘running’ averages. To get an adjusted or smoothed frequency we add the frequency of a class interval with the two adjacent intervals, just below and above the class interval. Then the sum is divided by 3. When these adjusted frequencies are plotted against the class intervals on a graph we get a smoothed frequency polygon.

Illustration 7.4:

Draw a smoothed frequency polygon, of the data given in the illustration No. 7.3:

Here we have to first convert the class intervals into their exact limits. Then we have to determine the adjusted or smoothed frequencies.

3. Ogive or Cumulative Frequency Polygon:

Ogive is a cumulative frequency graphs drawn on natural scale to determine the values of certain factors like median, Quartile, Percentile etc. In these graphs the exact limits of the class intervals  are shown along the X-axis and the cumulative frequencies are shown along the Y-axis. Below are given the steps to draw an ogive.

Get the cumulative frequency by adding the frequencies cumulatively, from the lower end (to get a less than ogive) or from the upper end (to get a more than ogive).

Mark off the class intervals in the X-axis.

Represent the cumulative frequencies along the Y-axis beginning with zero at the base.

Put dots at each of the coordinating points of the upper limit and the corresponding frequencies.

Join all the dots with a line drawing smoothly. This will result in curve called ogive.

Illustration No. 7.5:

Draw an ogive from the data given below:

To plot this graph first we have to convert, the class intervals into their exact limits. Then we have to calculate the cumulative frequencies of the distribution.

Uses of Ogive:

1.  Ogive is useful to determine the number of students below and above a particular score.

2.  When the median as a measure of central tendency is wanted.

3.  When the quartiles, deciles and percentiles are wanted.

4.  By plotting the scores of two groups on a same scale we can compare both the groups.

4. The Pie Diagram:

Figure given below shows the distribution of elementary pupils by their academic achievement in a school. Of the total, 60% are high achievers, 25% middle achievers and 15% low achievers. The construction of this pie diagram is quite simple. There are 360 degree in the circle. Hence, 60% of 360′ or 216° are counted off as shown in the diagram; this sector represents the proportion of high achievers students.

Ninety degrees counted off for the middle achiever students (25%) and 54 degrees for low achiever students (15%). The pie-diagram is useful when one wishes to picture proportions of the total in a striking way. Numbers of degrees may be measured off “by eye” or more accurately with a protractor.

Uses of Pie diagram:

1.  Pie diagram is useful when one wants to picture proportions of the total in a striking way.

2.  When a population is stratified and each strata is to be presented as a percentage at that time pie diagram is used.


The purpose of graphical presentation of data is to provide a quick and easy-to-read picture of information that clearly shows what otherwise takes a great deal of explanation. The impact of graphical data is typically more pointed and memorable than paragraphs of written information

For example, a person making a presentation regarding sales in various states across the country establishes the point of the presentation to the audience more quickly by using a color-coded map rather than merely stating the sales figures for each state. Observers quickly determine which states are ahead and which are behind in sales, and they know where emphasis needs to be placed. Alternatively, when making a presentation on sales by age groups using a pie chart that divides the pie into various ages, the audience quickly sees the results of sales by age. This  means that the audience is more likely to retain that information than if the presenter simply reads the results aloud or puts it into writing.


  • Simpler is Better
  • Graphs, Tables and charts can be used together
  • Use clear Description, title and labels
  • Provide a narrative Description of the highlights
  • Don’t compare variables with different scales of magnitude.
  • A Diagram must be attractive, well proportioned,neat and pleasing to the eyes.
  • They should be geometrically Accurate
  • Size of the diagram should be proportional to paper should not be too big or too small
  • Different colors should be used to classify data’s.


  • Acceptability: graphical report is acceptable to the busy persons because it easily highlights about the theme of the report. This helps to avoid wastage of time.
  • Comparative Analysis : Information can be compared in terms of graphical representation. Â Such comparative analysis helps for quick understanding and attention.
  • Less cost : Information if descriptive involves huge time to present properly. It involves more money to print the information but graphical presentation can be made in short but catchy view to make the report understandable. It obviously involves less cost.
  • Decision Making: Business executives can view the graphs at a glance and can make decision very quickly which is hardly possible through descriptive report.
  • Logical Ideas: If tables, design and graphs are used to represent information then a logical sequence is created to clear the idea of the audience.
  • Helpful for less literate Audience: Less literate or illiterate people can understand graphical representation easily because it does not involve going through line by line of any descriptive report.
  • Less Effort and Time: To present any table, design, image or graphs require less effort and time. Furthermore, such presentation makes quick understanding of the information.
  • Less Error and Mistakes: Qualitative or informative or descriptive reports involve errors or mistakes. As graphical representations are exhibited through numerical figures, tables or graphs, it usually involves less error and mistake.
  • A complete Idea: Such representation creates clear and complete idea in the mind of audience. Reading hundred pages may not give any scope to make decision. But an instant view or looking at a glance obviously makes an impression in  the mind of audience regarding the topic or subject.
  • Use in the Notice Board: Such representation can be hanged in the notice board to quickly raise the attention of employees in any organization.


Graphical representation of reports is not free from limitations. The following are the problems of graphical representation of data or reports:

  • Costly : Graphical representation pf reports are costly because it involves images, colors and paints. Combination of material with human efforts makes the graphical presentation expensive.
  • More time : Normal report involves less time to represent but graphical representation involves more time as it requires graphs and figures which are dependent to more time.
  • Errors and Mistakes : Since graphical representations are complex, there is- each and every chance of errors and mistake. This causes problems for better understanding to general people.
  • Lack of Secrecy: Graphical representation makes full presentation of information which may hamper the objective to keep something secret.
  • Problems to select the suitable method: Information can be presented through various graphical methods and ways. Which should be the suitable method is very hard to select.
  • Problem of Understanding: All may not be able to get the meaning of graphical representation because it involves various technical matters which are complex to general people.

Last of all it can be said that graphical representation does not provide proper information to general people.


Graphical representation makes the datamore possible to easily draw; visual impression of data. Graphical representation of data enhances the understandings of the observer. It makes comparisons easy. This kind of method creates an imprint on mind for a long period of time. Well in this chapter we have discussed about the definition ,types ,advantages and disadvantages in detail with relevant examples which will have an impact in the power of understanding. I request you all to go through the various types of graphs commonly used in research studies in with reference to home science research studies to explore new ideas in the field of research.

  • http://shodhganga.inflibnet.ac.in/bitstream/10603/143688/2/file%202%20chapter%201 %20data%20representation%20techniques.pdf
  • http://www.mas.ncl.ac.uk/~ndah6/teaching/MAS1403/notes_chapter2.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5453888/
  • http://cec.nic.in/wpresources/module/Anthropology/PaperIX/9/content/downloads/file1. pdf
  • https://www.kluniversity.in/arp/uploads/2096.pdf

SlideBazaar Blog Logo

Hook Your Audience Online: Storytelling Techniques for Slides

Ever felt the frustration of watching your audience’s eyes glaze over during an online presentation? It’s a common problem driven by disengagement and information overload. Endless data and dull slides can lose viewers’ interest in minutes. But there’s a solution: storytelling.

