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17 Data Scientist Resume Examples for 2024

Stephen Greet

  • Data Scientist Resume
  • Data Scientist Resumes by Experience
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Writing Your Data Scientist Resume

We’ve reviewed countless data scientist resumes and have made a concerted effort to distill what works and what doesn’t about each of them.

Our number one tip to create an effective data science resume is to quantify your impact on the business ! These 17 data scientist resume samples below and our  data scientist cover letter templates  can help you build a great job application in 2024, no matter your career stage.

Whether you’re looking for your first job as an entry-level data scientist or are a veteran with 10+ years of expertise, you’ll find plenty of tools to  build your perfect resume , like our new  Word resume examples  or  free Google Docs resume templates .

Data Scientist Resume Example

or download as PDF

Data scientist resume example with 8 years of experience

Why this resume works

  • You need to  write your resume  in a way that  shows the employer that you’ve materially impacted the companies you’ve worked for.
  • This means you should quantify your value in terms of business impact, not model performance. Model performance metrics without context really don’t convey much.
  • They’re a way to quickly display your achievements and convince the employer that you’ll bring that same kind of energy to their team or company.

Entry-Level Data Scientist Resume

Entry-level data scientist resume example

  • Considering adding projects to your  entry-level data scientist resume  in lieu of enough work experience?
  • You can demo the punch of a project by framing a question and then answering that question with data.
  • Again, your results should be consistently expressed in numbers. Even if the result is as silly as saving 12 minutes per movie, it recognizes the importance of measuring impact.
  • Customizing looks like: mentioning the target business by name and including relevant keywords from the  job description . 

Associate Data Scientist Resume

Associate data scientist resume example

  • When you have little to no professional background,  the skills you list on your resume  matter more than ever. And your abilities aren’t just selling points—they’re also a springboard for you to demonstrate your willingness to learn. 
  • While writing your associate data scientist resume objective, immediately dive into any education or internship highlights with notable companies like Northrop Grumman. Then, sprinkle in some personality that shows your enthusiasm for new knowledge—drive and inquisitiveness are highly desirable traits in new professionals.

Senior Data Scientist Resume

Senior data scientist resume example with 10+ years of experience

  • Your  senior data scientist resume  can really wow when you show a clear career progression from data analyst to data scientist to senior data scientist.
  • That said, if you’ve got at least four years of experience under your belt, it’s fine for your work experience to account for about 70 percent of the page.
  • A worthwhile summary should give a quick snapshot of your career highlights in two to three power-packed sentences and include the target company by name.

Data Scientist Intern Resume

Data science intern resume example with 1+ years of experience in retail

  • Call attention to your expertise in computer science by listing your proficiency in advanced programs like Keras on your data scientist intern resume.

Data Visualization Resume

Data visualization resume example with 6 years of experience

  • Whether it’s geospatial analysis, real-time data monitoring, or even creating standard visuals, make sure to quantify the impact of each and clearly state the benefit these tasks brought to the company to strengthen your data visualization resume.

Healthcare Data Scientist Resume

Healthcare data scientist resume example with 6 years of experience

  • Having two qualifications! Now’s the time to show all the degrees you’ve got! The best-case scenario is to have two degrees where one caters to the healthcare field while the other highlights your expertise in data science!

Amazon Data Science Resume

Amazon data science resume example with 10+ years of experience

  • Let that statement capture your aspirations and what you desire to bring to your new employer. Hiring managers are eager to see your passionate side and value to the team.

Python Data Scientist Resume

Python data scientist resume example with 10+ years of experience

  • Mentioning achievements such as improving project outcomes and reduction in process duration in your Python data scientist resume is a great way to leverage your experience honed over years of hard work.

Data Scientist Machine Learning Resume

Data scientist machine learning resume example with 10 years of experience

  • Even if you already have ample experience in your field, you can give your data scientist machine learning resume a competitive edge by bringing your higher education to light. Create space to showcase your advanced degree in a relevant subject like statistics to further stand out.

Data Science Manager Resume

Data science manager resume example with 10+ years of experience

  • Again, the results of your work should be stated clearly in terms of tangible impact (are you sensing a theme?). 
  • Using a two-column layout for your  data science manager resume  allows more information to fit on a single page. Even with nine-plus years of experience, keeping your resume to one page is ideal.
  • Fretting these details? Our  resume templates for 2024  may suit your specific needs; additionally, we’ve got 10 fresh and  free Google Docs resume templates  that can make your  resume-building  blues go away!.

NLP Data Scientist Resume

Nlp data scientist resume example with 7 years of experience

  • When you’re trying to figure out  what to put on your resume  for a more specialized role like an NLP data scientist, it’s important you showcase your proficiency in operationalizing models to have a big impact on the business.
  • Don’t focus on the technical aspects of the models you’ve built on your  NLP data scientist resume  (you’ll talk more about that in your interviews). Instead, take a step back and talk about the broad impact you’ve had in your previous roles.

Metadata Scientist Resume

Metadata scientist resume example with 2+ years of experience

  • Prove your experience in programming, testing, modeling, and data visualization through well-designed projects that solve real problems through code.
  • The key isn’t to reinvent the wheel but to create something dynamic and unique that isn’t easily replicated with a few Google searches and a video tutorial.
  • Solve this problem with projects. If you’ve worked on excellent projects that used and showcased the necessary skills required for the job, list them and watch your resume bloom with confidence!

Educational Data Scientist Resume

Educational data scientist resume example with 10+ years of experience

  • Think “well-rounded” as you write; you might include an exciting publication related to the job role, quickly outline your relevant experience or abilities, and conclude with how and why you’ll better the company through your new role. 
  • Skills and certifications add credibility, but potential employers also want to know about your impact.
  • If you performed evaluations, what improvements did you make afterward? If you integrated machine learning, what optimizations did you use it for?

Data Analytics Scientist Resume

Data analytics scientist resume example with 5 years of experience

  • Your data scientist, analytics resume should target the list of requirements that companies in your state commonly request.
  • For example, 18 out of 20  job descriptions  for data science, analytics in the state of California list Python, SQL, R, Tableau, and Hadoop (in that order) as required skills.
  • After you add job-market-specific data, our  free resume checker  can assess your resume for other key elements like spelling, grammar, and active language. 

Data Science Consultant Resume

Data analytics consultant resume example with 9 years of experience

  • To best represent your capabilities, use metrics to talk about your accomplishments.

Data Science Director Resume

Data science director resume example with 5 years of experience

  • For an effective data science director resume, use a clean and simple resume template and format your work experience in reverse-chronological order. Doing so will put your most recent and relevant accomplishments at the top, making it the first thing a recruiter will look at.

Related resume guides

  • Data Analyst
  • Data Engineer
  • Computer Science

Three peers review job application materials on laptop and tablet

Recruiters only spend an  average of seven-plus seconds reviewing your resume , so it’s vitally important that you catch their attention in that time. Our guide for 2024 takes you section by section through your resume to ensure you get that first interview.

You can successfully choose a winning  resume format in 2024  that will snag an employer’s attention.

Short on time? Here are the quick-hit summaries of each section you can apply to your resume:

  • Whether for a company or yourself, what you’ve worked on should be the focus of your resume. Always try to include a measurable impact of your work.
  • Make this the job title you’re looking for (e.g., “data scientist”), and don’t worry about a summary unless you’re making a career change.
  • Only include technical skills that you’d be comfortable having to code with/in during an interview. Avoid a laundry list of different skills.
  • Include relevant courses if you’re looking for an entry-level role. Otherwise, make your work the focus of your resume. If you went to a boot camp, list it here.
  • Double-check everything. This is not the place you want to make a mistake. You don’t need to put your exact address. City, state, and zip are fine.
  • Try to keep it to one page. Keep your bullets brief. Triple-check your grammar and spelling, and then have someone else read it.
  • Read the  data scientist job description . See if any projects you’ve worked on come to mind while reading it. Incorporate those specific projects into your resume.

data science project on resume

Your data science projects and work experience

Let’s jump right into the good stuff and talk about the most important part of your resume: your work experience and projects. This is it. This is the grand finale. This is where the person reviewing your resume decides whether or not you’ll get an interview.

When talking about your previous work (whether that’s for another employer or on a side project), your goal is to convince the person reviewing your resume that you’ll provide value to their company. This is not the place to be humble. We want to see that “I’m wearing my favorite outfit” level of confidence.

The template for successfully talking about your experience as a data scientist is:

  • Clearly state the goal of the project
  • You can mention the programming languages you used, the libraries, modeling techniques, data sources, etc.
  • State the quantitative results of your project

You’re a data scientist, so highlight your value by demonstrating the quantitative impact of your work.  These can be estimates . For example, did you automate a report? Roughly how many hours of manual work did you save each month? Here are some ideas for how you can quantitatively talk about your projects:

Ways to define the impact of your data science work

  • Example:  You developed a pricing algorithm that resulted in a $200k lift in annual revenue.
  • Example:  You built a model to predict who would cancel their subscription and introduced an intervention to improve monthly retention from 90% to 93%.
  • Example:  You built a marketing attribution model that helped the company focus on marketing channels that were working, resulting in 2,100 more users.
  • Example:  You ran an experiment across different product features, which resulted in a 25% increase in engagement rate.
  • Example:  As a side project, you built a movie recommendation engine that now saves you 26 minutes each time you need to decide which movie to watch.
  • Example:  Since you built a customer segmentation model to determine how to communicate with different customer types, customer satisfaction is up 17%.

Numbers draw attention, are convincing, and make your resume more readable. Which of these two ways to describe reporting is more compelling?

  • Used Python, SQL, and Tableau to conduct daily reporting for the business
  • Using Python, SQL, and Tableau, combined 11 data sources into a comprehensive, real-time report that saved 10 hours of work weekly

If nothing else, please take this away from this guide:  state the results of your projects on your resume in numbers.

data science project on resume

Trade-offs between projects and work experience

Simply put, the more work experience you have, the less space “projects” should take up as a section on your resume. In the sample resumes above, you’ll notice that only the more entry-level data scientist resumes have a section for projects.

The senior-level resumes focus on projects in the context of experience within companies. Real estate is precious on a one-page resume, so you’ll want to focus on the bullets that most clearly demonstrate how you’re a great fit for the job. Companies want to hire data scientists who have demonstrated success at other companies.

data science project on resume

Entry-level data science projects for resume

Junior data scientists should include projects on their resumes. Try starting with a  resume outline , where you can brain dump anything and everything about your projects; then, you can distill the best of it into your final resume. Can you share the Github link? Do you have a link to a write-up you did about your project?

