data analytics coursework

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• Data Science
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The 5 Best Courses to Learn Data Analytics for 2024

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The increasing demand for data analysts has spurred a proliferation of data-related courses. However, many of these programs do not offer a well-rounded learning experience that covers all the essential analytics skills.

For this article, I've sifted through the best online course platforms to find the best classes for learning data analytics. Since many skills are involved in an analyst's job, I've provided the best course option for specific categories. Hopefully, this categorization will help you more easily decide which course is the best for you .

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If you're still having trouble picking a course, feel free to check out the Learning Guide at the end of this article for information about what data analysts do, what skills they need, and recommendations on how to get started.

Course selection criteria

There're thousands of data courses these days, so to narrow down the options to only the most qualified, I considered the following data points:

• Organization and quality of content
• Student reviews
• Staff and student discussions
• Quizzes and assignments
• Inclusion of statistical concepts

These criteria helped reduce the number of potential courses to only a handful, of which I then compared and contrasted to bring you my recommendations listed below. These final few are what I found to be the best for learning data analytics online today.

TL;DR Best Data Analytics courses for 2024

Google Data Analytics Professional Certificate — Google, Coursera

Complete beginners looking for a broad introduction to data analytics focused on Google products.

This highly-rated course from Google covers a wide range of topics and is designed to "have you job-ready in less than six months," according to their description. Google states that certificate holders will have access to an Employer Consortium, which comprises 150 U.S. companies committed to considering graduates for entry-level analytics positions.

Out of all the courses I could find, this series by Google is the most comprehensive. The course video content is well-organized, professional, and exciting, and with so many students enrolled, there's an active community for questions and answers. Through the course, you're exposed to the most popular analytics tools: Google Sheets, SQL, R, and Tableau. These topics cover the entire data analytics pipeline and give you the skills to develop your own projects.

Course 1: Foundations: Data, Data, Everywhere

• Intro to data analytics
• Intro to analytics tools (Sheets, SQL)

Course 2: Ask Questions to Make Data-Driven Decisions

• Problem-solving
• Asking good questions
• Spreadsheet basics
• Communicating effectively

Course 3: Prepare Data for Exploration

• Data formats, types, modeling, collection
• Data ethics
• Using spreadsheets with databases
• Intro to BigQuery
• Data security

Course 4: Process Data from Dirty to Clean

• Data integrity
• Data cleaning with spreadsheets and SQL
• Resume/career info

Course 5: Analyze Data to Answer Questions

• Organizing data for Sheets and Bigquery
• Formatting and transforming data
• Data aggregation functions in Sheets and SQL
• More formulas, functions, and pivot tables
• Intermediate SQL

Course 6: Share Data Through the Art of Visualization

• Intro to data visualization concepts
• Creating visualizations with Tableau
• Developing data stories
• Creating effective presentations

Course 7: Data Analysis with R Programming

• Intro to the R language and RStudio
• Cleaning, organizing, and transforming data with R
• Creating visualizations with R
• Making reports and docs for R analyses

Course 8: Google Data Analytics Capstone: Complete a Case Study

• Developing your own project to display in your portfolio and resume
• Info on building a compelling portfolio

The curriculum is divided into "courses," but some of the content can be completed far shorter than a typical Coursera course. Some learners report they completed the entire Specialization in under a month. So, depending on your background, you may well finish the course series quicker than advertised.

Enroll in the Google Data Analytics Professional Certificate

Become a Data Analyst — Linkedin Learning

Those looking for broad exposure to many data analytics tools, but with more of a focus on Microsoft products

The data analyst learning path from Linkedin Learning in a collection of courses organized in a way that provides you with a well-rounded education. The course path is similar in scope to that of Google's listed above, but focusing on Microsoft products, namely Excel and Power BI.

One benefit to this course series over Google's is the inclusion of statistics modules, which is excellent for learners that would like to strengthen their math for analytics.

Course 1: The Non-Technical Skills of Effective Data Scientists

• Imperative non-technical skills

Course 2: Learning Excel: Data Analysis

• Basic statistics in Excel
• Visualizing data
• Hypothesis testing
• Using distributions
• Covariance and correlation
• Bayesian analysis

Course 3: Data Fluency: Exploring and Describing Data

• Data fluency
• How to use the most common chart types
• Descriptive statistics

Course 4: Learning Data Analytics: 1 Foundations

• Importing and cleaning data
• Creating and maintaining datasets
• Intro to Power Query

Course 5: Learning Data Analytics Part 2: Extending and Applying Core Knowledge

• Working with business data
• Building datasets with queries
• Building pivot tables
• Intro to Power BI
• Presenting data in meetings

Course 6: Excel Statistics Essential Training: 1

• Types of data
• Probability
• Central tendency
• Variability
• Distributions
• Analysis of variance (ANOVA)
• Repeated measure analysis
• Correlation

Course 7: Predictive Analytics Essential Training: Data Mining

• Defining problems
• Understanding data requirements
• Problems and solutions you'll face with data
• Deploying models
• Cross-Industry Standard for Data Mining (CRISP-DM)

Course 8: Power BI Essential Training

• Getting data into Power BI
• Reports and visualizations
• Creating dashboards
• Sharing data
• Power BI mobile

Course 9: Learning Data Visualization

• Information hierarchy
• Storytelling
• Visual paradigms
• Interactivity

Course 10: Tableau Essential Training

• Managing data sources
• Tableau worksheets and workbooks
• Creating custom calculations and fields
• Analyzing data in Tableau
• Mapping geographic data
• Creating dashboards and actions

Course 11 SQL: Data Reporting and Analysis

• Using SQL to report data
• Grouping SQL results
• Merging data
• Some advanced syntax

Course 12: R Essential Training: Wrangling and Visualizing Data

• Intro to R and RStudio
• Importing data
• Visualizing data in R
• Wrangling data
• Recoding data

Course 13 Data Cleaning in Python Essential Training

• Causes of errors
• Detecting, preventing, and fixing errors

You can acquire many in-demand skills from the data analyst path on Linkedin Learning. There is some overlap with the statistics and visualization content, but for a beginner, this can only reinforce your newly acquired analytics skills as a beginner.

The one gripe I have with this path is that the Python course at the end already assumes Python experience, but nowhere in the path is there a Python syntax course. If you intend to complete this path, I'd also recommend learning Python syntax on Codecademy , the top Python course according to the data.

Enroll in the Become a Data Analyst path

Excel Skills for Data Analytics and Visualization Specialization — Coursera

Those with some Excel experience looking to analyze data as fast as possible.

This course from Macquarie University covers many of Excel's intermediate to advanced concepts, allowing you to clean, analyze, and visualize data efficiently. If you're rusty on statistics, it may be a good idea to complement this course with an appropriate statistics course since the math side of analytics isn't covered here.

Overall, the quality of instruction is fantastic, and you'll find plenty of assessments and assignments to hone your Excel and Power BI skills.

Course 1: Excel Fundamentals for Data Analysis

• Cleaning and manipulating text
• Working with numbers and dates
• Defined Names
• Tables for automating data manipulation
• Logical and lookup functions

Course 2: Data Visualization in Excel

• Conditional formatting, sparklines, and number formats
• Various charting techniques
• Specialized charts
• Interactive dashboards using pivot charts, slicers, and dynamic charts

Course 3: Excel Power Tools for Data Analysis

• Get and transform data with Power Query
• Power Pivot and data models
• Visualize data with Power BI

Once you've completed this course and are comfortable analyzing data in Excel, it may make sense to start learning SQL or R. For SQL, see the previous course; for R see the following course.

Enroll in Excel Skills for Data Analytics and Visualization Specialization

Data Analyst in R — Dataquest

Beginners who are more interested in the programming side of data analytics, as opposed to a spreadsheet software, like Excel.

Dataquest is one of the most popular interactive data science learning platforms. Despite not having videos for each topic, their teaching methodology and project-based learning style are very effective.