This article explores how integrating storytelling techniques can transform lifeless presentations into engaging, memorable experiences. We’ll cover crafting narratives, using visuals effectively, and enhancing delivery to captivate your audience from start to finish.

Benefits of Storytelling

Humans are naturally drawn to stories, which can hold an audience’s attention much better than plain facts and figures. When you use storytelling in your presentations, you take advantage of this natural tendency, making your message more engaging and easier to understand. A Stanford University study shows that stories are 22 times more memorable than facts alone.

Using storytelling in online presentations has several benefits.

First, it enhances engagement. Stories capture attention and keep audiences interested throughout the presentation. Second, storytelling improves retention. Information conveyed through stories is easier to remember. Third, stories connect emotionally with the audience, making the content more impactful.

By integrating storytelling techniques, you can transform dull events into compelling presentations captivating experiences that leave a lasting impression. This approach makes your content more relatable and ensures that your audience stays engaged and remembers your key points.

Building Your Narrative Framework

Crafting a compelling story arc, basic story structure.

To make your presentation engaging, follow a simple story structure: Introduction, Conflict, Climax, and Resolution. This structure helps create a natural flow that keeps your audience interested.

  • Introduction : Start by setting the stage for your topic. Give your audience a brief overview of what you’ll be discussing.
  • Conflict : Introduce the main problem or challenge that your presentation will address. This is where you highlight the issue that needs solving.
  • Climax : Present the peak of your story. This is the most critical part, where you reveal the key insights or data points.
  • Resolution : Offer solutions or conclusions to the problem you introduced. This ties everything together and provides a clear path forward.

Adapting Story Elements to Presentations

What are the dos and don’ts of effective presentations ? When planning your presentation, consider incorporating conflict, tension, and resolution into your slides. Here’s how you can do it:

  • Conflict : Start with a surprising fact or statistic to introduce a problem. This grabs attention and sets up the need for a solution.
  • Tension : Build up to your main points by showing the potential negative outcomes if the problem is not addressed. This keeps your audience engaged and invested in the solution.
  • Resolution : Conclude with clear, actionable steps or solutions. This provides closure and gives your audience a sense of direction.
  • Conflict : Highlight a critical issue in your industry using compelling statistics. For example, “Over 70% of businesses struggle with data management.”
  • Tension : Show the potential negative outcomes if the problem is not addressed. For instance, “Poor data management can lead to a 30% loss in revenue.”
  • Resolution : Provide a clear solution or call to action, such as “Implementing a robust data management system can improve efficiency and increase revenue by 20%.”

Identifying Your Hero

Importance of a relatable protagonist.

Every great story has a hero. In your presentation, your hero can be a person, a team, or even a concept your audience can relate to. Establishing a protagonist helps to humanize your data and makes your narrative more engaging.

Protagonist’s Journey

The protagonist’s journey can become the foundation of your presentation narrative. Highlight their challenges, goals, and the steps they take to overcome obstacles. This approach makes your presentation more relatable and compelling.

  • Highlight Challenges : Show the difficulties faced by your hero. For example, a company’s struggle with a particular issue, such as “Company X faced a major challenge with customer retention.”
  • Show Goals : Illustrate what the hero aims to achieve. For instance, “Company X set a goal to improve customer retention by 15% within a year.”
  • Journey : Detail the steps taken and the results obtained. Describe how the company implemented new strategies and the positive outcomes, like “By adopting new customer engagement strategies, Company X increased retention by 20%.”

Engaging Techniques for Online Slides

Captivating introductions.

A strong opening hooks your audience and sets the tone for the rest of your presentation. It’s your chance to make a memorable first impression. Here are a few powerful techniques to achieve impactful introductions:

  • Start with a shocking or unexpected statistic. For instance, “Did you know that 75% of people forget the content of online presentations within 48 hours?”
  • Share a short, relatable story that ties into your main message.
  • Ask a question that gets your audience thinking right from the start. Suppose you’re a speaker at a career orientation. You can ask your audience, “ What is the best degree to get ?” Follow up by saying, “Consider how it aligns with your interests and the job market trends.” This engages them and prompts them to think critically about their educational and career choices.

Use engaging visuals to complement your introduction. High-quality images, bold typography, and clean design can make your introduction more compelling.

Data Storytelling

Data storytelling involves turning data into a compelling narrative through visuals. This makes complex information more accessible and engaging. You can consider using these kinds of visuals in your presentations:

  • Charts and Graphs : Use to illustrate trends and patterns.
  • Infographics : Combine text and visuals to explain data.
  • Images and Icons : Use to highlight key points.

Here are some data usage examples:

  • Highlight Trends : Use line charts to show trends over time or flow charts to explain a process .
  • Emphasize Key Points : Use bold icons or images to draw attention to important data points.

The Power of Pause

Strategically timed pauses allow your audience to comprehend and reflect on your information. Pauses can emphasize important points and create dramatic effects.

Here are some powerful ways when to use visuals and animation:

  • Visual Breaks : Use visuals to create natural breaks in your presentation.
  • Animation : Apply subtle animations to guide your audience’s attention and create pauses.

When should you pause?

  • Heighten Emotional Impact : Pause after a powerful statement or image to let it sink in.
  • Emphasize Data Points : Pause after presenting a key statistic to give your audience time to absorb it.

Hook Your Audience Online: Storytelling Techniques for Slides

Enhancing the Delivery

Speak like a storyteller.

To keep your audience engaged, speak like a storyteller. Use vocal variety, pacing, and body language to make your presentation more dynamic and interesting. Changing your tone and pace helps match the emotion of your story, while body language can emphasize important points.

Here are some tips to improve your storytelling:

Relate Personal Experiences : Share your own experiences that relate to your topic. This makes your presentation more relatable and authentic. For example, if you’re talking about overcoming challenges, share a personal story about a challenge you faced and how you dealt with it.

Use Analogies : Simplify complex ideas by comparing them to something familiar. Analogies help your audience understand and remember your points better. For instance, if explaining a complicated process, compare it to a simple everyday activity.

Research shows that speakers who use expressive body language and vocal variety are seen as more credible and engaging.

Interactive Storytelling for Online Audiences

To make your online presentations more engaging, include interactive elements. Interactive features like polls, quizzes, and Q&A prompts keep your audience involved and reinforce your narrative.

Encouraging participation helps create a connection with your audience, making your presentation more dynamic and memorable. Here are some examples:

Polls and Quizzes : Use live polls and quizzes to gauge audience understanding and keep them involved. For example, ask a question about your topic and show the results in real time. This engages your audience and gives you immediate feedback on their comprehension.

Q&A Prompts : Integrate Q&A sessions to address audience questions and make the presentation more interactive. This allows your audience to clarify doubts and engage more deeply with the content.

Using these interactive presentation techniques can significantly enhance the effectiveness of your delivery, making your presentation informative, engaging, and memorable.

Using storytelling techniques in online presentations can significantly enhance engagement, retention, and emotional connection with your audience. By crafting a compelling narrative, identifying relatable protagonists, and using engaging techniques, you can transform your presentations into captivating experiences.

Experiment with storytelling in your next presentation. Start with a strong opening, build a narrative framework, and use engaging techniques to keep your audience hooked.