The more initiative you can show for entry-level data science projects, the better. Do you have any questions to which you’ve always wanted the answer? You can probably think of some clever ways to get data around that question and come up with a reasonable answer. For example, our co-founder wanted to know  which data science job boards were best , so he pulled together some data, laid out his assumptions and methodology, and made his conclusions.

Sample Data Science Projects

No matter what projects you include on your resume, be sure to clearly state the question you were answering, the tools and technologies you used, the data you used to answer the question, and the quantitative outcome of the project. Succinctly stating conclusions and recommendations from your analysis is a highly sought-after skill by employers in data science.

data science project on resume

The data scientist summary

Since you have limited space on your resume, you should only include a  resume objective  if you take the time to customize it for each role to which you apply.

You may want to include a  resume summary  or objective when you’re making a big career change. If you do include one, make sure to keep it specific about your goal and experience. This is valuable space you’re going to be using on this statement, so take the time to personalize it to each job.

Include the title of the job you’re looking for under your name. This should be aspirational. So if you’re a data analyst looking to apply for data scientist jobs, you would put “data scientist” under your name as the headline:

Sample Data Science Resume Headlines.

Skills that pay the bills

The most common mistake we see on data science resumes (that we used to make on our resumes) is what we call skill vomit. It’s a laundry list of skills in which no one person could have expertise. A quick rule of thumb:  if the skills section takes up a third of the page, it takes too much space. This is a big red flag for hiring managers.

The reason people make such an exhaustive skills section is to get through the mythical data science resume keyword filters. If you’re changing your resume in small ways for each job you apply to (for example, put Python for jobs that mention Python and R for jobs that list R if you know both), you’ll have no problem with those keyword filters.

The rule of thumb that we recommend you use in determining whether to include a skill on your resume is this:  i f it’s on your resume, you should be comfortable coding with/in it during an interview.

So that means if you’ve read a few articles on Spark or adversarial learning, but you can’t use them in code, they should not be on your resume. If you only have a handful of tools under your toolbelt, but you can use them effectively to answer questions with data, you’ll be able to find jobs looking for that skill set. 

We can assure you there are all kinds of data science jobs available. Our scraper that indexes jobs across thousands of company websites shows over 5,000+ full-time data science job openings in the US across all tenures and skill sets. And our scraper has a lot of room for improvement, so that’s significantly lower than the actual number. 

There are tons of fish in the job market sea; you just need a fishing rod.

data science project on resume

Entry-level vs. senior skills sections

Generally, the more senior you are, the shorter your skills section needs to be. If you’re a senior data scientist, you should talk about the major tools and languages you use but save specific modeling techniques for the “Work Experience” section. Show how you used particular models in the context of your work.

When you’re more junior, you likely haven’t had the chance to use all of the techniques you’re comfortable with within work or a project. That’s okay! It’s expected. But you still want to make it clear to a potential employer that you can use those methods or libraries.

Example Data Science Skills Section.

Education is a lot like skills in that the more senior you are as a data scientist, the less space the education section should take up on your resume. When you’re looking for one of your first data science jobs, you might want to include courses relative to data science to demonstrate you have a strong foundation.

Classes in subjects like linear algebra, calculus, probability, and statistics and any programming classes are directly relevant to being a data scientist. If you’re looking for your first job out of college, you should include your GPA on your resume. When you have a few years of work experience, it’s not necessary to include it.

If you just finished (or are finishing) a data science boot camp, this is the place to list where you went. You can include the relevant lessons or classes you took. Be sure to have a few projects from your boot camp (especially if it was an original project) in your resume’s “Projects” section.

Sample Data Science Education Section.

Contact information

The takeaway from this section is simple:  this is not where you should make a mistake . Storytime! When our co-founder was first applying to jobs out of college, he realized about 20 applications in, he had spelled his name “Stepen” instead of “Stephen.” Don’t pull a Stepen.

Data suggests that when your email is wrong, your response rate from companies drops to zero percent. That’s just math. We’ve seen exactly four data science resumes where the email address on the resume was incorrect.

Make sure your email address is appropriate. While we don’t doubt the authenticity of your “ [email protected] ” email, maybe don’t use it when applying for jobs. To play it safe, stick to a combination of your name and numbers for your email.

This is the section you can include anything you want to show off for a data science role. Have a blog where you document the analysis you do for Dungeons & Dragons? Active on Github or an open-source project? Include a link to anything relevant to data that will help you stand out in your application.

data science project on resume

General resume formatting tips

This section is just a list of one-off styling and formatting tips for your data science resume:

  • Keep it brief. Bullets should be informative but should not drag on for paragraphs.
  • Each bullet point in your resume should be a complete thought. You don’t have to have periods at the end of each bullet.
  • Keep your tense consistent. If you’re referring to old projects in the past tense, do that for all old projects.
  • Please, please don’t get your contact information wrong.
  • Don’t give the person reviewing your resume a silly reason to put it in the “No” pile.  Check your resume  carefully.

data science project on resume

Customization for each application

You don’t have to go overboard with your resume customization. Here are the steps we recommend to customize it for each job:

  • So in this example, we’ll have one “Python” resume and one “R” resume depending on what the job is seeking.
  • For example, if you have experience with attribution modeling and this is a marketing data science role, you should include that experience.
  • Do you have experience with a certain library or modeling technique they mention? 
  • Do you have experience in the domain of the specific job?
  • Do you have any relevant industry experience with the company?

Let’s walk through a specific example to highlight what we mean by including particular projects for different jobs. Let’s say that a senior data scientist is applying for the position below.

Sample Data Science Job Description.

In the “Ideally, you’d have” section, they mention they want someone who has “Experience with ETL tools.” Let’s say that in reality, the candidate had a large role in building out data pipelines in his fictional role as a senior data scientist at EdTech Company.

So all we’d do is change that section of his experience at EdTech Company to talk about that project, as you see below:

Data science resume customization example

Original bullet on the resume: Worked closely with the product team to build a production recommendation engine in Python that improved the average length on the page for users and resulted in $325k in incremental annual revenue

Customized for the role: Built out our company’s ETL pipeline with Airflow, which scaled to handle millions of concurrent users with robust alerting/ monitoring

data science project on resume

Customization for startups

For early-stage startups (anything less than 50 employees), one of the most important qualities they’re looking for in a hire is ownership. That means they want someone who can ask a question and come up with an answer with minimal instruction. 

If you want to stand out to these companies, you should demonstrate ownership in the way you list projects on your resume. Include active words like “drove” or “built” instead of passive language like “worked on” or “collaborated on.” We know this seems nit-picky, but this matters to early-stage companies. Hiring managers at companies this size are strained for time and will use any signal to weed people out.

Concluding thoughts

There you have it—a compelling, easy-to-read data science resume built for 2024. Now you can celebrate by doing something as fun as  writing a resume . Maybe your taxes? Or go to the dentist?

By building or  updating your current resume , you took a huge step toward landing your next (or first) data science job. Now please, we beg you, check your grammar and spelling again and have someone else read your resume. Don’t let that be the reason you don’t get an interview.

Congrats! The first and hardest step is done. You have a data science resume! With great power comes great responsibility, so go and apply wisely.

Create my free resume now

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16 Data Science Projects with Source Code to Strengthen your Resume

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For the original article click here. 

Tried to build some data science projects to improve your resume and got intimidated by the size of the code and the number of concepts used? Does it feel too out of reach, and did it crush your dreams of becoming a data scientist? We have collected for you sixteen data science projects with source code so you can actually participate in the real-time projects of data science. These will help boost confidence and also tell the interviewer that you’re serious about data science.

Do you know?

Finding a perfect idea for your project is something that concerns you more than implementing the project itself, isn’t it? So keeping the same in mind, we have compiled a list of over 500+ project ideas just for you. All you have to do is bookmark this article and get started.

  • Python Projects
  • Python Django (Web Development) Projects
  • Python Game Development Projects
  • Python Artificial Intelligence Projects
  • Python Machine Learning Projects
  • Python Data Science Projects
  • Python Deep Learning Projects
  • Python Computer Vision Projects
  • Python Internet of Things Projects

In this blog, we will list out different data science project examples in the languages R and Python. Let’s separate these on the basis of difficulty so you have a proper path to follow.

Top Data Science Project Ideas

Here are the best data science project ideas with source code:

1. Beginner Data Science Projects

1.1 fake news detection.

Drive your career to new heights by working on Data Science Project for Beginners  –  Detecting Fake News with Python

A king of yellow journalism, fake news is false information and hoaxes spread through social media and other online media to achieve a political agenda. In this data science project idea, we will use Python to build a model that can accurately detect whether a piece of news is real or fake. We’ll build a TfidfVectorizer and use a PassiveAggressiveClassifier to classify news into “Real” and “Fake”. We’ll be using a dataset of shape 7796×4 and execute everything in Jupyter Lab.

Language:  Python

Dataset/Package:  news.csv

1.2 Road Lane Line Detection

Check the complete implementation of Lane Line Detection Data Science Project:  Real-time Lane Line Detection in Python

Data Science Project Idea:  The lines drawn on the roads guide human drivers where the lanes are. It also refers to the direction to steer the vehicle. This application is cardinal for developing driverless cars.

You can build an application having the ability to identify track lines from input images or continuous video frames.

1.3 Sentiment Analysis

Check the complete implementation of Data Science Project with Source Code –  Sentiment Analysis Project in R

Sentiment analysis is the act of analyzing words to determine sentiments and opinions that may be positive or negative in polarity. This is a type of classification where the classes may be binary (positive and negative) or multiple (happy, angry, sad, disgusted,..). We’ll implement this data science project in the language R and use the dataset by the ‘janeaustenR’ package. We will use general-purpose lexicons like AFINN, bing, and loughran, perform an inner join, and in the end, we’ll build a word cloud to display the result.

Language:  R

Dataset/Package:  janeaustenR

1.4 Detecting Parkinson’s Disease

Put your best foot forward by working on Data Science Project Idea –  Detecting Parkinson’s Disease with XGBoost

We have started using data science to improve healthcare and services – if we can predict a disease early, it has many advantages on the prognosis. So in this data science project idea, we will learn to detect Parkinson’s Disease with Python. This is a neurodegenerative, progressive disorder of the central nervous system that affects movement and causes tremors and stiffness. This affects dopamine-producing neurons in the brain and every year, it affects more than 1 million individuals in India.