The Data Analyst with R path, which also has a Python version , brings learners with no programming experience through the entire analytics pipeline using R and SQL.

Python is often the first choice for working with data, but if your goal is to become proficient in analytics, R plays more nicely with data and statistics out of the box. The language's straightforwardness lets you be productive with data faster than Python in many cases.

• Intro to R syntax
• Data structures
• Loops, iterations, functions, and control flow
• Data visualization
• Data cleaning
• SQL fundamentals
• Getting data from APIs and web scraping in R
• Beginner to intermediate statistics using R
• Probability fundamentals and conditional probability
• Linear regression
• Machine learning fundamentals
• Interactive web apps with R

Dataquest has a much stronger curriculum in statistics and probability than other courses, so if you feel like your math needs work, this course path will help. Furthermore, Dataquest can also serve as a great resource for practicing your skills through their guided projects, regardless of which other course you decide to take.

Enroll in the Data Analyst in R path

Business and Data Analysis with SQL — Skillshare

Those who are more interested in learning and applying SQL to analytics, or anyone wishing to expand past spreadsheets

Geoff Devitt, the instructor for this course, is a 20+ year veteran of the Big Data industry and created this Skillshare class to share what he's learned building projects and analyzing data over his career.

Unlike other courses mentioned in this list, this course focuses solely on solving analytics problems using Structured Query Language (SQL), a programming language built to interface with databases. Since SQL runs many businesses, it was one of the most highly requested skills mentioned in job posts during my research for this article.

• Intro to databases
• Normalizing data
• Fact tables
• Aggregating data
• Entity Relationship Diagrams (ERD)
• Setting up and connecting to MySQL
• Beginner to intermediate SQL
• Data analysis example project
• Query explain plans
• MySQL administration

Once you've finished this course, you should be reasonably comfortable with SQL and can start working on your portfolio projects.

Enroll in Business and Data Analysis with SQL

Learning guide

What do data analysts do.

Data analysts' responsibilities vary widely from company to company, but most have the following primary objectives: 1) Pull data from a variety of sources, such as databases, Excel workbooks, and CSVs 2) Apply statistical analysis to the data 3) Condense information into easily digestible formats 4) Create visualizations, summary reports, and dashboards

A data analyst creates value for a company by helping them make better decisions and, ultimately, to generate more revenue.

Data Analyst vs. Data Scientist

Becoming a data scientist often starts with first becoming a data analyst. If we were to look at a Venn diagram of the skillsets of both roles, the data scientist's circle would fully encompass the data analyst's.

The Northeastern University blog sums up the differences nicely:

While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists , on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

Essentially, data analysts work with established data and pipelines to draw insight, while data scientists create new data pipelines and use more advanced tools and techniques to solve more complex problems.

Data analyst tools and skills

Data analysts use various tools and processes, so the courses you take will depend on your desired position and what knowledge you already have. One company may need an analyst primarily for Excel and Power BI, while another business needs someone with SQL and Tableau experience.

If you're coming into data analytics with no programming experience, your quickest path to job-ready would be to build proficiency in a spreadsheet program like Excel or Google Sheets and visualization software, such as Tableau or Google Data Studio.

Alternatively, if you have any programming experience, starting with SQL, Python, or R would be a good choice. Most job posts mention SQL, so if that's your primary interest, feel free to check out my picks for the best SQL courses , which rank the best courses for learning SQL in general. In this article, I focus my SQL suggestions primarily on using SQL for analytics.

Which technology (Excel, SQL, Tableau, etc.) do I start with?

Learning Excel is one of the best ways to get started in data analytics since many people already have exposure to the software. You can more easily build on this familiarity through an Excel-specific course, like Excel Skills for Data Analytics and Visualization Specialization from Coursera.

Despite using Python to analyze data for years, I still reach for Excel when I need to do a quick analysis, share results with the team, and make educated decisions. Many companies still run their entire analytics pipeline through Excel, so many job opportunities are awaiting skilled Excel data analysts.

Knowing more about Excel (or Google Sheets) can only benefit you in analytics, but if you already have a decent Excel foundation, going on to SQL would be a safe bet. Many businesses store their data in a SQL database and need analysts to pull, summarize, and make sense of that data. When researching job posts for this article, I found more jobs requiring someone with solid SQL experience than any other technology.

Since SQL is a programming language, it makes sense that most SQL courses focus solely on syntax and data modeling, which is why I've included Business and Data Analysis with SQL here since it has more of an analytics approach.

Finally, if you already have some spreadsheet experience and would rather move on to programming language with more applications, then learning R is an excellent choice. Unlike SQL, which is confined to databases, R is a regular programming language with many advantages in statistics and visualization. R is more straightforward than Python for pure analytics work, but Python skills are still in high-demand. Either way, I would recommend working through the free R or Python material on Dataquest to start learning as quickly as possible.

Apply what you learn

No matter which skill, technology, or path you start, it's imperative you practice everything you learn. Solidify your knowledge by analyzing something you're interested in, and produce reports or dashboards to display your findings. Many employers consider unique, completed projects an equivalent replacement for work experience. Not only does talking through a passion project you've completed aid interviewers with gauging your abilities, but it also helps you escape imposter syndrome and build self-confidence.

Are certificates worth it?

Every course in this list offers a certificate, and they are a fantastic way to prove to yourself that you completed a course and solidified knowledge of a topic. Despite that, you should approach building your resume as if the certificates you earn don't exist. You should focus solely on building projects that demonstrate your knowledge and aptitude, especially when you lack past job experience.

Pick a niche you're interested in, whether it's sports, finance, health, or marketing, and build something that interests you. It doesn't have to be groundbreaking, but it should showcase your abilities to interviewers. If you're looking for inspiration, check out Kaggle datasets and community notebooks, or follow a few of the Dataquest or Coursera guided projects.

Good luck, have fun!

Learning data analytics can be a challenging journey, but whether for a career or hobby, I'm confident analytics will lead you down a path of exciting projects, tangents, and fortuitous findings.

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The Top 14 Data Analytics Training Courses

The global economy is facing seismic shifts and traditionally steady jobs no longer offer the stability they once did. As many people now look to diversify their skills, data analytics is proving a compelling solution.

This once-niche discipline is rapidly expanding into all areas of the modern economy—from IT to healthcare and finance, the sciences, construction, and more. Plus, beyond the core technical skills, data analytics roles increasingly require creative, analytical thinkers; from those with a meticulous eye for detail to others capable of seeing the bigger picture.

In short, data analytics is for everyone who wants to give it a try.

In this post, we explore 14 of the best data analytics training courses available right now. Whether you’re a career shifter or a recent graduate, we’ll look at everything you need to get the basics down.

Times are tough and not everyone has money to spare, so we’ve included some options: data analytics training courses you can find for free online; paid online bootcamps, and, for the committed; a sample of full-time graduate programs.

We’ll cover:

Beginner: Free data analytics training courses

• Intermediate: Paid data analytics training courses
• Expert: College-based data analytics training courses

Ready to find a data analytics training course that’s right for you? Then let’s dive in.

If you’re new to data analytics and want to explore some of its themes and tools before forking out for a more comprehensive training program, here are five great free training courses you might want to consider:

1. Data Analytics Short Course – CareerFoundry

Best for: Those wanting to get a quick taster of the world of data analytics

Cost: Free, but with the option to enroll into the full Data Analytics Program afterward.

Completion time: 5 days, self-paced

Our free, five-tutorial data analytics short course is ideal if you want a digestible introduction to data analytics. When you sign up to the platform, you’ll get access to five hands-on lessons delivered by email—each focused on a separate step of the data analytics process. The course provides a broad view of data analytics, setting you up to explore the topic further if you choose.