By integrating these storytelling techniques, you’ll capture your audience’s attention and leave a lasting impression. Start your storytelling journey today and watch your presentations come to life.

slidebazaar logo

At SlideBazaar, we help you create engaging and memorable presentations. Choose from our collection of professional templates or opt for our custom design services for a personalized touch. Your presentations deserve to be elevated to new heights, and we’re here to help you achieve just that!


  • PowerPoint Templates
  • Keynote Presentations
  • Infographic
  • Free slides


  • Frequently Asked Questions
  • Terms & Conditions
  • Privacy Policy
  • DMCA Policy


Get updates of our PowerPoint templates and slide designs before anyone else.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

  • Knowledge Base
  • Research paper

Writing a Research Paper Conclusion | Step-by-Step Guide

Published on October 30, 2022 by Jack Caulfield . Revised on April 13, 2023.

  • Restate the problem statement addressed in the paper
  • Summarize your overall arguments or findings
  • Suggest the key takeaways from your paper

Research paper conclusion

The content of the conclusion varies depending on whether your paper presents the results of original empirical research or constructs an argument through engagement with sources .

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes


Table of contents

Step 1: restate the problem, step 2: sum up the paper, step 3: discuss the implications, research paper conclusion examples, frequently asked questions about research paper conclusions.

The first task of your conclusion is to remind the reader of your research problem . You will have discussed this problem in depth throughout the body, but now the point is to zoom back out from the details to the bigger picture.

While you are restating a problem you’ve already introduced, you should avoid phrasing it identically to how it appeared in the introduction . Ideally, you’ll find a novel way to circle back to the problem from the more detailed ideas discussed in the body.

For example, an argumentative paper advocating new measures to reduce the environmental impact of agriculture might restate its problem as follows:

Meanwhile, an empirical paper studying the relationship of Instagram use with body image issues might present its problem like this:

“In conclusion …”

Avoid starting your conclusion with phrases like “In conclusion” or “To conclude,” as this can come across as too obvious and make your writing seem unsophisticated. The content and placement of your conclusion should make its function clear without the need for additional signposting.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

  • Academic style
  • Vague sentences
  • Style consistency

See an example

presentation of data conclusion

Having zoomed back in on the problem, it’s time to summarize how the body of the paper went about addressing it, and what conclusions this approach led to.

Depending on the nature of your research paper, this might mean restating your thesis and arguments, or summarizing your overall findings.

Argumentative paper: Restate your thesis and arguments

In an argumentative paper, you will have presented a thesis statement in your introduction, expressing the overall claim your paper argues for. In the conclusion, you should restate the thesis and show how it has been developed through the body of the paper.

Briefly summarize the key arguments made in the body, showing how each of them contributes to proving your thesis. You may also mention any counterarguments you addressed, emphasizing why your thesis holds up against them, particularly if your argument is a controversial one.

Don’t go into the details of your evidence or present new ideas; focus on outlining in broad strokes the argument you have made.

Empirical paper: Summarize your findings

In an empirical paper, this is the time to summarize your key findings. Don’t go into great detail here (you will have presented your in-depth results and discussion already), but do clearly express the answers to the research questions you investigated.

Describe your main findings, even if they weren’t necessarily the ones you expected or hoped for, and explain the overall conclusion they led you to.

Having summed up your key arguments or findings, the conclusion ends by considering the broader implications of your research. This means expressing the key takeaways, practical or theoretical, from your paper—often in the form of a call for action or suggestions for future research.

Argumentative paper: Strong closing statement

An argumentative paper generally ends with a strong closing statement. In the case of a practical argument, make a call for action: What actions do you think should be taken by the people or organizations concerned in response to your argument?

If your topic is more theoretical and unsuitable for a call for action, your closing statement should express the significance of your argument—for example, in proposing a new understanding of a topic or laying the groundwork for future research.

Empirical paper: Future research directions

In a more empirical paper, you can close by either making recommendations for practice (for example, in clinical or policy papers), or suggesting directions for future research.

Whatever the scope of your own research, there will always be room for further investigation of related topics, and you’ll often discover new questions and problems during the research process .

Finish your paper on a forward-looking note by suggesting how you or other researchers might build on this topic in the future and address any limitations of the current paper.

Full examples of research paper conclusions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

  • Argumentative paper
  • Empirical paper

While the role of cattle in climate change is by now common knowledge, countries like the Netherlands continually fail to confront this issue with the urgency it deserves. The evidence is clear: To create a truly futureproof agricultural sector, Dutch farmers must be incentivized to transition from livestock farming to sustainable vegetable farming. As well as dramatically lowering emissions, plant-based agriculture, if approached in the right way, can produce more food with less land, providing opportunities for nature regeneration areas that will themselves contribute to climate targets. Although this approach would have economic ramifications, from a long-term perspective, it would represent a significant step towards a more sustainable and resilient national economy. Transitioning to sustainable vegetable farming will make the Netherlands greener and healthier, setting an example for other European governments. Farmers, policymakers, and consumers must focus on the future, not just on their own short-term interests, and work to implement this transition now.

As social media becomes increasingly central to young people’s everyday lives, it is important to understand how different platforms affect their developing self-conception. By testing the effect of daily Instagram use among teenage girls, this study established that highly visual social media does indeed have a significant effect on body image concerns, with a strong correlation between the amount of time spent on the platform and participants’ self-reported dissatisfaction with their appearance. However, the strength of this effect was moderated by pre-test self-esteem ratings: Participants with higher self-esteem were less likely to experience an increase in body image concerns after using Instagram. This suggests that, while Instagram does impact body image, it is also important to consider the wider social and psychological context in which this usage occurs: Teenagers who are already predisposed to self-esteem issues may be at greater risk of experiencing negative effects. Future research into Instagram and other highly visual social media should focus on establishing a clearer picture of how self-esteem and related constructs influence young people’s experiences of these platforms. Furthermore, while this experiment measured Instagram usage in terms of time spent on the platform, observational studies are required to gain more insight into different patterns of usage—to investigate, for instance, whether active posting is associated with different effects than passive consumption of social media content.

If you’re unsure about the conclusion, it can be helpful to ask a friend or fellow student to read your conclusion and summarize the main takeaways.

  • Do they understand from your conclusion what your research was about?
  • Are they able to summarize the implications of your findings?
  • Can they answer your research question based on your conclusion?

You can also get an expert to proofread and feedback your paper with a paper editing service .

Scribbr Citation Checker New

The AI-powered Citation Checker helps you avoid common mistakes such as:

  • Missing commas and periods
  • Incorrect usage of “et al.”
  • Ampersands (&) in narrative citations
  • Missing reference entries

presentation of data conclusion

The conclusion of a research paper has several key elements you should make sure to include:

  • A restatement of the research problem
  • A summary of your key arguments and/or findings
  • A short discussion of the implications of your research

No, it’s not appropriate to present new arguments or evidence in the conclusion . While you might be tempted to save a striking argument for last, research papers follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the results and discussion sections if you are following a scientific structure). The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Caulfield, J. (2023, April 13). Writing a Research Paper Conclusion | Step-by-Step Guide. Scribbr. Retrieved July 1, 2024, from https://www.scribbr.com/research-paper/research-paper-conclusion/

Is this article helpful?