Language:  Python

Dataset/Package:  UCI ML Parkinsons dataset

1.5 Color Detection with Python

Build an application to detect colors with Beginner Data Science Project –  Color Detection with OpenCV

How many times has it occurred to you that even after seeing, you don’t remember the name of the color? There can be 16 million colors based on the different RGB color values but we only remember a few. So in this project, we are going to build an interactive app that will detect the selected color from any image. To implement this we will need a labeled data of all the known colors then we will calculate which color resembles the most with the selected color value.

Dataset:  Codebrainz Color Names

1.6 Brain Tumor Detection with Data Science

Data Science Project Idea:  There are many famous deep learning projects on MRI scan dataset. One of them is Brain Tumor detection. You can use transfer learning on these MRI scans to get the required features for classification. Or you can train your own convolution neural network from scratch to detect brain tumors.

Dataset:  Brain MRI Image Dataset

1.7 Leaf Disease Detection

Data Science Project Idea:  Disease detection in plants plays a very important role in the field of agriculture. This Data Science project aims to provide an image-based automatic inspection interface. It involves the use of self designed image processing and deep learning techniques. It will categorize plant leaves as healthy or infected.

Dataset:  Leaf Dataset

2. Intermediate Data Science Projects

2.1 speech emotion recognition.

Explore the complete implementation of Data Science Project Example  –  Speech Emotion Recognition with Librosa

Let’s learn to use different libraries now. This data science project uses librosa to perform Speech Emotion Recognition. SER is the process of trying to recognize human emotion and affective states from speech. Since we use tone and pitch to express emotion through voice, SER is possible; but it is tough because emotions are subjective and annotating audio is challenging. We’ll use the mfcc, chroma, and mel features and use the RAVDESS dataset to recognize emotion on. We’ll build an MLPClassifier for the model.

Dataset/Package:  RAVDESS dataset

2.2 Gender and Age Detection with Data Science

Put the pedal to the metal & impress recruiters with ultimate Data Science Project –  Gender and Age Detection with OpenCV

This is an interesting data science project with Python. Using just one image, you’ll learn to predict the gender and age range of an individual. In this, we introduce you to Computer Vision and its principles. We’ll build a  Convolutional Neural Network   and use models trained by Tal Hassner and Gil Levi for the Adience dataset. We’ll use some  .pb, .pbtxt, .prototxt, and .caffemodel  files along the way.

Dataset/Package:  Adience

2.3 Diabetic Retinopathy

Data Science Project Idea:  Diabetic Retinopathy is a leading cause of blindness. You can develop an automatic method of diabetic retinopathy screening. You can train a neural network on retina images of affected and normal people. This project will classify whether the patient has retinopathy or not.

Dataset:  Diabetic Retinopathy Dataset

2.3 Uber Data Analysis in R

Check the complete implementation of Data Science Project with Source Code –  Uber Data Analysis Project in R

This is a data visualization project with ggplot2 where we’ll use R and its libraries and analyze various parameters like trips by the hours in a day and trips during months in a year. We’ll use the Uber Pickups in New York City dataset and create visualizations for different time-frames of the year. This tells us how time affects customer trips.

Dataset/Package:  Uber Pickups in New York City dataset

2.4  Driver Drowsiness detection in Python

Drive your career to new heights by working on Top Data Science Project  –  Drowsiness Detection System with OpenCV & Keras

Drowsy driving is extremely dangerous and around thousands of accidents happen each year due to drivers falling asleep while driving. In this Python project, we will build a system that can detect sleepy drivers and also alert them by beeping alarm.

This project is implemented using Keras and OpenCV. We will use OpenCV for face and eye detection and with Keras, we will classify the state of the eye (Open or Close) using Deep neural network techniques.

2.5 Chatbot Project in Python

Build a chatbot using Python & step up in your career –  Chatbot with NLTK & Keras

Chatbots are an essential part of the business. Many businesses has to offer services to their customers and it needs a lot of manpower, time and effort to handle customers. The chatbots can automate most of the customer interaction by answering some of the frequent questions that are asked by the customers. There are mainly two types of chatbots: Domain-specific and Open-domain chatbots. The domain-specific chatbot is often used to solve a particular problem. So you need to customize it smartly to work effectively in your domain. The Open-domain chatbots can be asked any type of question so it requires huge amounts of data to train.

Dataset:  Intents json file

2.6 Handwritten Digit Recognition Project

Practically implement the Deep Learning Project with Source Code –  Handwritten Digit Recognition with CNN

The MNIST dataset of handwritten digits is widespread among the data scientists and machine learning enthusiasts. It is an amazing project to get started with the data science and understand the processes involved in a project. The project is implemented using the Convolutional Neural Networks and then for real-time prediction we also build a nice graphical user interface to draw digits on a canvas and then the model will predict the digit.

Dataset:  MNIST

Get hired as a data scientist with  Top Data Science Interview Questions

3. Advanced Data Science Projects

3.1 image caption generator project in python.

This is an interesting data science project. Describing what’s in an image is an easy task for humans but for computers, an image is just a bunch of numbers that represent the color value of each pixel. So this is a difficult task for computers to understand what is in the image and then generating the description in Natural language like English is another difficult task. This project uses deep learning techniques where we implement a Convolutional neural network (CNN) with Recurrent Neural Network( LSTM) to build the image caption generator.

Dataset:  Flickr 8K

Framework:  Keras

3.2 Credit Card Fraud Detection Project

Put your best foot forward by working on Data Science Projects  –  Credit Card Fraud Detection with Machine Learning

By now, you’ve begun to understand the methods and concepts. Let’s move on to some advanced data science projects. In this project, we’ll use R with algorithms like  Decision Trees , Logistic Regression, Artificial Neural Networks, and Gradient Boosting Classifier. We’ll use the Card Transactions dataset to classify credit card transactions into fraudulent and genuine. We’ll fit the different models and plot performance curves for them.

Dataset/Package:  Card Transactions dataset

3.3 Movie Recommendation System

Explore the implementation of the Best Data Science Project with Source Code-  Movie Recommendation System Project in R

In this data science project, we’ll use R to perform a movie recommendation through machine learning. A recommendation system sends out suggestions to users through a filtering process based on other users’ preferences and browsing history. If A and B like Home Alone and B likes Mean Girls, it can be suggested to A – they might like it too. This keeps customers engaged with the platform.

Dataset/Package:  MovieLens dataset

3.4 Customer Segmentation

Put the medal to the pedal & impress recruiters with Data Science Project (Source Code included) –  Customer Segmentation with Machine Learning

This is one of the most popular projects in Data Science. Before running any campaign companies create different groups of customers.

Customer Segmentation is a popular application of unsupervised learning. Using clustering, companies identify segments of customers to target the potential user base. They divide customers into groups according to common characteristics like gender, age, interests, and spending habits so they can market to each group effectively. We’ll use  K-means clustering  and also visualize the gender and age distributions. Then, we’ll analyze their annual incomes and spending scores.

Dataset/Package:  Mall_Customers dataset

3.5 Breast Cancer Classification

Check the complete implementation of Data Science Project in Python –  Breast Cancer Classification with Deep Learning

Coming back to the medical contributions of data science, let’s learn to detect breast cancer with Python. We’ll use the IDC_regular dataset to detect the presence of Invasive Ductal Carcinoma, the most common form of breast cancer. It develops in a milk duct invading the fibrous or fatty breast tissue outside the duct. In this data science project idea, we’ll use  Deep Learning  and the Keras library for classification.

Dataset/Package:  IDC_regular

3.6 Traffic Signs Recognition

Achieve accuracy in self-driving cars technology with Data Science Project on  Traffic Signs Recognition using CNN  with Source Code 

Traffic signs and rules are very important that every driver must follow to avoid any accident. To follow the rule one must first understand how the traffic sign looks like. A human has to learn all the traffic signs before they are given the license to drive any vehicle. But now autonomous vehicles are rising and there will be no human drivers in the upcoming future. In the Traffic signs recognition project, you will learn how a program can identify the type of traffic sign by taking an image as input. The German Traffic signs recognition benchmark dataset (GTSRB) is used to build a Deep Neural Network to recognize the class a traffic sign belongs to. We also build a simple GUI to interact with the application.

Dataset:  GTSRB (German Traffic Sign Recognition Benchmark)

The source code of all these data science projects is available on DataFlair. Get started now and build a project in Data Science. Follow from beginner to advanced, and once you’re done, you can move on to other projects.

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What to include in the Projects section of your Data Science Resume

What to include in the Projects section of your Data Science Resume

I’ve done a few projects off my own back to try to be taken seriously. Which of these should I be putting on my resume?”

The Projects section will be more or less important for you depending on your circumstance. If you have some (or lots of) relevant work experience to showcase your skills / impact then listing a lot of independent projects is less critical. If however, you’re light on formal work experience, showcasing your proactivity - and technical proof-points - via Project work is hugely helpful (and frankly, a necessity!). You can also use this section to include other non-work proof-points of your expertise such as publications and/or presentations which demonstrate a reasonable (or better!) level of comfort with a given language, technique, tool etc.

So, with that said, what should you include?

Any Project (Presentation, Publication) which involved you using skills mentioned on the Job Posting - in the following priority order

  • One where you received an award / recognition
  • One where you generated the idea independently (i.e., you weren’t relying on Kaggle or equivalent for the idea). Importantly, within this category, projects where you had to work end-to-end data munging through to analysis/model-building to results/conclusions are better than when the data is ready to go (cleaned) from the outset as you can showcase a more holistic skill-set / approach
  • One which was part of your coursework and/or specified competition project

You can also detail Projects (Presentations, Publications) that don’t feature skills in the Job Posting if they demonstrate

  • A major accomplishment and/or
  • An ability to learn a new skill quickly (as this can help give comfort that even if you don’t know all the key skills the Hiring Manager is looking for that you have a track record of picking up new ones rapidly)

Note: If you have done a lot of Kaggle competitions and performed decently, you should put a link to your Kaggle profile at the end of the Projects section with a quick comment on the range of competitions / your performance, as this will be a further good proof-point of your competence level.

If you don’t have significant (or any) work experience, you’ll likely want to include 3 independent Projects outlined in detail (see the related post for how to describe the Project itself ). If you do have the Work Experience, then you’re best off focusing time (and resume space!) there, and only including your most impactful / interesting personal project in this Section.