This short course covers everything to get a broad overview of the field: from the different types of data analytics roles, to a summary of tools and skills you’ll need to develop if you pursue a career in the field. You’ll also have a few opportunities to get a hands-on experience with the basics of the data analytics process.

2. Data Analytics Basics for Everyone – edX:

Best for: Those seeking an all-round, high-level taste of data analytics.

Cost: Free, with optional upgrades for certifications or add-on training.

Completion time: 5 weeks, 2-3 hours of study a week.

Led by expert professionals from IBM, this self-paced course covers all the need-to-know information about data analytics across 10-15 hours of video lectures. The introductory course offers a straightforward explanation of what data analytics involves, including the different steps in the process.

Although very high-level, it covers both the practical elements of the topic (such as various types of data structure and file formats) as well as career-related content. For example, the course clearly outlines the difference between data engineers, data scientists, and business intelligence roles, while also exploring different career paths.

You won’t get any in-depth training here on the tools and software that data analysts typically use. However, you will enjoy a whistle-stop tour through the major big data platforms used to gather, mine, analyze and visualize data. Overall, a great all-around introduction to get any beginner started.

3. SQL for Data Analysis – Udacity

Best for: Those who want to learn the basics of SQL.

Cost: Free (but expect regular plugs for their paid courses!)

Completion time: About 30 hours of self-paced learning across 4 weeks.

Relational databases are one essential building block of data analytics, and mastering SQL is necessary for effectively managing them. This free course from Udacity covers all the basics of SQL, gradually building your knowledge across five standard and two advanced lessons. Starting with an introduction to SQL, you’ll learn the key commands for querying a database. You’ll proceed to work with multiple tables, picking up additional functions on the way via practical examples.

Finally, you can expect to move on to more advanced techniques, such as how to quickly query data across giant data lakes. We love the step-by-step approach of this course, which ensures that learners follow along with it, rather than being thrown right in at the deep end and being expected to swim.

4. Python for Data Science – Cognitiveclass.ai

Best for: Those who want an introduction to Python programming.

Cost: Free.

Completion time: About 20 hours of self-paced learning.

This beginner-friendly introduction to Python is suitable for all, the only prerequisite being the basic math skills that anyone should have from school. Split across five modules with a final exam, the course introduces the basic concept of Python and how it’s used, before progressing to its core data analytics functionality.

Like the SQL course, it builds on knowledge as it goes. You’ll start with entry-level content, like how to define variables and set conditional statements, all carried out within Cognitive Class’s Jupyter Notebook sandpit (which is also beneficial since data analysts often use this software).

Once you’ve picked up the standalone functionality, the course shifts focus to one of Python’s many popular data analytics libraries, pandas, which is commonly used for data analysis, data cleaning , and machine learning tasks. While you’ll still have plenty to learn after completing the course, it offers a concrete foundation to build upon.

5. Excel Basics for Data Analysis – Coursera

Best for: Those looking to unleash the statistical power of MS Excel.

Cost: Free, so long as you complete it within the 7-day trial period.

Completion time: Approximately 12 hours of learning.

Another course from IBM via Coursera, this data analytics training course focuses on everyone’s favorite spreadsheet software, MS Excel. Spread across 9 modules, the first three focus specifically on MS Excel, starting with a beginner’s introduction to spreadsheets. Next, it progresses to topics such as how to insert, filter, and sort data.

Finally, more complex themes emerge, including an exploration of Excel’s most valuable data analytics functions, (like how to create visualizations and dashboards). Modules 5 to 8 focus on the R programming language and aren’t exclusively Excel-focused. However, R can be used with Excel, and is a useful tool in its own right, so you may decide to continue on.

While you’ll need to complete the R modules to gain a certification, you can easily skip them if you’re using Coursera’s 7-day free trial. The whole course is capped off with a quiz-based assessment. But again, you can skip this if you want to access the rest of the course content for free.

6. Machine Learning for Data Analytics – Coursera

Best for: Those who want to dive deep into machine learning.

Cost: Free if you complete it within Coursera’s 7-day trial period.

Completion time: Approximately 61 hours of learning.

Machine learning isn’t a topic beginner data analysts need to master but it’s so fascinating we couldn’t resist including one course on our list! This particular one is also delivered by Andrew Ng who is kind of a big deal in data science circles.

Although not technically free—and at 61 hours of learning, a squeeze to complete within a 7-day trial—we reckon it’s worth completing at least the introductory module. This is only 42 minutes of learning and covers the basics of machine learning theory, such as the difference between supervised and unsupervised learning.

If you progress, however, you’ll dive into the technical stuff, such as multivariate linear regression, logistic regression, and how to train neural networks. While this stuff is not for beginners, it’s a great option if you’re a math nut who finds the potential of artificial intelligence intriguing.

2. Intermediate: Paid data analytics bootcamps

Done dabbling and want to invest in the no-nonsense skills you’ll need for an entry-level data analytics role? Online data analytics bootcamps are becoming increasingly popular for this.

While these types of data analytics training courses don’t always come cheap, they’re much more affordable than full degrees and are usually flexible enough to fit around your schedule. Some even come with job guarantees, making them a much safer investment. Here are five top courses to consider:

1. Data Analytics Program – CareerFoundry

Best for: Beginners looking for comprehensive training, mentoring, and career support.

Cost : $6,900 USD (via payment plan, or with a discount if you pay upfront).

Completion time: 5 months studying full-time (30-40 hours a week) or up to 8 months studying part-time (15-20 hours a week).

Do you know that data analytics is the path you want to take? Do you also want the guarantee of a job at the end of your course? Then look no further than CareerFoundry’s Data Analytics Program. Regardless of background, the course is designed to take learners from beginner to job-ready in 8 months. While longer than some data analytics training courses, it’s fully comprehensive, covering the tools, skills, and processes you’ll need in detail.

To effectively prepare you, CareerFoundry’s Data Analytics Program uses a project-based curriculum to get you working hands-on from the very start in a professional environment. It offers unrivaled mentoring from active industry professionals and the Career Services team offers job coaching.

CareerFoundry’s offering comes in at $6,900 for the entire program, b ut the cost of the tuition is dependent on your location and is competitively priced. A range of flexible payment options include paying upfront, or getting a small course discount. Contact them to find out your local pricing and if there are any partial scholarships available.

Once you’ve completed the course, you’ll not only be skilled-up—you’ll have a portfolio of projects, a polished resume, and be ready for interviews. And if you don’t have a job within six months of completing the course? Provided you fulfil some clearly stated conditions, you get your money back, making CareerFoundry about as safe an investment as you can get.

2. Online Data Analytics Course – General Assembly

Best for: Non-data analytics professionals looking to supplement their skills.

Cost : $3,950 USD (with employer sponsorship options).

Completion time: 10 weeks part-time or one week intensive.

As data analytics starts to permeate all sectors, you might decide to supplement your present role (e.g. in marketing or finance) rather than changing careers completely.

In this case, General Assembly’s data bootcamp is aimed at busy professionals looking to upskill in their current roles. This flexible, self-paced course covers all the essential functions of Excel and SQL, along with the need-to-know aspects of data visualization and software tools like Tableau.

It includes 40 hours of learning, project coursework, and a final assignment with individualized feedback and guidance from an expert instructor. You’ll get a completion certificate, too, ideal for showing off your newfound skills to your employers or on LinkedIn. While the course costs just under $4,000 USD, General Assembly welcomes employer sponsorship, so it’s definitely an option worth considering.

3. Data Science Career Tracks – Datacamp

Best for: Those who want a good selection of specific (and affordable) modules.

Cost : $25 USD per month.

Completion time: From 12-25 hours of learning per course.

Unlike many course providers, Datacamp specializes specifically in data science. The best part about their courses is the number available. Rather than providing a single generic course, you can sort Datacamp’s offering by the technology you want to focus on. From spreadsheets in Excel to data analysis using Python and R, specialized industry software such as Tableau for data viz, or Microsoft Power BI (an industry staple for many big companies), the choice is yours.