Jack Caulfield

Jack Caulfield

Other students also liked, writing a research paper introduction | step-by-step guide, how to create a structured research paper outline | example, checklist: writing a great research paper, what is your plagiarism score.

Educate 360

  • Resources / Blog

Python vs R: Which Language Excels in Data Analysis?

Python vs R: Which Language Excels in Data Analysis?


Data is the lifeblood of your organization, so the ability to analyze and interpret data effectively is crucial to your success. Across industries, organizations rely on data analysis to make informed decisions, optimize processes, and gain a competitive edge. Python and R are two of the most popular programming languages among the many tools and methods available for data analysis. We will compare Python and R in this blog to help you understand their core strengths, use cases, and how to choose the right tool for your specific needs.

Background of Python and R

History and development of python.

Python, created by Guido van Rossum and first released in 1991, is a high-level, general-purpose programming language. Known for its simplicity and readability, Python has become a favorite among developers and data scientists alike. Its design philosophy emphasizes code readability and syntax that allow programmers to express concepts in fewer lines of code. Python's extensive standard library and vibrant community have contributed to its widespread adoption and continuous growth.

History and Development of R

Statisticians Ross Ihaka and Robert Gentleman created R in the early 1990s. It was designed specifically for statistical computing and graphics, making it an ideal data analysis and visualization tool. R is an implementation of the S programming language and strongly focuses on providing a variety of statistical techniques, including linear and nonlinear modeling, time-series analysis, and clustering. Over the years, R has garnered an active community of statisticians and data scientists who have contributed numerous packages to extend its capabilities.

Popularity and Community Support

Python and R boast large communities providing extensive support and resources. Python's versatility has made it popular in various domains beyond data analysis, such as web development, automation, and machine learning. It has resulted in a diverse community with many tutorials, forums, and libraries. R's community, while more specialized, is highly focused on statistical analysis and data visualization. The Comprehensive R Archive Network (CRAN) hosts thousands of packages contributed by users, ensuring that R remains a powerful tool for data analysis.

Core Strengths of Python

Versatility and ease of learning.

One of Python's greatest strengths is its versatility. Its simple and intuitive syntax makes it an excellent choice for beginners and experienced programmers. The language's readability and straightforward learning curve allow users to quickly pick up and start coding, making it a popular choice for introductory programming courses.

Extensive Libraries

Python has extensive libraries that are invaluable for data analysis. Some of the most widely used libraries include:


Provides data structures and functions needed to manipulate structured data seamlessly.


Supports large, multi-dimensional arrays and matrices and contains a collection of mathematical functions to operate arrays.


Builds on NumPy and provides additional optimization, integration, and statistical analysis modules.

Integration with Other Languages and Tools

Python's ability to integrate with other languages and tools further enhances its appeal. It can be easily combined with languages like C, C++, and Java, as well as tools such as Hadoop and Spark, making it a powerful component in a data scientist's toolkit. Python's compatibility with various databases and its ability to handle web scraping, data cleaning, and automation tasks add to its versatility.

Use Cases in Web Development, Machine Learning, and Automation

Python's application extends beyond data analysis into web development, with frameworks like Django and Flask enabling the creation of robust web applications. Thanks to libraries like TensorFlow, Keras, and Scikit-learn, Python is the go-to language in machine learning. Additionally, Python's ability to automate repetitive tasks makes it a valuable tool for improving workflow efficiency and productivity.

Core Strengths of R

Designed specifically for statistical analysis and data visualization.

R's primary strength lies in its design for statistical analysis and data visualization. Its syntax and functions can perform complex statistical operations and produce high-quality plots with minimal effort, making R an excellent choice for statisticians and data scientists focused on data exploration and presentation.

Powerful Packages

R has extensive libraries that help better analyze data. Some of the most widely used libraries include:


Allows you to create visually appealing and customizable data visualizations


Simplifies data manipulation with intuitive syntax for data transformation operations


A collection of R packages and libraries providing a set of tools for tidying data

Strong Capabilities in Exploratory Data Analysis

R's strong capabilities in exploratory data analysis (EDA) make it a preferred tool for initial data exploration and hypothesis testing. Functions for summary statistics, data visualization, and data cleaning are readily available, allowing users to gain insights and identify patterns in their data quickly.

Use Cases in Academic Research and Data Science

Because of its many capabilities, R is used extensively in academic research and data science. Researchers across various disciplines rely on R to analyze experimental data, conduct statistical tests, and visualize results. Its extensive library ecosystem ensures that users have access to powerful techniques and methodologies for their analyses.

Comparing Python and R in Data Analysis

Data manipulation and cleaning.

Python and R excel in data manipulation and cleaning but approach it differently. Python's Pandas library offers a powerful and flexible framework for handling structured data, with functions for merging, reshaping, and aggregating datasets. R's dplyr package provides similar functionality with an intuitive syntax allowing users to chain multiple operations.

Data Visualization Capabilities

In data visualization, R's ggplot2 is often considered superior due to its flexibility and the quality of its plots. However, Python's Matplotlib and Seaborn libraries are also highly capable and widely used. While ggplot2's grammar of graphics approach allows for highly customized plots, Matplotlib and Seaborn offer more straightforward options for creating standard visualizations.

Machine Learning and Advanced Analytics

Thanks to its extensive libraries and frameworks, Python has a clear edge in machine learning and advanced analytics. TensorFlow, Keras, and Scikit-learn provide comprehensive tools for building and deploying machine learning models. While R also has packages for machine learning, such as caret and randomForest, Python's ecosystem is more mature and widely adopted in the industry.

Performance and Scalability

Performance and scalability are crucial considerations for large-scale data analysis. You can enhance Python's performance through libraries like NumPy, which leverage optimized C and Fortran code. Additionally, Python's ability to integrate with big data tools like Hadoop and Spark makes it suitable for handling large datasets. R, while efficient for smaller datasets, may require additional packages like data.table for improved performance with larger datasets.

Ease of Use and Learning Curve

Python's simple syntax and readability make it easier to learn and use, especially for those with a programming background. While a powerful tool for statistical analysis, R's syntax can be less intuitive for beginners. However, R's domain-specific design can benefit those primarily focused on statistical analysis and visualization.

Choosing the Right Tool for Your Needs

When choosing between Python and R, consider project requirements, existing skill sets, and team preferences. Python's versatility makes it a strong candidate if your project involves a mix of data analysis, web development, and machine learning. R's specialized capabilities may be more suitable if the focus is on statistical analysis and data visualization.

Scenarios Where Python is More Suitable

  • Projects that require integration with web applications or other programming languages
  • Machine learning and artificial intelligence applications
  • Automation of repetitive tasks
  • Development of scalable solutions for large datasets

A data engineer at a financial institution needs to clean and preprocess large datasets of transaction records for fraud detection analysis.

Python's pandas library offers robust data manipulation and cleaning tools, making it easy to handle large datasets. The DataFrame structure allows efficient operations like merging, reshaping, and aggregating data.

A data scientist at an e-commerce company is building a predictive model to forecast sales.