How to take action now! List out the (non-work) Projects you’ve completed and for each jot down

  • The skills it helps demonstrate (languages, stats techniques, software packages etc.).
  • Whether it was independently generated idea or a competition (pre-defined) idea
  • Whether you worked it end-to-end, or just a partial process

With this, it will be easier to identify which projects most closely map to any given Job Posting, and hence you can apply the above filtering mechanism to identify what Projects to include on your resume for a given application :)

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7 Data Science Projects You Should Do to Make Your Resume Stand Out

Work on these projects and make yourself visible in today’s competitive job market.

Zita

Level Up Coding

Data science is a popular field that is in high demand.

As a data scientist, you should always look for ways to improve your skills and stay up-to-date on the latest techniques that can help you stand out from the crowd.

However, sometimes the most effective way to showcase your skills is by taking on a fun and unique project that pushes you out of your comfort zone.

Data science projects are a crucial part of the data science process, as they allow data scientists to apply their skills and knowledge to real-world problems and gain practical experience.

Here are seven ideas for data science projects that you can work on in your free time to enhance your resume and stand out to potential employers.

1. Clustering Project

Clustering is a popular technique used by data scientists to group data points that are similar to one another in some way.

With the aid of clustering techniques, objects from the data can be sorted into buckets or categories, making it simpler for people to browse through enormous datasets.

This can be useful for a variety of purposes, such as identifying patterns or trends within a dataset, or for making predictions about future data points based on their similarity to past ones.

The process of conducting the Clustering Project is:

  • Defining the problem
  • Selecting an appropriate algorithm
  • Pre-processing the data
  • Running the algorithm
  • Evaluating the results
  • Communicating the findings

For the process, it’s important to choose an appropriate algorithm, properly pre-process the data, and evaluate the results to get meaningful and accurate clusters.

Data scientists should also be able to effectively communicate their findings to stakeholders.

Examples of Clustering Projects

Listed below are examples of Clustering Projects that can be done by anyone learning the process.

1. Fraud Detection

Using a dataset of financial transactions, create clusters of similar transactions to identify any unusual or suspicious activity.

This can be useful for detecting and preventing fraud.

2. Text Classification

Using a dataset of documents, such as emails or articles, create clusters of similar documents based on the content of the text.

This can be useful for organizing and categorizing large volumes of text data.

3. Image Classification

Using a dataset of images, create clusters of similar images based on their visual characteristics.

This can be useful for organizing and categorizing large volumes of image data.

2. Recommender System Project

A recommender system is a subclass of information filtering systems that seeks to predict the “rating” or “preference” a user would give to an item based on their past behavior and the behavior of similar users.

For businesses to tailor their marketing strategies and boost client engagement, recommender systems are crucial to understanding as data scientists.

Recommender systems are utilized in a variety of areas but are most commonly recognized as playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms.

There are several approaches to building recommender systems, such as collaborative filtering and content-based filtering, and the success of the system depends on the quality and diversity of the data used to build it.

Data scientists working on a recommender system project must:

  • Collect and pre-process data
  • Build and evaluate a machine-learning model
  • Integrate the system into the target application or platform

Recommender systems can improve user engagement and satisfaction, as well as increase revenue for businesses.

Examples of Recommender System Project

The following are easy-to-do recommender system projects that can serve as a starting point for data scientists interested in exploring this field.

1. Recommending the perfect movie to watch on a rainy day

A data scientist could use a recommender system to suggest the perfect movie to watch on a rainy day based on the user’s preferences and the weather forecast.

2. Recommending the best type of pizza topping

A data scientist could use a recommender system to recommend the best type of pizza topping based on the user’s previous pizza orders and their stated preferences.

3. Recommending the perfect vacation destination

A data scientist could use a recommender system to suggest the perfect vacation destination based on the user’s budget, preferred climate, and activities of interest.

3. Regression Project

Regression is a statistical method used to study the relationship between a dependent variable and one or more independent variables. It can be used to determine how much one variable changes when other variable changes.

In a regression project, the goal is to build a model that can accurately predict the value of the dependent variable based on the values of the independent variables.

This model is typically a mathematical equation that represents the relationship between the variables.

A Regression Project consists of:

  • Identifying the appropriate independent variables
  • Collecting and cleaning data
  • Building and evaluating a mathematical model
  • Communicating the results

Regression is a commonly used technique in data science for understanding and predicting real-world phenomena.

Examples of Regression Project

By exploring these examples, we can gain a deeper understanding of how regression can be applied in different contexts to generate valuable insights and inform decision-making.

1. Predicting the success of a dating app match

A data scientist could use regression to predict the success of a match on a dating app based on independent variables such as the users’ ages, locations, and interests.

2. Forecasting the likelihood of a car running out of gas

A data scientist could use regression to forecast the likelihood of a car running out of gas based on independent variables such as the make and model of the car, the driver’s driving habits, and the distance between gas stations.

3. Predicting the success of a marketing campaign

In this project, a data scientist could use regression to predict the success of a marketing campaign based on independent variables such as the target audience, the marketing channels used, and the budget allocated for the campaign.

4. Classification Project

Classification is a supervised learning method in which a model is trained to predict the class or category to which a given data point belongs.

It is a fundamental task in data science and machine learning and is widely used in a variety of applications such as spam detection, image recognition, and credit risk assessment.

Classification requires careful planning, data exploration, and model development to achieve good results.

You can learn how to apply machine learning algorithms to classify incoming data points into a predefined set of categories by working on a classification project.

A classification project typically involves the following steps:

  • Exploring and pre-processing the data
  • Training and evaluating the model
  • Validating and optimizing the model
  • Deploying and monitoring the model

Key challenges in classification projects include data quality, feature selection, model complexity, and class imbalance.

By following best practices and addressing common challenges, data scientists can build effective classification models that drive meaningful business value.

Examples of Classification Project

These examples of classification projects demonstrate the versatility and power of machine learning in solving a wide range of problems.

1. Classifying types of coffee shop customers

A data scientist could build a model to classify different types of coffee shop customers, such as “office workers,” “students,” or “tourists.”

This project could involve training the model on a dataset of customer characteristics and behavior and evaluating its performance based on its ability to accurately classify new customers.

2. Diagnosing medical conditions

A data scientist might be asked to build a classification model to diagnose a medical condition based on patient data.

For example, the model might be used to predict whether a patient has a particular disease based on symptoms, test results, and other relevant data.

3. Identifying spam emails

In this project, a data scientist might be asked to build a classification model to identify spam emails in a person’s inbox.

The data scientist would analyze data on past emails, including the content of the email, the sender, and other metadata, to build a model that can accurately classify whether or not an email is spam.

5. Artificial Neural Network Project

Artificial Neural Networks (ANNs) are a type of machine learning algorithm modeled after the structure and function of the human brain.

They are composed of interconnected nodes, or “neurons,” that are capable of learning and identifying patterns in data.

ANNs are particularly well-suited for tasks that involve analyzing and interpreting complex data, such as image and speech recognition, natural language processing, and predictive modeling.

To work on an Artificial Neural Network (ANN) project as a data scientist, you should follow these steps:

  • Define the problem and gather data
  • Pre-process the data
  • Choose an appropriate ANN architecture
  • Train the ANN
  • Evaluate its performance
  • Fine-tune the ANN (if necessary)
  • Deploy it in a production environment

By following these steps and utilizing the appropriate tools and resources, you can successfully develop and deploy an ANN to solve complex data problems.

Examples of Artificial Neural Network Project

Here are examples of projects involving Artificial Neural Networks (ANNs) that data scientists may find interesting.

1. Image generation

ANNs can be used to generate images that look like photographs, drawings, or other types of visual media.

For example, a data scientist could use a Generative Adversarial Network (GAN) to train an ANN to generate realistic images of animals, landscapes, or people.

This type of project can be challenging and require a large amount of data and computational resources, but it can also be very rewarding and allow data scientists to explore the creative potential of ANNs.

2. Music generation

ANNs can also be used to generate music by learning from a dataset of audio samples and predicting the next notes or rhythms based on the input.

A data scientist could use a Long Short-Term Memory (LSTM) network, which is a type of recurrent neural network, to train an ANN to generate music in a particular style, such as jazz or electronic dance music.

This project could be a fun way to explore the intersection of machine learning and music.

3. Text-to-speech synthesis

ANNs can be used to synthesize speech from text, allowing data scientists to create a machine that can read out loud any text they provide.

They could use a sequence-to-sequence model, which is a type of neural network that can handle sequential data, to train an ANN to generate realistic speech in different languages and accents.

This project could be useful for creating assistive technology for people with disabilities or for creating voice assistants for smartphones and smart speakers.

6. Sentiment Analysis Project

Sentiment analysis is a natural language processing task that aims to identify and extract subjective information from text.

It can be used to classify text as positive, negative, or neutral, detect specific emotions, or extract opinions and attitudes.

Data scientists should be familiar with sentiment analysis since it may be used to understand customer feedback, product reviews, and even stock market patterns.

Data scientists can approach a sentiment analysis project by:

  • Defining the goals and scope
  • Building and training a model
  • Evaluating and deploying the model

Sentiment Analysis is a rapidly growing field in data science, with applications ranging from social media analysis to customer service.

Examples of Sentiment Analysis Project

These examples of Sentiment Analysis can help data scientists in getting a good grip on an often-used project in the industry.

1. Twitter sentiment analysis

Data scientists can use Twitter data to build a sentiment analysis model that classifies tweets as positive, negative, or neutral.

This can be useful for businesses to understand public sentiment towards their products or services, or for researchers to study trends in social media conversations.

2. Customer service sentiment analysis

Data scientists can use customer service transcripts or reviews to build a sentiment analysis model that detects emotions such as anger, happiness, or frustration.

This can be useful for businesses to improve customer satisfaction and identify areas for improvement.

3. Product review sentiment analysis

Data scientists can use online product reviews to build a sentiment analysis model that extracts opinions and evaluations about specific products.

This can be useful for businesses to understand consumer sentiment and make informed decisions about product development and marketing.

7. NLP Project

Natural Language Processing (NLP) is a field within data science that focuses on the interaction between human language and computers.

NLP is a fascinating field that offers data scientists a wide range of exciting projects to work on.

NLP projects also require large amounts of annotated data for training machine learning algorithms.

To complete these projects, data scientists need:

  • A strong understanding of NLP concepts and techniques
  • Programming skills such as Python or R
  • The ability to pre-process and clean data

Whether it’s developing machine translation systems, analyzing sentiment, or classifying text, NLP offers a wealth of opportunities for data scientists to make a real impact.