For $25 USD per month, Datacamp’s Career Tracks are also very affordable, and their bitesize courses cover everything from collecting and cleaning data, to theories like statistical inference, and even highly-specific training such as how to analyze genomic data. You’ll learn by doing, working on real projects, and collecting rewards as you go. And if you’re looking to shift into a specific role, Datacamp’s dedicated career service can help you select the best set of courses to get you on the right track.

4. Data Analytics Bootcamp – Springboard

Best for: Career changers with existing experience.

Cost: $10,140 USD or $8,500 USD if you pay upfront.

Completion time: 420 hours across 8 months(15-20 hours per week).

Already got some experience using office, design, or programming tools? Then Springboard’s Data Analytics Bootcamp offers the additional technical skills you need to take things up a notch. Going beyond mere data analytics, the course also focuses on areas where employers find the most significant competency gaps—things like critical thinking, problem-solving, and communication.

The Springboard bootcamp aims to develop these so-called ‘soft skills’ while helping you devise a job search strategy. They even provide career support for six months after completing the program. This aftercare is particularly practical as it includes things like interview tips and negotiating salaries.

Unlike other courses, you will require a little experience to enroll, and it’s also one of the pricier options on our list. However, like CareerFoundry, Springboard offers a job guarantee, with a full refund if you still don’t have a job six months after graduating.

5. Data Science Learning Paths – Dataquest

Best for: Those looking for flexible and affordable career-focused modules.

Cost: $24.50 per month for an annual subscription.

Completion time: Depends on the modules you choose.

Another flexible and affordable option is Dataquest’s Data Science Learning Paths. Reflecting the increasingly broad career paths available in data analytics, Dataquest offers 70+ courses on topics ranging from Python to R, SQL, Excel, and much more.

These modular courses can be compiled in any way you wish, but Dataquest also provides 5 pre-structured career paths to help prepare you for specific roles in areas like business analytics or data engineering. They offer an additional 12 paths for specialized skills development, too, in areas like machine learning, data pipelines, or statistics and probability. Check out their full list of 70+ courses here .

At the lower end of the price range, Dataquest’s offering doesn’t come with mentoring. But for those who are happy studying under their own steam, it’s a great way to build skills. They also regularly add new courses, keeping things industry-relevant.

3. Expert: College-based data analytics programs

If you already work in the field you might be looking to jump the tracks from data analytics into more senior data science roles.

If so, a master’s degree is usually a minimum prerequisite. You’ll find countless options available, varying widely depending on your location and particular area of interest. These aren’t for beginners but to give you a flavor, here are three possible options from North America. Check local institutions first, though.

1. Master of Business Analytics (MBAn) at MIT

If you’ve got business specifically in mind, MIT’s MBAn program is a 12-month master’s course delivered by some of the leading minds in data science. It will fully prepare you with the skills necessary to drive smarter business decisions and solve some of the biggest problems that companies face.

Aimed at engineers, mathematicians, physicists, computer programmers , and other high-tech professionals, the program’s core focus is on machine learning; how this emerging technology is currently being used, and what its future applications are likely to be. This includes all the practical aspects and theory you’ll need to become an expert on the topic. As a prerequisite, you’ll either need an undergraduate degree in a related subject or significant hands-on experience in the field.

2. Master of Science (MSc) in Applied Computing at the University of Toronto

If you prefer not to hone in purely on data science for business, the University of Toronto’s MSc in Applied Computing is a good example of a course that diversifies a data analyst’s skills for all industries.

Concentrating on computer and data science, applied math, and quantum computing, this is one of many such degrees aimed at preparing tomorrow’s data scientists for the high-tech economy. Four semesters over 16 months, this particular course includes an applied-research internship with one of the university’s employer partners (something to consider looking out for if you want hands-on, practical experience).

Once again, all applicants need to have completed (or be in the process of completing) a relevant undergraduate degree with certain grade expectations, which is also a common requirement for this type of course. Although this is just one example, you’ll find many similar MScs available in locations all over the globe.

3. Master of Statistics at Stanford University

All master’s degrees in data-related fields require some expertise in math. However, Stanford’s Master’s in Statistics focuses specifically on mathematical topics and how these can be applied to an individual learner’s specific area of interest. On the Stanford course, core modules include probability theory, stochastic processes, and applied statistics. Once complete, students will then apply these to their elective area of interest via additional modules of their choosing.

Elective topics are diverse, ranging from the biological sciences to computation, mathematical engineering, computer science, economics, operational management, and much more. Stanford’s course is about 15-18 months, but students have three years to complete it, making it a very flexible and career-focused option.

While these are just three of many master’s degrees out there, they should offer a taste of just how niche you can go with your data career over time. 

From broad-stroke tutorials for beginners to career-focused bootcamps and niche university specializations, there are many courses available for would-be data analysts to choose from. The options might seem staggering at first but don’t be put off. One of the best things about data analytics is that you can carve your career in any way that appeals.

There’s no single prescribed path that you have to take. We hope this list of data analytics training courses gave you a good idea of the scope that’s available to you, no matter what stage of your career you’re currently at.

To learn more about a potential future career in data analytics, check out this free, 5-day Intro to Data Analytics short course or read the following introductory guides for more:

• What Is Power BI?
• Creating Data Visualizations in Tableau (A Beginner’s Guide)
• What Is Prescriptive Analytics? A Complete Guide

Best Data Analytics Certificates Online

Best data analytics certificates online of 2024.

Updated: Jun 4, 2024, 10:04am

Data-related roles are some of the most versatile and in-demand careers in the United States. With more access to consumer data than ever, companies rely on data analysts to optimize their financial, marketing and production decisions. As a result, demand for data scientists is expected to triple over the next decade, the U.S. Bureau of Labor Statistics reports.

Data analysts work in nearly all sectors, including government, marketing, insurance, accounting, education and manufacturing. At any scale, they help organizations run efficiently by organizing, managing and interpreting data. Depending on the project scope, they use software, programming skills and machine learning to identify patterns and predict outcomes.

If you enjoy working with numbers, tackling complicated projects and learning new technology, a data-oriented career may suit you. Completing a data analyst certificate online can help you develop competitive skills that all industries need. Below, we’ll explore the 10 best online data analytics certificates.

Why You Can Trust Forbes Advisor Education

Forbes Advisor’s education editors are committed to producing unbiased rankings and informative articles covering online colleges, tech bootcamps and career paths. Our ranking methodologies use data from the National Center for Education Statistics , education providers, and reputable educational and professional organizations. An advisory board of educators and other subject matter experts reviews and verifies our content to bring you trustworthy, up-to-date information. Advertisers do not influence our rankings or editorial content.

• 6,290 accredited, nonprofit colleges and universities analyzed nationwide
• 52 reputable tech bootcamp providers evaluated for our rankings
• All content is fact-checked and updated on an annual basis
• Rankings undergo five rounds of fact-checking
• Only 7.12% of all colleges, universities and bootcamp providers we consider are awarded

Our Methodology

We ranked 24 accredited, nonprofit colleges offering online undergraduate data analytics certificates in the U.S. using 12 data points in the categories of credibility, affordability, student outcomes and student experience. We pulled data for these categories from reliable resources such as the Integrated Postsecondary Education Data System ; private, third-party data sources; and individual school and program websites. Data is accurate as of February 2024.

We scored schools based on the following metrics:

Student Outcomes:

• Graduation rate within eight years of normal time
• Pell Grant recipient graduation rate
• Retention rate
• Pell Grant graduation rate vs. overall graduation rate
• Ability to transfer certificate credits to a degree program

Student Experience:

• Student-to-faculty ratio
• Socioeconomic diversity
• Program’s coursework (excluding student orientations, field experiences and labs) is available 100% online
• Portion of undergraduate students enrolled in at least some distance learning courses

Credibility:

• Fully accredited
• Nonprofit status

Affordability:

• Per-credit tuition rate

We chose the 10 best schools to display based on those receiving a curved final score of 88% or higher.