Python's scikit-learn library provides many machine learning algorithms and tools for model building, evaluation, and deployment with a consistent API and extensive documentation.

A startup is developing a web application that provides users real-time data analytics on their fitness activities.

Python's web frameworks, like Django and Flask, integrate easily with data analysis libraries such as pandas and matplotlib, enabling real-time data processing and visualization.

A media company wants to analyze customer feedback from social media and reviews to understand sentiment and trends.

Python's nltk and spaCy libraries offer powerful tools for natural language processing. They make it easy to analyze and extract insights from text, including tokenization, tagging, and sentiment analysis.

An operations team needs to automate the generation of daily reports from a database.

Python's simplicity and extensive library support make it ideal for scripting and automation, with libraries like SQLAlchemy for database interaction and pandas for data manipulation, streamlining the creation of automated workflows.

Scenarios Where R is More Suitable

  • Academic research with a strong emphasis on statistical analysis
  • Projects that require advanced data visualization
  • Exploratory data analysis with complex statistical techniques
  • Workflows that benefit from R's extensive package ecosystem for statistical computing

A public health researcher is analyzing a dataset on the effectiveness of different interventions in reducing the spread of a disease.

R is designed for statistical analysis, and packages like MASS, survival, and lme4 support advanced statistical modeling, including mixed-effects, survival analysis, and generalized linear models.

A data analyst at a marketing firm needs to create detailed visualizations to show customer segmentation based on purchasing behavior.

R's ggplot2 package excels in creating complex, multi-layered visualizations with its grammar of graphics approach, allowing for highly customizable and aesthetically pleasing plots.

A bioinformatician analyzes genomic data to identify genetic markers associated with a disease.

R's Bioconductor is designed explicitly for bioinformatics and genomic data analysis, providing tools for handling large-scale genomic data, sequence analysis, and visualization.

A sociologist is analyzing survey data to understand public opinion on social issues.

R offers packages like survey and srvyr tailored for analyzing complex survey data, handling survey weights, stratification, and clustering, ensuring statistically valid results.

In conclusion, Python and R are powerful data analysis tools with unique strengths. Python's versatility, extensive libraries, and integration capabilities make it an excellent choice for various applications, from web development to machine learning. R's design for statistical analysis and data visualization, along with its powerful packages, make it a preferred tool for data scientists and researchers focused on data exploration and presentation. When choosing between the two, consider your specific project requirements, existing skill set, and team preferences to select the language that best fits your needs. As the field of data analysis continues to evolve, both Python and R will remain indispensable tools for extracting valuable insights from data.

  • Expand/Collapse Microsoft Office 38 RSS
  • Expand/Collapse Training Trends 107 RSS
  • Expand/Collapse CyberSecurity 59 RSS
  • Expand/Collapse DevOps 2 RSS
  • Expand/Collapse Modern Workplace 43 RSS
  • Expand/Collapse Cloud 21 RSS
  • Expand/Collapse Programming 11 RSS
  • Expand/Collapse Artificial Intelligence (AI) 10 RSS
  • Expand/Collapse ITIL 17 RSS
  • Expand/Collapse Data & Analytics 29 RSS
  • Expand/Collapse Business Analyst 13 RSS
  • Expand/Collapse Training By Job Role 3 RSS
  • Expand/Collapse Leadership and Professional Development 14 RSS
  • Expand/Collapse Managed Learning Services 3 RSS
  • Expand/Collapse Design & Multi-Media 1 RSS

Institute of Data

  • New Zealand
  • United Kingdom

Crafting Compelling Presentations: Strategies for Effective Communication in Cybersecurity

US - Crafting Compelling Presentations Strategies for Effective Communication in Cybersecurity

Stay Informed With Our Weekly Newsletter

Receive crucial updates on the ever-evolving landscape of technology and innovation.

By clicking 'Sign Up', I acknowledge that my information will be used in accordance with the Institute of Data's Privacy Policy .

Effective communication in cybersecurity is a crucial skill that can make or break the success of your efforts.

The ability to convey complex concepts, risks, and mitigation strategies clearly and concisely is an invaluable asset in an ever-evolving landscape.

The importance of effective communication in cybersecurity

Tech team understanding the importance of effective communication in cyber security.

Effective communication plays a pivotal role in cybersecurity.

It serves as the foundation for collaboration, information sharing, and decision-making.

Without effective communication in cybersecurity, professionals risk misinterpreting information, overlooking critical details, and failing to address threats effectively.

Furthermore, cybersecurity is often perceived as a complex and technical discipline, making it essential for professionals to articulate their findings and recommendations in a manner that is easily understandable to various stakeholders.

Effective communication bridges the gap between technical expertise and organizational understanding, ensuring cybersecurity efforts align with business goals and objectives.

The role of effective communication in cybersecurity

Effective communication in cybersecurity serves multiple purposes.

Firstly, it enables cybersecurity professionals to convey various threats’ potential risks and consequences.

By presenting these risks comprehensively and relatably, stakeholders can better understand the importance of investing in cybersecurity measures and mitigating potential vulnerabilities.

Additionally, effective communication fosters collaboration and teamwork within organizations.

Cybersecurity professionals often work cross-functionally with individuals from different departments and backgrounds.

By effectively communicating their findings, recommendations, and requirements, they can facilitate productive discussions, encourage collaboration, and secure buy-in from key stakeholders.

The impact of poor communication on cybersecurity efforts

Poor communication in cybersecurity can have severe consequences.

Miscommunication or lack of clarity can lead to misunderstandings, delays, and even the failure to address critical security vulnerabilities.

For example, imagine a cybersecurity professional identifying a critical vulnerability in the organization’s network infrastructure but failing to communicate the urgency and potential impact effectively to the relevant stakeholders.

As a result, the necessary actions to remediate the vulnerability may not be taken promptly, exposing the organization to potential breaches and data loss.

Poor communication can also lead to a lack of trust and credibility.

Stakeholders may question the competence and expertise of cybersecurity professionals if their findings and recommendations are presented in a confusing or disjointed manner.

This can hinder future collaboration and undermine the effectiveness of cybersecurity initiatives.

Key elements of a compelling cybersecurity presentation

Effective communication in cybersecurity is compelling when it captivates and engages the audience while effectively conveying the intended message.

Structuring your presentation for maximum impact

The structure of your cybersecurity presentation plays a crucial role in capturing and maintaining the audience’s attention.

While each presentation may vary, a generally effective structure includes:

  • An attention-grabbing opening to hook the audience.
  • A clear outline of the presentation’s purpose.
  • A logical progression of information, transitioning between sections seamlessly.
  • An impactful closing summarising the key takeaways.

This structure ensures that your presentation flows smoothly, allowing the audience to easily follow the content and stay engaged throughout.

Using language effectively in cybersecurity presentations

Effective communication in cybersecurity involves using the most appropriate language and terminology.

Avoid excessive technical jargon that may alienate non-technical stakeholders, opting for clear and concise language that is easily understandable to a broader audience.

Consider using analogies and real-world examples to illustrate complex concepts and potential risks.

This helps to bridge the knowledge gap between cybersecurity professionals and other stakeholders, making the information more relatable and memorable.