Examples of NLP Projects

These easy-to-do NLP projects mentioned below demonstrate the wide range of applications for NLP in data science.

1. Sentiment analysis

This is a common NLP task that involves classifying the sentiment of a piece of text as either positive, negative, or neutral.

For example, a data scientist could build a model that takes movie reviews as input and predicts whether the review is positive or negative.

2. Text classification

This involves assigning a label or category to a piece of text.

For example, a data scientist could build a model that takes news articles as input and predicts which category the article belongs to (e.g., politics, sports, entertainment).

3. Language translation

This involves translating text from one language to another.

A data scientist could build a model that takes English text as input and translates it to Spanish, or vice versa.

By taking on projects that showcase your skills and abilities, you can demonstrate your passion for data science and your commitment to staying up-to-date in the field.

With a strong portfolio of data science projects, you will be well-equipped to land the job you want in this exciting and fast-growing field.

Whatever project you choose, be sure to put in the time and effort to complete it to the best of your ability, and don’t be afraid to seek help or guidance along the way.

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Zita

Written by Zita

Data Scientist | Get my FREE Ebook “The Complete Python for Data Science Cheatsheet”: https://bit.ly/3UeUU2k

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Data Science Projects for Boosting Your Resume

Peter Scobas

Peter Scobas

  • June 28, 2019

We all know the old catch-22 — you need a job to get job experience and job experience to get a job. Luckily, that’s not entirely true in data science . You can use personal data science projects to demonstrate your skills to prospective employers — especially for landing your first data science job.

But where do you start? It’s important to pick a project you can showcase effectively. And it’s just as important to know how to include it in your resume or CV.

When you’re just starting to look into putting together your own data science project, you might feel a bit overwhelmed. In this post, I’ll guide you through the data science personal project process — from how to pick a good project topic to how to actually utilize your data science projects in your application.

data science project guestpost Peter Scobas

What to think about before picking a data science project topic

Before you start brainstorming topics, it’s important to think about the point of these projects: to show prospective employers you have strong technical skills and a knack for presenting data science results.

During a standard application process, you really have two opportunities to show and discuss your projects to the hiring team: a non-conversational opportunity (so either on your resume/CV or on your personal website — more on this later) as well as during an actual interview.

You need your project topic to work well in both capacities. Is it easy to digest and is it skimmable, so a recruiter or a hiring manager can quickly read it and understand it? Can you elaborate and discuss it at length to an interviewer?

So you might be thinking — wait, skimmable? I’m doing a bunch of work so a recruiter or a hiring manager might skim my data science project?

It’s true. The reality is that (at least during the early stages of the job application process) your application will be skimmed. And this includes your personal projects. Now, if a project catches their eye, a recruiter or hiring manager will spend more time reviewing your work. Which brings me to my next point: pick a project topic that will make potential recruiters and hiring managers say, “Huh. That’s actually pretty cool.”

Lastly: how many projects do you really need? I personally believe 2-3 good, interesting side projects is more than enough . Hiring companies just won’t spend the time looking through and reading the 4, 5, 6+ projects you have.

How to pick your dataset

The process of brainstorming your project topic starts off fairly straightforward. I recommend you begin by Googling “free public data” to get a general idea of what data is out there (or visit Google’s dataset search feature ) — and what you might be interested in working with. (Spoiler: there are TONS and TONS of free public datasets out there). 

data science project google public dataset

Before getting into data science, I came from an economics research background — so I knew a ton about where to find and how to analyze U.S. economic data. For one of my projects, I experimented with R’s ggplot2 and created aesthetically-pleasing charts to show economic trends using data from the Federal Reserve’s Economic Database . I was able to explain this project during one of my interviews because the panel was impressed by the visualizations I constructed… Moral of the story: companies are impressed when you have a portfolio of projects. And personal projects give you the chance to discuss work that you know a lot about and are passionate about.

If you’re still struggling for inspiration, a great strategy is finding a way to weave together data and pop culture. I’m a huge T.V. comedy fan; one of my favorite shows of all time is Parks and Recreation . It’s fairly easy to take one of your favorite shows or movies, find the script online, scrape the show/movie dialogue, and do some basic text analysis. If you’re intrigued with blending data science and pop culture but need more inspiration, I highly recommend the website Pudding.cool . (It’s also just a fantastic website to browse.)

Okay, so to summarize: start by thinking about a topic that you’re interested in. Google “free public data” if you need some inspiration–and don’t be afraid to get creative!

How to decide what to analyze  

Once you’ve decided on a dataset you’d like to explore, the next step is actually figuring out what questions to answer and what to analyze. If you recall what I said earlier: the best data science personal projects are eye-catching and skimmable. And the easiest way to make them that way is to create an awesome visualization.

No matter what you analyze, what question you try to answer, or what methodology you use, you need to think about how you will visualize your results. When you’re exploring your dataset, start thinking about possible trends or different ways you can segment the data.

Let’s revisit my Parks and Recreation example from before. Using the show dialogue, you can create a visualization to see which characters had the most lines. Or find out (if you’re familiar with the show this will make more sense) were Leslie Knope and the rest of the Parks Department really that mean to Jerry?

You might feel like you need to shoot for the moon and put together some technically-astounding machine learning project in order to impress a hiring team. If you have a strong background in statistics and programming and a lot of time — more power to you. However, a project like this is in no way necessary for getting hired as a data scientist. This may be a subject for another blog post, but in my experience, aspiring data scientists seem to immediately jump to fancy machine learning or deep learning tutorials — and forget about learning the basics and honing their problem solving, critical thinking, and presentation skills. 

If you’d like to go for an in-depth machine learning project — that’s great. But if you don’t, rest assured that simply answering an interesting and insightful question with your dataset is more than enough.

How to start building your projects

Once you have settled on how you will analyze your dataset, the next step is to start coding. What’s most important here is writing clean, easy to read, and well-commented code . (This is good practice in general–but especially important for your data science projects.) 

Once your code is written, the best way to display your code (and demonstrate to prospective employers that you can code) is to set up a GitHub account.

Already have a GitHub? Awesome. Just pin the repos you want people to see and add clear and concise READMEs that explain what your project is about.

Don’t have a GitHub? Confused what “pin the repo” means? Then I recommend you create a GitHub account and read this introduction .

GitHub is a fantastic place to demonstrate your programming ability to hiring managers. Just make sure that in addition to having clean and well-commented code, you also include a README file explaining your motivation and what your project is about.

How to present your projects in your CV/resume

Let me just mention this one more time: the point of these projects is to show prospective employers you have strong technical skills and a knack for presenting data science results.

With that in mind, let’s revisit my Parks and Recreation example and I’ll show you how I’d present this project on my resume/CV:

Okay, so a couple of things to notice: one, yes, this is short. However, space on your resume is scarce. You have your job experience, skills, education, and contact information taking up space. If you’re discussing 2-3 projects (with 1-2 bullet points each), that can easily take up over a third of your resume (and your resume needs to stay one page, of course!). 

Also a topic for another blog post — but you don’t want your resume to become cluttered. More is not always better — short and skimmable is the name of the game.

It’s also important to notice that I mention the packages I used in my project. This signals your programming proficiency and gives recruiters keywords to see. (Oftentimes, recruiters are looking for certain keywords while reviewing resumes.)

Yes, this description is short, and yes it’s disappointing to do a bunch of work and not be able to fully explain and outline your project on your resume. But you have two more opportunities to go more in depth about your projects: on your website and during an actual interview.

In an ideal world, recruiters and whoever else is reviewing your resume would spend 5-10 minutes looking over your resume, carefully reading each bullet point, and fully grasping your skills and experience. However, that’s just not the case. Your resume/CV will be skimmed. Oftentimes, the people who are able to succinctly demonstrate their skills and experience end up getting the interviews. So, write short descriptions. Include keywords. Avoid clutter.

How to present your projects on your website

Your website gives you the opportunity to showcase your personal projects in depth.

As I mentioned before, the best projects to display are ones that can be succinctly presented — meaning, you have a well-constructed plot or table and a clear description of the project that is a few sentences to a paragraph or so in length. Also — don’t forget to include a link to your code!

Below is how I’d present my Parks and Recreation example on my website (note: this is just an example, not an actual analysis of the show) :

data science project chart

At this point you’re probably tired of listening to me say how you need your analysis to be clear and concise. But this point is incredibly important! The biggest struggle with data science departments is being able to effectively communicate their findings to the rest of the company to help make data-driven business decisions. If you’re able to show the hiring manager that you can clearly present your analysis (whether it is a simple visualization or a fancy machine learning model) you will stand out in the interview process.

I’ve always been one to preach simplicity and clarity over anything else — especially for your first data science job. Unless you’re coming from a technical PhD program, companies just aren’t expecting first-time data science applicants to be able to take on difficult machine learning tasks (if a company does expect that from a first-time data science applicant, that company’s data team is a mess).

Your personal data science projects are a fantastic way to showcase your technical skills, presentation skills, and creativity. If you focus on writing clean code and having clear visualizations and an insightful analysis you’ll be well on your way to landing your first data science job.

data science project guestpost peter scobas-1

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The Junior Data Scientist's First Month

Resume Worded

  • Resume Examples
  • Data & Analytics Resumes

12 Data Scientist Resume Examples - Here's What Works In 2024

Data scientists are one of the hottest jobs of 2023. however, it’s also one of the most analytical, results-driven, and requires superb use of numbers. if you can show that on your resume, you’ll be on your way to a nice career as a data scientist. here are five data scientist resume templates to help you get an idea of what to put in your resume..

Hiring Manager for Data Scientist Roles

If career growth is one of your main qualifications for your next job, a career in data science is perfect for you. According to Towards Data Science , it’s the fastest-growing job on LinkedIn with an estimated over 11 million jobs by 2026. And it deserves to have such a bright future. You can apply for this job in several industries like e-commerce, IT, business, and much more. Because this field is so versatile, you can apply your skills somewhere that would greatly benefit others, not just a company. For example in healthcare, you can help visualize and manage data necessary for operation procedures. For a job like this, you need to be good with numbers and data. The ability to use statistics, analyze complex data, simplify it, and present it more easily for others are all necessary components of the job. You’ll need to display these skills, plus some experience with computer programs like Amazon Web Services to handle big data, in your resume. Today, we’ll be sharing with you the tips you need to make a data scientist resume that recruiters will look at.