Find our full list of methodologies here .

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Should You Earn a Data Analyst Certificate Online?

Accreditation for online data analytics certificates, how to find the best online data analytics certificate for you, frequently asked questions (faqs) about online data analytics certificates, texas a&m university – college station, university of richmond, mount saint mary’s university, champlain college, maryville university of saint louis, university of north texas, franklin university, pennsylvania western university, grand valley state university, university of arizona, best for students on a budget.

Certificate Tuition

$260/credit

Time Commitment

Can Certificate Credits Transfer to a Degree?

Texas A&M University offers an undergraduate data analytics certificate for students with some college experience. To enroll in the upper-level university, learners should have at least 30 transferable college credits. Applicants with a bachelor’s degree can opt for the graduate-level certificate.

The undergrad certificate emphasizes business analytics with real-world applications, covering theory and practice, data visualization, data mining, and web and social analytics. The College of Business Administration carries accreditation from the Association to Advance Collegiate Schools of Business.

• School Type: Public
• Certificate Prerequisites: At least 30 transferable college credits; minimum 2.0 transfer GPA
• Certificate Credit Requirements: 18 credits
• Example Courses: Data mining, web and social analytics
• Certificate Graduation Requirements: Minimum 2.0 GPA
• In-Person Requirements: No

Best for customization

$575/credit

27-36 hours per week for 30 weeks

At the Virginia-based University of Richmond , learners with bachelor’s degrees can pursue a certificate of applied studies in data analytics. Instead of following a set curriculum, students complete two core courses and choose four electives, with options focusing on big data, ethics, Java programming and predictive analytics.

Students can also set their learning pace, finishing the program in two semesters, in one year or one class at a time. Enrollees can complete the mostly asynchronous coursework online but might have the opportunity to take some on-campus classes. Graduates gain lifetime access to the school’s career services.

• School Type: Private
• Certificate Prerequisites: Bachelor’s degree, minimum 2.0 cumulative GPA
• Example Courses: Introduction to analytics, data visualization
• Certificate Graduation Requirements: N/A

Best for beginners

$350/credit

Mount Saint Mary’s University (MSMU) in Los Angeles creates opportunities by prioritizing social mobility, diversity and financial accessibility. Its six-course data analytics certificate helps learners build foundational skills, with a two-part “programming for everyone” series that introduces Python, SQL and database design. Throughout the project-based curriculum, students learn to design experiments, evaluate results, solve business problems using machine learning algorithms and improve predictive models.

MSMU offers relatively affordable tuition and several avenues for financial aid, including merit scholarships and support for undocumented learners.

• Certificate Prerequisites: High school diploma or GED® credential
• Example Courses: Programming for everyone, principles and techniques of data analytics

Best for career switchers

$335/credit

Based in Burlington, Vermont, Champlain College ’s data science certificate program is open to undergrads and bachelor’s degree holders. The coursework emphasizes career-ready skills that learners can apply to their jobs right away, including Python, data storage and manipulation, statistical analysis and presenting solutions.

Learners hoping to make a career switch can add on the fast-start formula career bundle, which includes a self-paced course, webinars and an ebook. Students learn to identify their career goals, create a résumé, develop a personal brand and create a job search strategy.

• Certificate Prerequisites: High school diploma or GED credential
• Example Courses: Introduction to Python, advanced data analytics

Best for students with some experience

$540/credit

10-15 hours/week per eight-week course; 48 weeks

Maryville University of Saint Louis , based in Missouri, offers a stackable, customizable data science certificate. The coursework explores industry-standard programming languages and tools like Amazon web services, MySQL and Databricks. The program’s 18 credits count toward a B.S. in data science or computer science.

Aside from the six required courses, applicants must complete a college algebra class. Students with prior experience in data science can replace two core courses with electives, allowing them to focus on new skills. After completing their certificate, learners can stack on related certificates in AI, cybersecurity or software development.

• Certificate Prerequisites: College algebra course
• Example Courses: Machine learning, big data analytics

Best for flexibility

$387/credit

Denton-based University of North Texas (UNT) allows students to earn an undergraduate data analytics certificate in a way that works for them. Learners can get facetime with instructors through optional weekly meetings, but the program does not require any synchronous participation. Enrollees have access to online tutoring, a career center and a writing center.

Students can finish the program in seven months by taking two courses per eight-week term, and UNT Online offers five start dates each year. Additionally, learners can enroll in focused, four-week micro-courses worth one credit each, which build into a certificate.

• Certificate Credit Requirements: 15 credits
• Example Courses: Principles of data structures, harvesting and wrangling; methods for discovery and learning from data

Best for accounting professionals

$398/credit

Franklin University in Columbus, Ohio, offers the only accounting-focused certificate on our ranking. The online accounting data analytics certificate teaches learners to create financial decision-making models, identify causes and solutions to business problems, design efficient systems, and communicate their findings. The curriculum covers Excel, Python and Tableau.

The 16-credit program can help graduates prepare for the CPA exam or earn licensure. Potential job titles include accounting analyst, auditor, audit analyst and financial analyst.

• Certificate Credit Requirements: 16 credits
• Example Courses: Accounting data analytics, accounting information systems

Best for SAS® certification

$322/credit

Pennsylvania Western University partnered with SAS, an industry-standard analytics platform, to create a program that leads to professional certification. The data science certificate helps learners prepare for two SAS exams, which help them earn certification. They also receive 50% off exam fees.

The 15-credit program includes a capstone course, allowing students to practice using SAS in a hands-on project. PennWest operates three Pennsylvania campuses but charges roughly the same tuition for in-state and out-of-state students.

• Example Courses: Big data tools, data preparation and cleaning

Best for students with college credit

$500/credit

Grand Valley State University (GVSU), located in Allendale, Michigan, delivers its applied data analytics certificate through six-week accelerated courses. Students get an introduction to applied statistics, learn to code in Python and R, and develop data visualization skills. The program requires four core courses and offers two elective options.

GVSU also offers stackable, short-term badges that can supplement students’ learning in areas such as data analytics, database management and cybersecurity.

All learners pay the same tuition, regardless of state residency.

• Certificate Prerequisites: Junior standing or 55 college credits
• Example Courses: Predictive analytics, statistics in the media

Best for career-ready training

The University of Arizona offers a data science and visualization undergraduate certificate through its online campus. Students gain job-ready skills and practical experience by using various data science techniques, platforms and programming languages to address realistic data problems.

The curriculum provides many choices for electives courses, including computational social science, introduction to machine learning and applied cyberinfrastructure concepts.

Learners can complete this certificate on its own or in tandem with an undergraduate degree. Up to six credits can count toward both the certificate and a degree from UArizona.

• Certificate Prerequisites: High school diploma or equivalent credential, completion of Arizona’s core competency requirements; first-year admission guaranteed for students in the top 25% of their high school class or who have a minimum 3.0 unweighted GPA
• Certificate Credit Requirements: 12 credits
• Example Courses: Statistical foundations of the information age, applied data visualization
• Certificate Graduation Requirements: Minimum 2.0 GPA, $15 graduation fee

An online data analytics certificate can prepare you for new opportunities in a high-paying, high-demand field. In many cases, you can pursue a beginner-friendly certificate instead of a four-year degree, which might save you significant time and money. However, online college isn’t the right choice for every learner. Consider the following factors before enrolling.