Strategies for engaging your audience

Specialists developing strategies for engaging audience with effective communication in cyber security.

Engaging your audience is essential to ensure the success of your cybersecurity presentation.

By capturing and maintaining their attention, you increase the likelihood that they will understand and retain the information presented.

The power of storytelling in cybersecurity presentations

Effective communication in cybersecurity includes the ability to convey a narrative.

Storytelling is a powerful technique for effectively engaging your audience in a cybersecurity presentation.

By telling relatable stories that illustrate the potential impact of cyber threats, you create an emotional connection and capture the audience’s attention.

For example, recounting a real-life cyber attack and highlighting its consequences for a similar organization can help stakeholders understand the potential risks and the importance of implementing robust security measures .

Visual aids and their role in maintaining audience engagement

Incorporating visual aids, such as charts, graphs, and infographics, can significantly enhance audience engagement in a cybersecurity presentation.

Visuals help break up the monotony of text and facilitate the understanding and retention of complex information.

When using visual aids , ensure they are clear, well-designed, and directly support the information you present.

Avoid overwhelming the audience with too many visuals or using them solely for decorative purposes.

Overcoming common challenges in cybersecurity communication

Various challenges can hinder effective communication in cybersecurity.

Understanding and proactively addressing these challenges is critical to successfully conveying your message.

Addressing technical jargon: simplifying complex concepts

One of the main challenges in cybersecurity communication is the use of technical jargon that may be unfamiliar to non-technical stakeholders.

To overcome this, strive to simplify complex concepts by using plain, understandable language to a broader audience.

Consider providing definitions and explanations for technical terms when necessary to understand the topic.

Additionally, provide context and real-world examples to help stakeholders grasp the relevance and potential impact of the discussed concepts.

Dealing with audience skepticism and indifference

Skepticism and indifference can be significant barriers to effective cybersecurity communication.

Some stakeholders may need to be more aware of the risks associated with cyber threats or fail to see the value in investing resources in security measures.

To address this challenge, emphasize the potential consequences of cyber threats, highlighting real-world examples and case studies.

Provide concrete evidence and statistics demonstrating the prevalence and impact of cyber attacks.

By presenting compelling evidence, you can help overcome skepticism and indifference and gain support for your recommendations.

Measuring the effectiveness of your presentation

Tech professional measuring the effective communication in cyber security.

Measuring the effectiveness of your cybersecurity presentation allows you to assess its impact and identify areas for improvement.

Feedback mechanisms for continuous improvement

Solicit feedback from your audience to gain insights into their perception of your cybersecurity presentation.

Provide opportunities for anonymous feedback to encourage honest and constructive criticism.

Consider using surveys or feedback forms to collect quantitative and qualitative data.

Analyze the feedback received, identify patterns or recurring suggestions, and use this information to refine and improve future presentations.

Indicators of a successful cybersecurity presentation

Several indicators can suggest the success of your cybersecurity presentation, including:

  • Positive feedback and engagement from the audience during and after the presentation.
  • Active participation and follow-up inquiries from stakeholders.
  • Implementation of recommended security measures.
  • Reduction in security incidents or breaches.

Evaluating these indicators can provide valuable insights into the effectiveness of your presentation and its impact on your organization’s cybersecurity efforts.

In conclusion

Effective communication in cybersecurity is a vital skill that enables professionals to convey complex concepts, risks, and mitigation strategies clearly and concisely.

Cybersecurity professionals can effectively engage, educate, and influence stakeholders by understanding the importance of communication and employing strategies to craft compelling presentations that enhance their organizations’ resilience and security.

Ready for a career in cybersecurity?

Whether you are new to tech or a seasoned professional looking for a change, the Institute of Data’s Cybersecurity Program equips you with the skills you’ll need to thrive in this ever-evolving field of tech. To learn more about what goes into our programs, download a Cybersecurity Course Outline. 

Want to learn more about our programs?

Our local team is ready to give you a free career consultation . Contact us today!

presentation of data conclusion

Follow us on social media to stay up to date with the latest tech news

Stay connected with Institute of Data

Delving into the attacker’s mind: exploring the psychology of cyber attacks.

Exploring the Psychology of Cyber Attacks: The Attacker’s Mind

US - Maximizing Your Education_ How to Transition Into a New Career Field

Maximizing Your Education: How to Transition Into a New Career Field

US - Crafting Compelling Presentations Strategies for Effective Communication in Cybersecurity

Maximizing Your Education: How to Transition Into a Cybersecurity Career

Delving into the attacker’s mind: exploring the psychology of cyber attacks.

© Institute of Data. All rights reserved.

presentation of data conclusion

Copy Link to Clipboard

COLLEGE FOOTBALL 25 Rankings Week Showcase

Who are the best teams in ea sports™ college football 25.

Hey College Football Fans,

Welcome back to the Campus Huddle! This week, we have a special “living” edition of the Campus Huddle, centered around Rankings Week.

So what is Rankings Week? 

It’s a time to celebrate various EA SPORTS™ College Football 25 rankings, from the Toughest Places to Play, to the Top Offenses and Defenses, to our final Team Power Rankings before the worldwide launch on July 19. Plus, we’ll have our Sights and Sounds Deep Dive coming Wednesday, showcasing the incredible and unique presentation features coming to EA SPORTS™ College Football 25.

The full Rankings Week schedule can be seen here:


We laid out the significant impact that Homefield Advantage can have on the outcome of games in EA SPORTS™ College Football 25 during our Gameplay Deep Dive Campus Huddle . Audio and in-game modifiers such as blurred routes, incorrect play art, confidence and composure affects, and screen shaking are some of the immersive impacts away teams and players will be forced to contend with. 

But not all Homefield Advantages are created equal. The Development Team worked to compile a list of the Top 25 Toughest Places to Play, factoring in historical stats such as home winning %, home game attendance, active home winning streaks, team prestige, and more.

Rankings are subject to change in future updates.

  • Kyle Field - Texas A&M
  • Bryant-Denny Stadium - Alabama
  • Tiger Stadium - LSU
  • Ohio Stadium - Ohio State
  • Sanford Stadium - Georgia
  • Beaver Stadium - Penn State
  • Camp Randall Stadium - Wisconsin
  • Gaylord Family Oklahoma Memorial Stadium - Oklahoma
  • Doak S. Campbell Stadium - Florida State
  • Ben Hill Griffin Stadium - Florida
  • Autzen Stadium - Oregon
  • Memorial Stadium - Clemson
  • Neyland Stadium - Tennessee
  • Jordan-Hare Stadium - Auburn
  • Williams-Brice Stadium - South Carolina
  • Michigan Stadium - Michigan
  • Lane Stadium - Virginia Tech
  • Rice-Eccles Stadium - Utah
  • Darrell K. Royal - Texas Memorial Stadium - Texas
  • Kinnick Stadium - Iowa
  • Notre Dame Stadium - Notre Dame
  • Spartan Stadium - Michigan State
  • Donald W. Reynolds Razorback Stadium - Arkansas
  • Albertsons Stadium - Boise State
  • Davis Wade Stadium - Mississippi State


In case you missed it, Kirk Herbstreit is back with our next Deep Dive, taking a look at the sights and sounds featured in EA SPORTS™ College Football 25. The Development Team spent years capturing countless traditions, mascots, fight songs, and more to the game, ensuring all 134 schools and fan bases were represented with pride. These elements make College Football special and unique, bringing the unmatched feeling of game day to your fingertips.  