Data Scientist Resume Templates

Jump to a template:

  • Data Scientist
  • Senior Data Scientist
  • Entry Level Data Scientist
  • Data Science Manager
  • Data Science Vice President
  • Junior Data Scientist
  • Career Change into Data Science

Jump to a resource:

  • Keywords for Data Scientist Resumes
  • How To Write a Data Scientist Resume (Step-by-Step)

Data Scientist Resume Tips

  • Action Verbs to Use
  • Writing a Resume Summary
  • Bullet Points on Data Scientist Resumes
  • Frequently Asked Questions
  • Related Data & Analytics Resumes
  • Similar Careers to a Data Scientist
  • Data Scientist CV Examples

Template 1 of 12: Data Scientist Resume Example

A data scientist uses and processes raw data to discover interesting insights that help organizations make more informed decisions. They are part of the entire life cycle of data science projects. This means they work on collecting and storing data, as well as in data processing, developing data models, data analysis, and visualization. Cloud migration is now an in-demand skill for data scientists, due to the rapid adaptation of cloud services. Hence, it might be a good idea to include cloud migration skills on your resume.

A data scientist resume template including big data and programming skills.

We're just getting the template ready for you, just a second left.

Tips to help you write your Data Scientist resume in 2024

   include up-to-date data analysis or big data skill sets on your resume, like tinyml..

Data science is a fast-changing field, and hiring managers particularly at tech companies or startups love when candidates include recent technologies. One example is TinyML or other ML algorithms. Machine learning algorithms are perfect for processing large sets of data, especially when working with cloud-based systems with unlimited bandwidth. It might be worth including a project on your resume where you used ML or insights from an ML algorithm to improve the bottom line at your company (if you drove revenue or saved costs as a result of running a data science algorithm, hiring managers will be thrilled).

Include up-to-date data analysis or big data skill sets on your resume, like TinyML. - Data Scientist Resume

   Indicate your proficiency in data visualization tools like Tableau or Google Charts.

Mention projects in which you used your data visualization skills to present your insights. Data visualization plays a huge role in data science projects, so it’s important to demonstrate you have experience in this area.

Indicate your proficiency in data visualization tools like Tableau or Google Charts. - Data Scientist Resume

Skills you can include on your Data Scientist resume

Template 2 of 12: data scientist resume example.

Because you are working with data that provide to you or you provide other departments data to use, you need to display successful collaboration with results in your resume. This sample does this by talking about what company goals were accomplished with other teams using metrics to highlight the achievements.

If your work has brought in positive results for the company, explain it in your data scientist resume using numbers, achievements, and strong verb choice.

   Numbers and metrics relevant to data scientists

You can see examples of metrics to go with the companies’ achievements. For example, this person increased “customer traffic by 75%”, and generated “$1 million in wealth management sales”. Data science is always aligned with company KPIs, so list your achievements in a way that describes how you solved a company’s problem.

Numbers and metrics relevant to data scientists - Data Scientist Resume

   Strong action verbs related to data scientists

When you read this sample, you’ll see words like “implemented”, “optimize”, and “reduced.” All these are action verbs that communicate the ability to do/succeed in a task. Include strong action verbs in your resume that communicates your ability to organize projects and collaborate with others.

Strong action verbs related to data scientists - Data Scientist Resume

Template 3 of 12: Senior Data Scientist Resume Example

Senior data scientists outline project requirements, delegate tasks to junior data scientists, monitor their performance and carry out upper-level responsibilities. Their purpose is to drive companies to success by using data analytics. Your potential employer might expect you to have extensive experience in data science, so it’s important to demonstrate seniority on your resume. You should prioritize relevant job experience and highlight your leadership background.

A senior data scientist resume template demonstrating seniority through experience.

Tips to help you write your Senior Data Scientist resume in 2024

   indicate your proficiency in r, python, or other relevant programming languages by mentioning previous projects in which you used them..

Since most companies are generating a large amount of data, you need specific programming languages such as R or Python to process them. That’s why your potential employer might be looking for an experienced senior data scientist in these programming languages.

Indicate your proficiency in R, Python, or other relevant programming languages by mentioning previous projects in which you used them. - Senior Data Scientist Resume

   Demonstrate experience in formulating and overseeing data-centered projects.

A senior data scientist is a leadership role. You will be supervising other junior data scientists to ensure they follow certain standards and processes, whether that involves cleaning or exploration. That’s why it is important to demonstrate on your resume that you have experience with developing and monitoring these types of projects.

Demonstrate experience in formulating and overseeing data-centered projects. - Senior Data Scientist Resume

Skills you can include on your Senior Data Scientist resume

Template 4 of 12: senior data scientist resume example.

If you’re trying to climb up to the top of the data scientist ladder, you need to show that you excelled in lower positions. Don’t forget to list what you did that earned you an upper-level role in your previous job. Recruiters love to see that you desire to grow. Talking about your transitions is key in this kind of resume.

Demonstrate growth in your senior data scientist resume by explaining promotions and ways you’ve improved your company’s bottom line.

   Shows growth in promotions

In the sample, you see that there was a promotion within a short amount of time at a company. If you had a promotion, emphasize it by separating the job titles and explaining what work you’ve done that contributed to you getting promoted.

Shows growth in promotions - Senior Data Scientist Resume

   Numbers and metrics relevant to senior data scientists

Don’t just list promotional achievements without also providing the metrics. Recruiters want to see how you’ve been beneficial to the previous company, and numbers are a great way to show your achievements. That gives recruiters an idea of how you can help their company out.

Numbers and metrics relevant to senior data scientists - Senior Data Scientist Resume

Template 5 of 12: Entry Level Data Scientist Resume Example

As an entry level data scientist, you'll be dipping your toes into the world of analyzing and interpreting complex data sets to help businesses make informed decisions. While the demand for data scientists has been booming in recent years, competition for entry-level roles can be fierce. To stand out, your resume should showcase your technical skills and demonstrate your ability to turn raw data into valuable insights for the company. Think about highlighting projects where you've used relevant programming languages, machine learning techniques, and data visualization tools. In addition to showcasing your technical expertise, don't forget to highlight any internships or relevant work experience you have related to data analysis. Companies are not just looking for technical wizards; they are also seeking individuals who can work well with others, translate complex findings into understandable insights, and ultimately drive business growth. Make sure to include any instances where you've collaborated with cross-functional teams or presented data-driven findings to non-technical stakeholders.

Entry level data scientist resume snapshot

Tips to help you write your Entry Level Data Scientist resume in 2024

   show off your technical skills.

As an entry level data scientist, you should emphasize your programming abilities and proficiency in languages like Python, R, and SQL. Additionally, mention any experience working with data analysis tools, such as Tableau, to demonstrate your ability to visualize and communicate results effectively.

Show off your technical skills - Entry Level Data Scientist Resume

   Highlight your problem-solving capabilities

Data scientists need to be adept at solving complex problems and uncovering insights from raw data. Use your resume to share examples of how you've approached and solved data-related challenges, emphasizing your analytical mindset, creativity, and critical thinking skills.

Highlight your problem-solving capabilities - Entry Level Data Scientist Resume

Skills you can include on your Entry Level Data Scientist resume

Template 6 of 12: entry level data scientist resume example.

Right out of college, you may not have much experience in the field. To supplement that, use your experience in clubs and activities, class projects, and useful coursework to help highlight your knowledge on the subject. Internship experience is essential, as well; any numeric results or accomplishments should be acknowledged. This sample does so by listing the percentages of costs, labor, and hours reduced thanks to their work.

Entry level data science resume: When you don’t have much on the field experience, use the skills and projects you’ve done that are related to data science to communicate how effective you can be for the role.

   Strong data scientist technical skills

Not only are key skills listed in the skills section (things like MATLAB or SQL), you can also see this sample mention the use of some of these skills throughout their experience. You should also include skills that are relevant to data science jobs that you have - review the job description that you're applying to for skills the job is looking for.

Strong data scientist technical skills - Entry Level Data Scientist Resume

   University projects relevant to data scientists

Class projects are good examples of how a recent grad has applied critical job skills. In the descriptions, it also lists awards won. This shows that the projects they worked on were successful in applying what they learned to get results.

University projects relevant to data scientists - Entry Level Data Scientist Resume

Template 7 of 12: Data Science Manager Resume Example

A data science manager has an administrative and technical role. They are responsible for guiding and overseeing the data science team. Hence, they will determine project outlines, deadlines, and priorities, and ensure team members follow specifications. As a data science manager, you should ideally have a master’s degree in data science or equivalent experience. You can take your resume to another level by demonstrating your impact on previous projects’ results. This way, you are showcasing your tangible value.

A data science manager resume template highlighting leadership experience.

Tips to help you write your Data Science Manager resume in 2024

   include your data science certifications on your resume..

Your data science manager resume should highlight your academic value and expertise, and certification is a great way to demonstrate that. These are third-party validated credentials that exhibit your skills and years of experience.

Include your data science certifications on your resume. - Data Science Manager Resume

   Highlight your project management skills through relevant work experience.

Data science managers should have project management skills to successfully drive success to the data science team. Recruiters are looking for past evidence of assigning tasks, prioritizing deliverables, providing feedback, conducting research, and ensuring team members’ performance. To highlight this, include action verbs like "Led" or "Managed".

Highlight your project management skills through relevant work experience. - Data Science Manager Resume

Skills you can include on your Data Science Manager resume

Template 8 of 12: data science manager resume example.

To be a successful manager in any role, you need to have the experience of a manager. A focus on team management and leading a team to great results are examples you should list on your resume. Showing recruiters that you can lead a team or data science project that brings high-yield results is what will set your resume apart from other applicants. Data science is all about using data to drive decision-making and top-level KPIs, so make sure you add accomplishments to your resume that highlight how your work has affected your company’s bottom line.

If you can show leadership abilities that lead to great results, display that in your data science manager resume just like this sample does.

   Emphasis on managerial skills

You can see in the experience section of this sample how they led a few projects. They discuss what was done, who they worked with, and how big a team they had. Follow a similar layout in your resume so recruiters can see that you can lead data science teams.

Emphasis on managerial skills - Data Science Manager Resume

   Tailored to the data science industry

One way that you can get your resume past the filtering system, or ATS, is to use specific keywords that are found throughout the job description. In this sample, you see keywords like “training and peer-mentoring”, “data systems”, and “regression analysis.”