• Your learning style. Flexible asynchronous programs allow you to watch lectures and complete coursework on your own schedule. While this might sound ideal, an asynchronous format requires an extra level of commitment, organization and time management. If you prefer collaborative learning, look for synchronous or hybrid components.
• Your prior commitments. How much time can you dedicate to your education? If you work full time or handle responsibilities at home, you might need to enroll in a part-time program that suits your schedule. On the other hand, seek out full-time or accelerated programs if you want to enter the workforce as quickly as possible.
• Your budget. Online programs save you money on transport, housing and food. Since certificates require fewer credits than full degrees, they offer a more affordable education. However, not all programs accept federal student aid, so assess your budget and total costs before committing.

To narrow down your search for the best data analytics certificate, check each prospective school’s accreditation status. During the college accreditation process, schools submit to third-party evaluation to assess the quality of their financial management, student outcomes and faculty. You can only access federal student aid if you attend an accredited institution.

Additionally, attending an accredited school makes it easier for your credits to transfer to another college. That’s a crucial factor if you plan to use your certificate as a starting point for a bachelor’s or master’s degree.

Institutional accreditation comes from accrediting bodies approved by the U.S. Department of Education or the Council for Higher Education Accreditation (CHEA). To confirm a school’s status, consult CHEA’s directory .

Individual departments and programs can also earn programmatic accreditation, which varies in importance depending on the field. Most certificates on our list do not hold programmatic accreditation. However, Texas A&M University’s business school is accredited by the Association to Advance Collegiate Schools of Business.

Most data analytics certificates cover basic career skills such as Python programming , data management and data visualization tools. But several of the programs on our list offer specialized curriculums focusing on particular skills, topics or softwares.

Finding the best online data analytics program for you depends on your academic history, interests, goals, timeline and budget. Below, we’ll discuss a few factors to consider.

Consider Your Future Goals

Data analysts can find lucrative work in nearly every industry, including marketing, finance, insurance, healthcare and education. To identify your ideal program, clarify your long-term goals, and research interesting industries and job titles. Evaluate your existing strengths and identify your improvement areas. For example, you might need to become proficient in industry software like SAS or Ersi.

If you already have professional experience, look for schools that allow you to test out of basic courses or pursue new electives. Otherwise, many programs can take you from a total beginner to an entry-level practitioner.

Your enrollment options will be determined by your level of education. Some programs welcome learners with only a GED credential, while others require a bachelor’s degree. The right certificate gets you closer to your academic goals, whether you want to pursue a more advanced degree later or supplement your existing degrees.

Know Your Budget and Financing Options

Earning a data analyst certificate online can offer a more affordable education. Online programs typically cost less than their in-person counterparts, and certificates require fewer credits than full degrees.

Total tuition for the schools on our list ranges from $4,680 to $10,350. Per-credit tuition rates depend on factors like your enrollment status, whether you attend a public or private school, and where you live.

Keep in mind that while our ranked programs only require 12 to 18 credits, you may have to earn a certain number of credits before enrolling, which may incur additional costs. For example, some programs require incoming certificate students to have 30 to 55 transferable credits, which increases your investment significantly.

To lower your overall cost, look for financial aid opportunities. Start by filling out the FAFSA ®, which determines your eligibility for federal grants. You can also look for merit- and need-based scholarships from colleges, nonprofit groups and government agencies. Finally, you may consider taking out a loan, but this option requires repayment with interest.

Which certificate is good for data analysts?

The best data analyst certificate depends on your career goals and current skills. In general, look for programs from accredited colleges that cover foundational skills, including data analysis techniques, Python programming, data visualization and machine learning.

Is a data analytics certificate worth it?

Yes, data analytics certificates can help you advance in your current role or start a new career. Academic certificates range from $4,600 to $10,000 in total tuition, which could eventually pay off in a high-paying, in-demand career.

Can you be a data analyst with just a certificate?

You typically need a bachelor’s degree to work as a data analyst. However, a certificate may help you land an entry-level job or move into a data-oriented role from another field.

Which course is best for data analytics?

Data analytics courses should cover programming languages like Python and R, software like Excel and Tableau, data analysis theory and practice, big data, machine learning and predictive analytics.

Kimberlee Leonard has 22 years of experience as a freelance writer. Her work has been featured on US News and World Report, Business.com and Fit Small Business. She brings practical experience as a business owner and insurance agent to her role as a small business writer.

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Comprehensive Course:

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Key Highlights:

• 3 Live Classes Weekly
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Session 1: introduction to excel for data analysis.

• Overview of Excel interface
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Session 4:Retrieving Data with SELECT Statements

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Data Analysis Courses

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Case Studies in Functional Genomics

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

Introduction to Bioconductor

The structure, annotation, normalization, and interpretation of genome scale assays.

Advanced Bioconductor

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

High-Dimensional Data Analysis

A focus on several techniques that are widely used in the analysis of high-dimensional data.

Statistical Inference and Modeling for High-throughput Experiments

A focus on the techniques commonly used to perform statistical inference on high throughput data.

Introduction to Linear Models and Matrix Algebra

Learn to use R programming to apply linear models to analyze data in life sciences.

Statistics and R

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

Quantitative Methods for Biology

Learn introductory programming and data analysis in MATLAB, with applications to biology and medicine.

Principles, Statistical and Computational Tools for Reproducible Data Science

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.

Machine Learning and AI with Python

Learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.

Data Science: R Basics

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

Data Science: Visualization

Learn basic data visualization principles and how to apply them using ggplot2.

Data Science: Probability

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

Data Science: Inference and Modeling

Learn inference and modeling: two of the most widely used statistical tools in data analysis.

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Data Analyst

Ranked #1 Data Analytics Course by Career Karma

• Earn a recognized Data Analyst certification to boost your career
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• Get hands-on experience with the latest tools and work on real-world projects
• Earn IBM certificates and benefit from Masterclasses by IBM experts

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• Next cohort Starts 6 th Jul, 2024
• Learning Format Online Bootcamp
• Course duration 11 Months

Why Join this Program

Access hackathons, masterclasses, and AMA sessions. Earn IBM certificates for IBM courses.

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Learn tools like Tableau, Excel, SQL, Python, R, PowerBI and more

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Data Analyst Program Overview

This data analyst course, in collaboration with IBM, will transform you into a data analytics expert. In this course, you will learn the latest analytics tools and techniques, how to work with SQL, the languages of R and Python, the art of creating data visualizations, and how to apply statistics and predictive analytics in a business environment.

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Data Analyst Program Advantage

Get certified in data analysis with this IBM program. Access masterclasses by experts, and AMAs with leadership. Earn Data Analyst and IBM certificates plus complete capstone projects. Advance your career now!

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About Data Analyst Program

Why become a data analyst.

Becoming a Data Analyst allows you to play a crucial role in decision-making processes by interpreting complex data to uncover trends and insights. This in-demand career offers opportunities across various industries, promises excellent growth potential, and provides the satisfaction of directly impacting business strategies and outcomes.

Simplilearn’s Data Analyst course, delivered in collaboration with IBM, will provide you with extensive expertise in the booming field of data analytics. This Data Analyst training will enable you to master descriptive and inferential statistics, hypothesis testing, regression analysis, data blending, data extraction, and forecasting. Through this Data Analyst course, you will gain expertise in data visualization techniques using Tableau and Power BI and learn how to organize data and design dashboards.

Read more about  how to become a data analyst here .

What are the Data Analyst course learning objectives?

This Data Analyst course, in collaboration with IBM, will transform you into a data analytics expert. In this Data Analyst certification course, you will learn the latest analytics tools and techniques, how to work with SQL databases, the languages of R and Python, the art of creating data visualizations, and how to apply statistics and predictive analytics in a business environment.

What are the job opportunities upon completing this Data Analyst certification course?

Upon completing the Data Analyst certification course, you will have the data analytics skills necessary to get your dream job in the data analytics space. Apart from Data Analyst, other jobs titles include:

• Data Analytics Manager/Lead
• Business Analyst/Senior Business Analyst
• Business Intelligence Analyst
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Data Analyst Program Learning Path

Accelerate your career trajectory with our extensive data analyst course curriculum. Delve into foundational statistics, master data analysis with Python and R, navigate databases using SQL, and harness the power of visualization with Tableau and Power BI.