For even more on the presentation elements and how they come to life, check out the latest Campus Huddle hosted by Senior Game Designer Christian Brandt.


The Development Team meticulously examined hundreds of thousands of data points to arrive at our team power rankings. With help from our friends at Pro Football Focus (PFF), the team analyzed all 134 rosters, thousands of players, years worth of game film, and mountains of stats, ultimately arriving at our Team Power Rankings.

Here are the Top 25 offenses in EA SPORTS™ College Football 25: 

  • Georgia - 94 OVR
  • Oregon - 94 OVR
  • Alabama - 91 OVR
  • Texas - 91 OVR
  • Ohio State - 89 OVR
  • LSU - 89 OVR
  • Miami - 89 OVR
  • Colorado - 89 OVR
  • Missouri - 89 OVR
  • Clemson - 87 OVR
  • Utah - 87 OVR
  • Penn State - 87 OVR
  • Ole Miss - 87 OVR
  • Kansas - 87 OVR
  • Arizona - 87 OVR
  • NC State - 87 OVR
  • Notre Dame - 85 OVR
  • Texas A&M - 85 OVR
  • Memphis - 85 OVR
  • SMU - 85 OVR
  • UCF - 85 OVR
  • Florida State - 83 OVR
  • Oklahoma - 83 OVR
  • Virginia Tech - 83 OVR
  • USC - 83 OVR

As the old saying goes, “Defense wins championships.” Here are the Top 25 defenses in EA SPORTS™ College Football 25:

  • Ohio State - 96 OVR
  • Oregon - 90 OVR
  • Alabama - 90 OVR
  • Clemson - 90 OVR
  • Notre Dame - 90 OVR
  • Michigan - 90 OVR
  • Texas - 88 OVR
  • Penn State - 88 OVR
  • Utah - 88 OVR
  • Florida State - 88 OVR
  • Oklahoma - 88 OVR
  • Iowa - 88 OVR
  • Virginia Tech - 86 OVR
  • Wisconsin - 86 OVR
  • USC - 86 OVR
  • Auburn - 86 OVR
  • LSU - 84 OVR
  • Texas A&M - 84 OVR
  • Colorado - 84 OVR
  • Oklahoma State - 84 OVR
  • Louisville - 84 OVR
  • North Carolina - 84 OVR
  • Kansas State - 84 OVR
  • Florida - 84 OVR


And the moment you’ve all been waiting for! Here are the Top Teams in EA SPORTS™ College Football 25.

  • Georgia - 95 OVR
  • Ohio State - 93 OVR
  • Oregon - 93 OVR
  • Alabama - 92 OVR
  • Texas - 92 OVR
  • LSU - 90 OVR
  • Michigan - 88 OVR
  • Miami - 88 OVR
  • Texas A&M - 88 OVR
  • Ole Miss - 88 OVR
  • Colorado - 87 OVR
  • Oklahoma - 87 OVR
  • Wisconsin - 87 OVR
  • USC - 87 OVR
  • Virginia Tech - 87 OVR
  • Oklahoma State - 87 OVR
  • Iowa - 87 OVR

Let us know what you think! Join the conversation today by following EA SPORTS™ College Football 25 on social media and rep your school. Next week, we’ll have even more information to share including our Dynasty Deep Dive where we explore the ins and outs of the mode, recruiting, and more! 

College Football 25 launches worldwide on July 19th, 2024. Pre-order the Deluxe Edition* or the EA SPORTS™ MVP Bundle** and play 3 days early. Conditions and restrictions apply. See disclaimers for details. Stay in the conversation by following us on Facebook , Twitter , Instagram , YouTube , and Answers HQ .

Pre-order the MVP Bundle*** to make game day every day, and get both Madden NFL 25 and College Football 25 with exclusive content.


Sign-up for our newsletter to be the first to know about new updates.


College football 25 sights and sounds deep dive, college football 25 gameplay deep dive, welcome to college football 25.

Funds heavily sold CBOT corn ahead of bearish US acreage data

  • Medium Text

The setting sun lights a corn field waiting to be harvested near Defiance, Iowa

Sign up here.

Our Standards: The Thomson Reuters Trust Principles. New Tab , opens new tab

presentation of data conclusion

Thomson Reuters

As a columnist for Reuters, Karen focuses on all aspects of the global agriculture markets with a primary focus in grains and oilseeds. Karen comes from a strong science background and has a passion for data, statistics, and charts, and she uses them to add context to whatever hot topic is driving the markets. Karen holds degrees in meteorology and sometimes features that expertise in her columns. Follow her on Twitter @kannbwx for her market insights.

Wall Street ends slightly lower, capping blockbuster year

Markets Chevron

Cuban ministers reveal details of food, fuel shortages amid economic crisis

Cuba announces new measures for "war-time economy" amid growing crisis

Cuba`s government said late on Sunday it would double down on price controls and continue to fight tax evasion in an increasingly desperate bid to tamp down on a ballooning fiscal deficit and spiraling inflation that have devastated its economy.

South African Rand coins are seen in this illustration picture


  1. PPT

    presentation of data conclusion

  2. Conclusion Ppt Samples

    presentation of data conclusion

  3. Eye-catching Conclusion Powerpoint Slide Templates F40

    presentation of data conclusion

  4. Conclusion Presentation of Data

    presentation of data conclusion

  5. Conclusion Slide Powerpoint Examples

    presentation of data conclusion

  6. Conclusion Slides PowerPoint Template

    presentation of data conclusion


  1. Presentation of Data |Chapter 2 |Statistics

  2. Hypothesis spaces, Inductive bias, Generalization, Bias variance trade-off in tamil -AL3451 #ML

  3. Linear Regression Models: Least squares, single & multiple variables in unit2 tamil -AL3451 #ML

  4. Bayesian linear regression, gradient descent linear regression in tamil (unit2)-AL3451 #ML

  5. Data Problems in the Humanities, or "When everybody is special, no one is"?

  6. Presentation of data ch 2 lec 1


  1. Understanding Data Presentations (Guide + Examples)

    A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. ... Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow ...

  2. 30 Examples: How to Conclude a Presentation (Effective Closing Techniques)

    30 Example Phrases: How to Conclude a Presentation. 1. "In summary, let's revisit the key takeaways from today's presentation.". 2. "Thank you for your attention. Let's move forward together.". 3. "That brings us to the end. I'm open to any questions you may have.".

  3. Data Presentation

    Furthermore, by combining data, visuals, and text, your audience will get a clear understanding of the current situation, past events, and possible conclusions and recommendations that can be made for the future. Audiences and Data Presentation. The simple diagram below shows the different categories of your audience.

  4. Data Presentation: A Comprehensive Guide

    As you approach the conclusion, succinctly recapitulate your key points and emphasize your core message once more. Conclude by leaving your audience with a distinct and memorable takeaway, ensuring that your presentation has a lasting impact. ... Data presentation is the process of visually representing data sets to convey information ...