Tailored to the data science industry - Data Science Manager Resume

Template 9 of 12: Data Science Vice President Resume Example

A Data Science Vice President sits at the intersection of data analytics, business strategy, and leadership. In recent years, your role has evolved from pure data analysis to one where you're expected to guide an entire organization's data strategy. As companies increasingly rely on data-driven decision-making, you're not just crunching numbers but explaining their implications to non-technical executives. When crafting a resume for this role, remember companies are looking for a strategic thinker who can leverage data to drive business growth, not just a seasoned analyst. As the field becomes more competitive, hiring managers are expecting more than just top-notch technical skills. They want to see a track record of transforming raw data into actionable insights that drive business results. They're also looking for leaders who can build and guide high-performing data science teams. So, make sure your resume reflects these demands and trends.

A professional resume of a candidate applying for a Data Science Vice President role.

Tips to help you write your Data Science Vice President resume in 2024

   highlight strategic leadership.

As a Data Science Vice President, you're expected to be a strategic leader. Highlight instances where you've used data to inform business strategy. Show how you've influenced decision-making at the executive level by translating complex data into digestible insights.

Highlight Strategic Leadership - Data Science Vice President Resume

   Focus on Team Building and Management

This role isn't just about your expertise with data, but also your ability to lead a team. Detail your experience in building, leading, and mentoring data science teams. If you've overseen sizeable teams or managed across different locations, ensure that it shines on your resume.

Focus on Team Building and Management - Data Science Vice President Resume

Skills you can include on your Data Science Vice President resume

Template 10 of 12: data science vice president resume example.

Like any VP role, the position of vice president of data science needs strong managerial skills. Not only will you need to manage a team, but that team will also have to consist of managers. Your goal is to implement and execute company-wide goals that greatly benefit the company. This sample lists out the processes done while managing managers lower on the corporate ladder, to bring in an increase of profit or a decrease in costs (or increase in productivity).

If your work experience displays you consistently climbing higher up the job ladder, talk about it in a way that shows how successful you are at helping a team/company perform dramatic positive changes.

In this sample, the positions listed are all higher than the ones listed below. That shows recruiters that you have the ambition to climb to the top. Additionally, with each upper management role, you see growth in the people they work with; they started with “hired 8 new candidates” and are now “worked closely with a cross-functional team.” Show your incline in managerial responsibilities in your resume.

Shows growth in promotions - Data Science Vice President Resume

   Focused on the vice president of data science role

In the upper management positions of this sample, you see how it talks about working with other department teams to deliver results that are often well over 40%. Positive metrics like this help show your abilities as a capable vice president.

Focused on the vice president of data science role - Data Science Vice President Resume

Template 11 of 12: Junior Data Scientist Resume Example

Junior data scientists are just data scientists that have under five years of industry experience, or have recently made a career change into the field. The title is sometimes used interchangeably with the regular 'data scientist', so you can use this template whether or not you're a junior data scientist or have some experience in the field.

Simple 2 column resume template that makes effective use of all the space in the document.

Tips to help you write your Junior Data Scientist resume in 2024

   numbers and metrics relevant to data scientists, and good use of skills relevant to data scientists..

You can see examples of metrics to go with the companies’ achievements. Plus, all the skills mentioned are very relevant to the data science and engineering field.

Numbers and metrics relevant to data scientists, and good use of skills relevant to data scientists. - Junior Data Scientist Resume

   Good use of space

The two-column in this data scientist resume template prioritizes the work experience sections, while maximizing the content into the resume. The resume does not look overcrowded and uses reasonable margins. Not all two column templates are ATS-compatible, but this one is when it is saved as PDF and passed through a resume screener.

Good use of space - Junior Data Scientist Resume

Skills you can include on your Junior Data Scientist resume

Template 12 of 12: career change into data science resume example.

If you're trying to break into data science, but don't have formal data science experience yet, use a template like this one.

Career change into data science

Tips to help you write your Career Change into Data Science resume in 2024

   stress transferrable skills from your previous experiences.

Even if you didn't do data science work in your previous professional roles, you have technical experience as well as leadership, teamwork and analytical skill sets.

Stress transferrable skills from your previous experiences - Career Change into Data Science Resume

   Use keywords and skills from the new industry on your career change resume

To get past the applicant tracking systems and resume screeners, it's important that you use the right keywords for your target job, which in this case is a data science position. Even though you might have sales or product marketing experience, use keywords that are specific to data science only - including things like SQL/database experience, ML/AI experience, and other data preparation tools and techniques.

Use keywords and skills from the new industry on your career change resume - Career Change into Data Science Resume

Skills you can include on your Career Change into Data Science resume

Action verbs for data scientist resumes, how to write a data scientist resume.

Here are step-by-step instructions on how to write an effective resume for a data scientist role. This guide can be used by both entry-level and experienced data scientists as well as data scientist managers.

Basic steps for writing a Data Scientist resume

1.1: place important information in your header.

Place your name at the top of the resume followed by your professional email address, city/country, and phone number. You could also include the job title of your desired role—e.g., Data Analyst—to tailor your resume to the job. It is a good idea to include links to your professional website and online profiles such as LinkedIn and GitHub.

Place important information in your header

1.2: Select sections that highlight your most relevant experience

A Data Scientist resume needs sections for experience and education. Unless you are a recent graduate, you should list your experience section first. If you have carried out projects that highlight your data analysis skills, you can include a projects section that briefly describes the projects alongside metrics that show what you accomplished.

Select sections that highlight your most relevant experience

Use bullet points to showcase your experience as a Data Scientist

2.1: use the [action verb] + [task] + [metric] format for your bulleted points.

A bulleted list of your achievements in the work experience section will make your resume easy for data science hiring managers to skim. Each bullet point should highlight a specific task or achievement from your previous role. Take a look at the bullet point example below: "Modelled user-engagement framework that reduced churn rate using predictive modeling and clustering that reduced churn rate by 40%." Notice how the bullet point uses an action verb that is relevant to data analysis, "Modelled". We describe a task that was completed and use numbers and metrics to quantify the impact of our achievement.

Use the [Action Verb] + [Task] + [Metric] format for your bulleted points

2.2: Highlight collaborative work and initiative

For mid to senior Data Scientist roles, you will need to demonstrate you can take initiative and work with other departments. Talk about collaborating with other teams to drive business decisions. To land a Data Science Manager role, highlight how you led a team to great results in a data science project.

Highlight collaborative work and initiative

Get past resume screeners by including the right technical skills

3.1: use word or google docs resume template for your draft, then save it as pdf.

Start your resume with a simple template in Word or Google Docs format. This ensures your resume can be scanned easily by Applicant Tracking Systems, which are software used to screen resumes online. Convert your resume to PDF to ensure the formatting and layout appears correctly to a data science recruiter.

Use Word or Google Docs resume template for your draft, then save it as PDF

3.2: Use an online resume checker to make sure resume scanners can read your resume

If the ATS cannot read your resume, it will automatically discard your application before a Data Science recruiter gets to see it. Upload your resume for free to a resume scanner to ensure it can be read correctly and that the bullet points and sections are correctly constructed.

Use an online resume checker to make sure resume scanners can read your resume

3.3: Include a technical skills section

Populate the skills section with hard skills and keywords that the resume filtering software will be looking for. Common skills for Data Scientists include Machine Learning, Python, SQL, R, Data Mining, Statistical Modeling, and Hadoop.

Include a technical skills section

Finalizing your Data Scientist resume

4.1: include resume summary if you are changing careers or are a senior level hire.

While resume objectives are outdated and should never be used, a resume summary is an optional section at the top of your resume that can help direct a recruiter's attention to specific skills and achievements not listed in the rest of the resume. The summary can also include transferable skills for people shifting to Data Science from other careers.

 Include resume summary if you are changing careers or are a senior level hire

4.2: Reread the job description as you edit your resume

When you finish writing your resume, reread the job description. This will give you a sense of how well your resume matches relevant keywords in the data scientist role. Check whether you have included examples of your impact, such as the amount of savings your company experienced because of the machine learning model that you implemented.

Reread the job description as you edit your resume

Skills For Data Scientist Resumes

Data science is a number-intensive, data-heavy field. It’s one thing to know how to read the data. You also need to convert that data in a way that makes a company’s overall processes smoother. Your list of skills should aid in showing that. Because you’d be using languages like Python or SQL, it’s important to state it beyond the skills section. Where possible, mention how you used these tools in your experience, whether that’s to process large data sets, discover insights or drive business decisions. If recruiters can see that you know how to use critical tools for the job on your resume, it’ll stand out more. Plus, your resume will get past resume screening tools/ATS since employers often filter resumes out by searching for skills they expect to see. Closely read the job description to find skills to include in your resume.

  • Data Science
  • Machine Learning
  • Artificial Intelligence (AI)
  • Deep Learning

Data Mining

  • Python (Programming Language)
  • Natural Language Processing (NLP)
  • Apache Spark
  • R (Programming Language)
  • Predictive Analytics
  • Predictive Modeling
  • Software Development
  • Statistical Modeling

Skills Word Cloud For Data Scientist Resumes

This word cloud highlights the important keywords that appear on Data Scientist job descriptions and resumes. The bigger the word, the more frequently it appears on job postings, and the more 'important' it is.

Top Data Scientist Skills and Keywords to Include On Your Resume

How to use these skills?

Data science is a broad job category. You could have a focus on designing machine learning algorithms/predictive analytics, or data visualization, or mathematics and statistics. You may even have more of a focus on the business side of things. No matter which area of data science you’re in, follow these tips to help you tailor the perfect resume.

   Think it all through first

Before you start filling out your resume, have a brainstorming session. What programs, teamwork-based, or other hard skills do you have that are relevant? What are some of the achievements you’ve had on the job? Did you do (and succeed) any data science projects? Have an idea of all of that first. Then, write it out in your experience. The key is to ensure you’re including quite a few metrics. A role that involves a lot of data requires someone who is good at handling big numbers and knows how to effectively use the info. If that data involves cooperation from another department, include that as well.

   Edit it so the resume is fitting for the job description

When you finish writing it, reread the job description. How well do you think you did in matching your resume’s keywords with the job opening’s keywords? Have you left out the filler information? (You should; only make space for what’s necessary, especially when you have lots of experience.)

  Include personal projects

For those of you who are transitioning from a different --but possibly somewhat relevant-- field, or are fresh out of school, projects are your friend. Just be certain to briefly describe what the project was for, what you accomplished, and provide metrics. Let’s say that you want to enter the finance field; an example project you can complete is a credit card fraud detector. You’ll use Python to track transaction history and spending habits, and use regression analysis to accurately track the two. You can also include links to your Github profile too, especially if you have a project that’s particularly relevant.