Learning Path

Boost your Data analytics career with powerful new Microsoft® Excel skills by taking this Data Analytics course, which includes training on Business Analytics. This combined with an official certificate will put you on the path to a successful career.

Classroom Schedule

• Ideal for aspiring SQL developers and data analysts
• Perfect for enhancing database management skills
• Beginner-friendly SQL course designed by Simplilearn
• Covers basics to advanced topics of SQL
• Learn data storage, retrieval, and manipulation using SQL

Learn how to perform Data Analytics with Python using multi-dimensional arrays in NumPy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and perform machine learning using scikit-learn. These Data Analytics courses will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more.

Discover R Programming with this introductory course. Learn how to write R code, utilize R data structures, and create your own functions.

The next step to becoming a Data Analyst is learning R—the most in-demand open-source technology. R is a powerful Data Science and analytics language, which has a steep learning curve and a very vibrant community. This is why it is quickly becoming the technology of choice for organizations that are adopting the power of analytics for competitive advantage.

• Aligned to Tableau Desktop Specialist Certification
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Simplilearn’s Data Analyst Capstone project will give you an opportunity to implement the skills you learned in the Data Analyst course. With dedicated mentoring sessions, you’ll know how to solve a real industry-aligned problem. The project is the final step in the learning path and will help you to showcase your expertise to employers

• Aligned with PL-300: Microsoft Power BI Data Analyst certification
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• Explore comprehensive Power BI training for hands-on applied learning
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Attend this interactive, online industry master class to gain insights about cutting edge Data Analytics advancements and techniques.

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8+ skills covered.

• Data Analytics
• Statistical Analysis using Excel
• Data Analysis using Python and R
• Data Visualization Tableau and Power BI
• Linear and logistic regression modules
• Clustering using KMeans
• Supervised Learning
• Unsupervised Learning

9+ Tools Covered

Industry Projects

App rating recommendations.

The Google Play Store team launches visibility booster for promising apps - displayed higher in recommendations. Build model to predict app ratings with provided app data.

COMCAST TELECOM CUSTOMER COMPLAINTS

Comcast promises better customer service but continues falling short, despite repeated vows to improve. Leverage database of customer complaints to finally boost satisfaction.

ECOMMERCE SALES DASHBOARD

E-commerce company seeks sales dashboard to analyze product categories, ease selection, and boost purchases. Help users review info on products they're considering.

COMPARATIVE STUDY OF COUNTRIES

Create a dashboard to compare different countries on various parameters using the sample insurance data set and world development indicators data set.

SALES PERFORMANCE ANALYSIS

Build dashboard showing monthly sales by segment and category, flagging those meeting/exceeding/missing targets - enabling clients to easily identify areas needing attention.

ANALYSIS OF SALES REPORT OF A CLOTHES MANUFACTURING OUTLET

Fashion retailer seeks to expand product line. Aims to grasp market trends, automate product attributes, predict sales, understand sales drivers, and standardize ratings with data.

COLLEGE ADMISSION

Each year, US colleges receive thousands of applications from international students. To streamline the process, the education department must analyze factors affecting admission.

IDENTIFYING AND RECOMMENDING THE BEST RESTAURANTS

Upgrading B2C portal with smart automation. Forming matrices for top-notch eateries and suggestions. Analysis-based reports and visual dashboards for sharing insights.

PREDICTING LOAN DEFAULTERS

High vehicle loan defaults cause losses, leading to stricter underwriting. Need a better risk model. Study to identify default factors and predict defaults.

EXAMINING FACTORS RESPONSIBLE FOR HEART ATTACKS

Global heart disease deaths are high. We need a system to predict heart attacks. Study data on cardiovascular health, identify key factors and create a predictive model.

Disclaimer - The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

Join the Data Analytics industry

Data Science and Analytics jobs are projected to see growth by 31% in the upcoming decade. The data analytics market is estimated to reach $24.63 Bn in 2021 and is projected to grow at a compound annual growth rate of 25 per cent from 2021 to 2030.

Expected New Jobs for Data Science and Analytics

Annual Job Growth by 2030

Average Annual Salary

Companies hiring Data Analysts

Batch Profile

This program caters to working professionals from a variety of industries and backgrounds; the diversity of our students adds richness to class discussions and interactions.

Learner Reviews

The admission fee for this program is € 1,299

Pay in Installments

You can pay monthly installments for Programs using Splitit payment option with 0% interest and no hidden fee.

Financing Options

We provide the following options for one-time payment

• Credit Card

Data Analyst Program FAQs

What does a data analyst do.

Data analysts play a unique role among the many data-centric jobs often found in today's businesses. A Data Analyst commonly works on collecting and interpreting data, analyzing the outcome, and reporting the results back to the relevant team. A Data Analyst also works closely with identifying patterns and trends in data sets, working alongside organizations within the business or the management team to establish business needs, define new data collection and analysis processes, and add real value to a company.

What are the prerequisites for pursuing this Data Analyst course?

Professionals who would like to successfully complete this Data Analyst course should have basic mathematical knowledge and problem-solving skills.

What is the average salary of a Data Analyst?

Although data analysts are well-paid worldwide and exponential demand for data analysts will never decline, data analysts' salaries vary in different sectors and nations. Data analysts' salaries depend on their skills, experience, company & location. Our Data Analyst course helps you achieve a higher salary, expertise, and skills to succeed in this lucrative growing profession. The average annual salary in top countries is:

• India: Rs. 509417
• US (New York): $105,001
• New Zealand: $70,000
• Canada: $61,766

What is the job market for data analysts?

Data analysis is a rapidly growing field, and the demand for data analysts is increasing in many industries. Here are a few placement trends for data analysts:

• Increasing demand: The amount of data being generated is increasing at a rapid rate, and organizations are looking for ways to extract insights from that data. As a result, the demand for data analysts is increasing in finance, healthcare, retail, and manufacturing industries, to name a few.
• The growing use of data analytics in different sectors: Data analysis is becoming increasingly important in various sectors, such as healthcare, finance, manufacturing, retail, and technology. The increasing use of data analytics in various sectors is creating new opportunities for data analysts.
• Remote work: With the COVID-19 pandemic, remote work has become more prevalent and many companies are now looking for data analysts who can work remotely.
• Use of machine learning and artificial intelligence: Data analysts are increasingly expected to use machine learning and AI to extract insights from data. This is creating new opportunities for data analysts with experience in these technologies.
• Interdisciplinary skills: Data analysts are increasingly expected to have interdisciplinary skills, such as data visualization and business acumen.

What industries use Data Analysis most?

Some of the top industries using data analysis include data assurance, retail, finance, entertainment, government and public sector, higher education, sharing economy services, sales & marketing, agriculture, business intelligence, healthcare, and data quality.

Out of all the ways to learn data and data visualization, one of the most efficient ways is to understand what is data analytics. To pursue a career as a data analyst, one must be fluent in data analysis and analysis data visualization.

Who will teach this Data Analyst course?

All of our highly qualified Data Analyst course trainers are business intelligence industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

How do I earn the Master's certificate?

Upon completion of 80% of LVC hours and 2 projects, you will be eligible to receive the certificate that will testify to your skills as an expert in Data Analyst.

Please find the completion criteria for each course within the mandatory learning path as:

Can a fresh graduate seek employment after completing this Data Analyst course?

Because the profession is vital to business, most companies require a data analyst, and new graduates have excellent job opportunities. If you aim to become more competitive, then it is worth earning the Data Analyst course. Explore your educational opportunities and start creating a solid basis for data analyst skills.

If I need to cancel my enrollment from this data analyst course, can I get a refund?

Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, please read our  Refund Policy .

I am not able to access the Data Analyst course. Who can help me?