  5. Drawing Conclusions From Your Data

    At the end of your analysis, you should be able to conclude whether your hypotheses are confirmed or rejected. To ensure you are able to draw conclusions from your analyses, we offer the following suggestions: Highlight key findings from the data. Making generalized comparisons . Assess the right strength of the claim.

  6. Present Your Data Like a Pro

    TheJoelTruth. While a good presentation has data, data alone doesn't guarantee a good presentation. It's all about how that data is presented. The quickest way to confuse your audience is by ...

  7. Presentation of Data (Methods and Examples)

    Presentation of data is an important process in statistics, which helps to easily understand the main features of data at a glance. ... Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely ...

  8. How To Create A Successful Data Presentation

    Storytelling with data is a highly valued skill in the workforce today and translating data and insights for a non-technical audience is rare to see than it is expected. Here's my five-step routine to make and deliver your data presentation right where it is intended —. 1. Understand Your Data & Make It Seen.

  9. How To Present Research Data?

    Data, which often are numbers and figures, are better presented in tables and graphics, while the interpretation are better stated in text. By doing so, we do not need to repeat the values of HbA 1c in the text (which will be illustrated in tables or graphics), and we can interpret the data for the readers. However, if there are too few variables, the data can be easily described in a simple ...

  10. 1.3: Presentation of Data

    Data sets can be presented either by listing all the elements or by giving a table of values and frequencies. This page titled 1.3: Presentation of Data is shared under a CC BY-NC-SA 3.0 license and was authored, remixed, and/or curated by Anonymous via source content that was edited to the style and standards of the LibreTexts platform. In ...

  11. What Is Data Presentation? (Definition, Types And How-To)

    Data presentation is a process of comparing two or more data sets with visual aids, such as graphs. Using a graph, you can represent how the information relates to other data. This process follows data analysis and helps organise information by visualising and putting it into a more readable format. ... At the end of your presentation ...

  12. Statistical data presentation

    In this article, the techniques of data and information presentation in textual, tabular, and graphical forms are introduced. Text is the principal method for explaining findings, outlining trends, and providing contextual information. A table is best suited for representing individual information and represents both quantitative and ...

  13. Graphical Presentation of Data

    Conclusion. Graphical presentation of data is a powerful tool for visualizing complex information and communicating insights effectively. By selecting the appropriate chart or graph for the data and following best practices for presentation, researchers and decision-makers can make informed decisions and gain a deeper understanding of their ...


    CHAPTER FOUR. DATA PRESENTATION, ANALYSIS AND INTERPRETATION. 4.0 Introduction. This chapter is concerned with data pres entation, of the findings obtained through the study. The. findings are ...

  15. Presentation of Data

    Yes, the introduction, summary, and conclusion can help condense the information. Tabular Ways of Data Presentation and Analysis. To avoid the complexities involved in the textual way of data presentation, people use tables and charts to present data. In this method, data is presented in rows and columns - just like you see in a cricket match ...

  16. Presenting data in tables and charts

    The forms of data presentation that have been described up to this point illustrated the distribution of a given variable, whether categorical or numerical. In addition, it is possible to present the relationship between two variables of interest, either categorical or numerical. ... CONCLUSION. Understanding how to classify the different types ...

  17. How To End A Presentation & Leave A Lasting Impression

    From summarizing key points to engaging the audience in unexpected ways, make a lasting impression with these 10 ways to end a presentation: 1. The summary. Wrap up your entire presentation with a concise and impactful summary, recapping the key points and main takeaways.

  18. PDF Tabular and Graphical Presentation of Data

    1. Data Cleaning 2. Descriptive Analyses 3. Main analysis for exposure outcome 4. Secondary analysis 5. Creating Tables and Graphs with results 6. Preparation of oral presentation or conference poster. 7. Preparation of final tables and graphs for publication (usually 2‐6 for a journal article).

  19. Presentation of data II

    A graph does represent a numerical data in the form of a qualitative structure and provides important information. Statistical surveys and experiments provides valuable information about numerical scores. For better understanding and making conclusions and interpretations, the data should be managed and organized in a systematic form.

  20. How to Write a Results Section

    Here are a few best practices: Your results should always be written in the past tense. While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible. Only include results that are directly relevant to answering your research questions.


    Chapter 10-DATA ANALYSIS & PRESENTATION. The document outlines the steps for planning and conducting data analysis, including determining the method of analysis, processing and interpreting the data, and presenting the findings through descriptive and inferential statistical analysis techniques to answer research questions.

  22. Hook Your Audience Online: Storytelling Techniques for Slides

    Conflict: Introduce the main problem or challenge that your presentation will address. This is where you highlight the issue that needs solving. Climax: Present the peak of your story. This is the most critical part, where you reveal the key insights or data points. Resolution: Offer solutions or conclusions to the problem you introduced. This ...

  23. Writing a Research Paper Conclusion

    Table of contents. Step 1: Restate the problem. Step 2: Sum up the paper. Step 3: Discuss the implications. Research paper conclusion examples. Frequently asked questions about research paper conclusions.

  24. Python vs R: Which Language Excels in Data Analysis?

    Introduction Data is the lifeblood of your organization, so the ability to analyze and interpret data effectively is crucial to your success. Across industries, organizations rely on data analysis to make informed decisions, optimize processes, and gain a competitive edge. Python and R are two of the most popular programming languages among the many tools and methods available for data ...

  25. Crafting Compelling Presentations: Strategies for Effective

    Evaluating these indicators can provide valuable insights into the effectiveness of your presentation and its impact on your organization's cybersecurity efforts. In conclusion. Effective communication in cybersecurity is a vital skill that enables professionals to convey complex concepts, risks, and mitigation strategies clearly and concisely.

  26. PDF The Sustainable Development Goals Report 2024

    data availability was notably low, with average scores ranging from 3 to 34. Interestingly, high-income countries generally exhibited lower overall disaggregated data availability compared to low- and middle-income countries. III. How to harness the power of data. Data availability score by sex and other characteristics and type of indicator,

  27. Biden's disastrous debate pitches his reelection bid into crisis

    Biden had entered the debate facing a somber test — to prove to the majority of Americans who believe he is too old to serve that he is vital, energetic and up to fulfilling his duties in a ...

  28. College Football 25 Rankings Week Showcase

    For even more on the presentation elements and how they come to life, ... The Development Team meticulously examined hundreds of thousands of data points to arrive at our team power rankings. With help from our friends at Pro Football Focus (PFF), the team analyzed all 134 rosters, thousands of players, years worth of game film, and mountains ...

  29. Proselytizing the potential of influencer marketing via artificial

    2.1. Artificial intelligence. Artificial intelligence is designed to mimic the human brain and make decisions similar to those made by humans in a variety of settings (Bar-Ilan, Citation 2004).Artificial intelligence is a technique that gives businesses the chance to obtain a competitive edge by utilising large data to specifically address the needs of their clients through individualised ...

  30. Funds heavily sold CBOT corn ahead of bearish US acreage data

    Speculators deepened their short bets in Chicago traded corn ahead of last week's major U.S. data release, establishing their most bearish-ever corn views for the end of June.