Contact us using the form on the right of any page on the Simplilearn website, select the Live Chat link or Request a callback.

Find Data Analyst Master's Program in other cities

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Data scientist.

14 tools & Rigorous curriculum

Master's certificate

In Collaboration With IBM

• PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

Master of Science in Data Science

Master of science in data science, the u of a msds empowers students with the in-demand skills they need to transform data into actionable insights..

Find out how the MSDS is the right program for you:

This AI-generated image showcases what's possible. At the College of Information Science, you can learn how to analyze, manage and lead our transition into an AI-fueled future.

Fortune Best Master's in Data Science Programs

Academic Credits Required

Next Application Deadline

Average Salary*

The University of Arizona Master of Science in Data Science (MSDS) prepares students for robust careers in one of the world's fastest-growing professions.

The top-ranked , STEM-designated MSDS is offered on campus and online, and can be completed in as few as 18 months.

Students take core courses in data mining and discovery, data analysis and visualization, and data ethics while choosing from a number of dynamic electives , including neural networks, artificial intelligence, natural language processing, machine learning, cyberinfrastructure, data warehousing, database development, data science and public interests, and advanced computational linguistics. With the MSDS, you'll graduate with the skills you need to excel in tomorrow's dynamic, data-driven economy .

* Average salary for data science master's graduates according to Lightcast, November 2023.

"Though I've spent many years in the tech industry, I recognized the need to delve into data science to advance my career. I chose the U of A Master of Science in Data Science because of the flexibility of curriculum." Ankit Pal, MS in Data Science '23

APPLICATION DEADLINES

• MAIN CAMPUS Fall Semester: February 1
• ONLINE CAMPUS Fall Semester: March 15
• MAIN CAMPUS Spring Semester: August 1
• ONLINE CAMPUS SPRING Semester: October 1

Applications are currently open for Fall 2024 (online campus only) and Spring 2025 (all campuses).

PROGRAM COST

• IN-STATE, ON-CAMPUS TUITION & FEES: $7,544.48 per semester
• OUT-OF-STATE & INTERNATIONAL, ON-CAMPUS TUITION & FEES: $17,430.48 per semester
• ONLINE CAMPUS TUITION & FEES: $6,708 for 9 units

Tuition, fees and other costs subject to change.

CURRICULUM & COURSES

The MSDS, offered both on campus and online, requires 30 units and can typically be completed in 18 months for full-time students.

An internship or capstone project are required.

Students take core courses in data mining and recovery, data analysis and visualization, and data ethics (or ethical issues in information), then choose from a wide array of electives.

CAREER OUTCOMES

In the U of A MSDS, students are prepared for a wide variety of in-demand jobs across industries. Positions include:

• Data analyst, architect, engineer, modeler, scientist or visualization designer
• Artificial intelligence engineer
• Big data engineer
• Business intelligence analyst or developer
• Language engineer
• Machine learning engineer
• Quality specialist
• Market research analyst
• Statistician

MSDS Lightning Talks

Learn how MSDS students incorporate research, collaboration, courses and internship experience to advance their skills and job readiness.  

View Recent MSDS Lightning Talks

Are you ready to transform your future in data science?

Learn more about the Master of Science in Data Science by contacting us at [email protected] or begin your application today:

Start Your Application

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Intro to Data Analytics 2024

pittcist/summer2024

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Course syllabus - intro to data analytics in r and python.

Summer 2024 (July 8-11, 2024, 08:00-12:00, Beijing Standard Time )

Course Description

Welcome to the Intro to Data Analytics course in R and Python, Summer 2024. This course will be taught online via Zoom Meeting from July 8-11, 2024, 8:00-12:00 BJT .

This course is designed for upper-level undergraduate students and graduate students in business programs with a strong interest in data analytics. This course will introduce concepts, tools, and methods related to data collection, cleaning, processing, analysis, and visualization for business operations. Students in this class will benefit from the hands-on teaching style and real-world cases and examples.

Topics of this course include: markdown (a lightweight markup language), the software package R, R statistical methods, Python programming language, Python web handling, regular expression and other data analytics-related tools and topics. This course is intended for students who plan to seek data analytics-related jobs or apply for graduate schools in information systems and/or data sciences. Students who wish to improve academic research skills may also benefit from this course.

This course will be taught synchronously on Zoom each day in the morining. Please use the following Zoom link and passcode to join the class sessions.

Per university policies, the primary instructional language of this course is English .

This course requires intensive English reading and writing. All course materials are given in English. Students are expected to complete writing assignments in English. Students are encouraged to participate in class discussions in English.

Canvas Enrollment

The course LMS (learning management system) is Canvas by Instructure. All course materials, including assignment and grade management will be hosted on Canvas.

All students should self-enroll on Canvas prior to July 8. The enrollment link is: https://canvas.instructure.com/enroll/MA9AJE

Prerequisite

Although students are not required to have a computer programming background, at least one intro-level programming course and one college-level statistics course prior to taking this course will be very helpful.

This is an intensive coding class . We'll spend the majority of time on hands-on coding practices. All students MUST bring a personal computer. If you are running Zoom on a tablet, a separate personal computer may be needed for coding.

Running Zoom on a mobile phone is NOT recommended because the small screen makes it difficult to view the code clearly.

Please follow the enclosed instructions to install the required software on your computer, including:

• Visual Studio Code
• PyCharm (optional)

Please have the above software packages installed on your computer before our first day of class.

Course Materials

There is no required textbook in this course. You may find the following reference books helpful for your learning and exercises:

R in Action, Data analysis and graphics with R , by Robert I. Kabacoff, ISBN: 978-1-935-18239-9

Learning Python , by Mark Lutz, 4th Edition, ISBN: 978-0-596-15806-4

Topics Covered

• Git and GitHub
• Regular expressions

Installation of R and R Studio

R Studio environment

• R Notebook and R Markdown
• Setting up work directory

R Data Structures

• Vectors and calculation
• Graphics output
• Sequence and repetition
• Missing values
• Random numbers
• Attaching a data set
• cbind() and rbind()
• Monte Carlo simulation
• File operations

Tidyverse - dplyr and tidyr

• dplyr and tidyr

Data Visualization

Statistics in R

• Installation of Python and PyCharm
• Python basic syntax
• Web Handling with BeautifulSoup (bs4)
• Image processing with Pillow
• Face detection

Course Conduct and Policies

Attendance policy.

Attendance is required, and attendance policy will be strictly enforced in compliance with the university policies.

Homework/Assignment Policy

Completed homework/assignment must be turned in by the due time as specified in the assignment requirements. No late homework/assignment will be accepted.

Make-up Policy

There will be no make-up assignment except in the case of a documented medical emergency.

Students enrolled in this course will receive a grade of either S (Satisfactory) or F (Fail). To earn an S, students must accumulate over 60% of the available credits. Those who do not meet this threshold will receive an F.

Professionalism

Students who are uncooperative, rude, and abusive to the instructor or other students will be penalized. Students who habitually show up late, or leave early without appropriate permission, bring food or drink into classrooms, read papers or magazines irrelevant to class instructions, listen to music, or use cell phones during class, use computers in class for anything other than approved class-related activities, or are disruptive in any other way, will be penalized.

Academic Integrity

Students are expected to uphold the standard of conduct relating to academic integrity. An individual student is assumed full responsibility for the content and integrity of the academic work he or she submits. The guiding principle of academic integrity shall be that a student’s submitted work, examinations, reports, and projects must be his or her own work.

All sources must be cited with full references. Copying ideas, code, and writings from another person and presenting them as if one’s own is plagiarism . Any form of cheating and plagiarism uncovered will result in an immediate F for that assignment and may lead to a discussion of the situation with the disciplinary officials assigned by the university.

Disability Policy

Students with a disability may request an accommodation as early as possible in the term. The instructor will accept verified disability and determine reasonable accommodations for this course.

• Python 17.6%