   Talk about collaborations with teams

For those of you who are veterans in the field, focus on your work done with other departments. Data science is all about working with other teams to drive business decisions, and teamwork is a skill that recruiters look for. What collaborative projects have you done that exemplifies this? Are/were you in charge of leading a team that brought in lots of revenue or extra work time? Have you been in charge of a major development project? Detail this information in your experience.

Action Verbs For Data Scientist Resumes

The field is all about quantifying aand using data. In your resume, you need to explain what you did with the data you have. In the samples, you’ll see examples of action verbs like “implemented”, “developed”, “coached”, and more. Action verbs like these show that you know how to apply the knowledge you have to your work.

For a full list of effective resume action verbs, visit Resume Action Verbs .

How To Write a Resume Summary for a Data Scientist Resume

If you're a senior-level employee, or you're changing careers to become a Data Scientist, it's useful to add a paragraph at the top of your resume highlighting your most impressive accomplishments. This is called a resume summary. Here's an example of a summary that can be used on a Data Scientist resume.
A resume summary is a totally optional section, and in most cases, it's better to leave it out of your resume than include it. For example, if you're a student or mid-level hire, you should not include a summary, and instead use the space to add to your work experience.

How to write a resume summary if you are applying for a Data Scientist resume

To learn how to write an effective resume summary for your Data Scientist resume, or figure out if you need one, please read Data Scientist Resume Summary Examples , or Data Scientist Resume Objective Examples .

Resume Bullet Points From Data Scientist Resumes

You should use bullet points to describe your achievements in your Data Scientist resume. Here are sample bullet points to help you get started:

Conducted private equity due diligence in $400M portfolio. Performed strategic and analytical valuation of assets based on interviews with experts and created extensive models of the industries; persuaded client to move forward with acquisition

Analyzed data from 25000 monthly active users and used outputs to guide marketing and product strategies; increased average app engagement time by 2x, decrease drop off rate by 30%, and increased shares on social media by 3x over 6 months

Generated insights on customer churn and renewal rates from data tables with 100M rows in SQL

Liaised with marketing to drive email and social media advertising efforts, using predictive modeling and clustering, resulting in a 35% increase in revenue

Reduced signup drop-offs from 65% to 15%, increased user-engagement by 40%, and boosted content generation by 15%, through a combination of user interviews and A/B-testing-driven product flow optimization

For more sample bullet points and details on how to write effective bullet points, see our articles on resume bullet points , how to quantify your resume and resume accomplishments .

Frequently Asked Questions on Data Scientist Resumes

How can i improve my data scientist resume.

  • Include a projects section that briefly describes the projects alongside metrics that show what you accomplished. Here, list projects that demonstrate the use of statistical methods, data visualization techniques and predictive models.
  • Include the job title for the desired role—Data Scientist—on the resume header below your name. This makes your resume easier for screening software to categorize.
  • Include links to your professional website and online profiles such as LinkedIn and GitHub.
  • Include a summary section if you are a senior-level hire or are changing careers to direct the recruiter’s attention to transferable skills and exceptional achievements.

How does a data scientist’s resume differ from that of other data analytics roles?

What skills should you put on a data scientist resume, what are strong examples of bullet points i can include in my data scientist work experience.

Modelled a user-engagement framework that reduced churn rate using predictive modelling and clustering that reduced churn rate by 40%. Designed and implemented securities forecasting models, improving stock market forecast accuracy by 15%.

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  • Data Scientist Resume Example
  • Senior Data Scientist Resume Example
  • Entry Level Data Scientist Resume Example
  • Data Science Manager Resume Example
  • Data Science Vice President Resume Example
  • Junior Data Scientist Resume Example
  • Career Change into Data Science Resume Example
  • Step-by-Step Guide
  • Skills and Keywords to Add
  • Tips for Data Scientist Resumes
  • Sample Bullet Points from Top Resumes
  • All Resume Examples
  • Data Scientist Cover Letter
  • Data Scientist Interview Guide
  • Explore Alternative and Similar Careers

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Top Data Science Projects With Source Code

Data science project ideas, best data science projects for beginners, intermediate data science projects with source code, advanced data science projects with source code, additional resources.

Data Science continues to grow in popularity as a promising career path for this era. It’s one of the most exciting and attractive options available. Demand for Data Scientists is increasing in the market. According to recent reports, demand will skyrocket in the future years, increasing by many times. Data Science encompasses a wide range of scientific methods, procedures, techniques, and information retrieval systems to detect meaningful patterns in organized and unstructured data. More opportunities emerge in the market as more industries recognize the value of Data Science. 

If you’re interested in Data Science and want to learn more about the technology, now is as good a time as ever to develop your abilities to understand and manage the upcoming problems. Initially, understanding it can be difficult, but with regular effort, you will soon understand the many concepts and terminology used in the field. If you are interested in becoming a Data Scientist , it is strongly recommended that you apply your skills to become a competent professional in this sector. If you’re genuinely interested in learning what it’s like to be a professional after gaining some solid theoretical understanding of Data Science, now is the time to start working on some actual projects. 

As a result, participating in live Data Science Projects will enhance your confidence, technical expertise, and general confidence. But, most significantly, if you undertake Data Science projects for final year projects, you will find it much simpler to land a solid job.

Confused about your next job?

This article aims to give project ideas on data science that are appropriate for different levels of learners.

 This section will provide a list of data science project ideas for students new to Python or data science in general. These data science projects in python ideas will provide you with all of the tools you’ll need to succeed as a data science developer . The following are the data science project ideas with source code.

1. Fake News Detection Using Python

Fake news do not require any introduction. It is very much easy to spread all the fake information in today’s all-connected world across the internet. Fake news is sometimes transmitted through the internet by some unauthorised sources, which creates issues for the targeted person and it makes them panic and leads to even violence. To combat the spread of fake news, it’s critical to determine the information’s legitimacy, which this Data Science project can help with. To do so, Python can be used, and a model is created using TfidfVectorizer. PassiveAggressiveClassifier can be implemented to distinguish between true and fake news. Pandas, NumPy, and sci-kit-learn are some Python packages suitable for this project, and we can utilize News.csv for the dataset.

Source Code – Fake news detection using python

2. Data Science Project on Detecting Forest Fire

Developing a project for identifying the forest fire and wildfire system is an alternatively good example to exhibit one’s skills in Data Scienc e. The forest fire or wildfire is an uncontrollable fire that develops in a forest. All the  forest fir will create havoc during weekends on the animal habitat, surrounding environment and human property. k-means clustering can be used for the identification of the  crucial hotspots during forest fire  and to reduce the  severity , to regulate them and even  to predict the behaviour of the wildfire. This is advantageous for allocating the required resources. To enhance the model’s accuracy, it is ideal to use climatological data to find out the common periods and seasons for wildfires.

Source Code – Detecting Forest Fire

3. Detection of Road Lane Lines  

A Live Lane-Line Detection Systems built-in Python language is another Data Science project idea for beginners. A human driver receives lane detecting instruction from lines placed on the road in this project. The lines placed on the roads indicate where the lanes are located for human driving. It also refers to the vehicle’s steering direction. This application is crucial for the development of self-driving cars. This application for the Data Science Project is critical for the development of self-driving cars.

Source Code – Detection of Road Lane Lines

4. Project on Sentimental Analysis

The act of evaluating words to determine sentiments and opinions that may be positive or negative in polarity is known as sentimental analysis. This is a sort of categorization in which the classifications are either binary (optimistic or pessimistic) or multiple (happy, angry, sad, disgusted, etc.). The project is written R Language, and u the dataset provided by the Janeausten R package is used. The general-purpose lexicons like AFINN, bing, and Loughran are used to execute an inner join and present the results using a word cloud.

Source Code – Project on Sentimental Analysis

5. Project on Influences of Climatic Pattern on the food chain supply globally

The abnormalities and changes occurring in the climate very often are the main challenges impressed on the environment that needs to be taken care of. These environmental changes will affect the human beings on earth. This Data Science Project makes an attempt to analyse the changes in the food production globally that occurs due to change in climatic conditions. The main purpose of this study is to evaluate the consequences of climatic changes on primary agricultural yields. This project will evaluate all the effects related to change in temperature and rainfall pattern. The amount of carbon dioxide that impacts plant development and the uncertainties in climate change will next be considered. As a result, data representations will be the primary focus of this project. It will also assess productivity across different locations and geographical regions.

In this section, data science projects for intermediate level learners are discussed:

1. Project on  Speech Recognition through the Emotions

One of the fundamental strategies for us to communicate ourselves is the speech, and it involves various feelings including silence, anger, happiness, and passion etc. It is possible to use the emotions behind the speech to reorganize our emotions, the service we offer, and the end products to deliver a custom-made service to particular persons by evaluating the emotions behind it. The main aim of this project is to identify and get the feelings from multiple files involving sound that comprises the human speech. Python’s SoundFile, Librosa,, NumPy, Scikit-learn, and PyAaudio packages can be used to produce something alike. In addition, you can use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for the dataset containing over 7300 files.

Source Code – Speech Emotion Analyzer and Speech Emotion Recognition

2. Project on Gender Detection and Age Prediction 

This project on detecting the gender and predicting the age identified as a classification challenge, will put your Machine Learning and Computer Vision skills to work. The goal is to create a system that can analyze a person’s photograph and determine their age and gender. Python and the OpenCV library to implement Convolutional Neural Networks can be used for this entertaining project. For this project, the Adience dataset can be downloaded. Remember that factors like cosmetics, lighting, and facial expressions will make this difficult, and try to throw your model off.

Source Code – Gender Detection and Age Prediction

3. Project on Developing Chatbots

Chatbots are important for companies since this project can answer all the questions posed by the clients and information without the process being slowing down. The customer support workload has been decreased by the procedures which is fully automating. This process can be easily obtained by implementing Machine Learning,  Artificial Intelligence and Data Science techniques. Chatbots operate by assessing the customer’s input and responding with a mapped response. Recurrent Neural Networks using the intentions JSON dataset may be used to train the chatbot, while Python can be used to implement it. The objective of the chatbot will determine whether it is domain-specific or open-domain.

Source Code – Developing Chatbots

4. Project on Detection of Drowsiness in Drivers

Sleepy drivers are one of the causes of road accidents, which claim many fatalities each year. Because drowsiness is a possible cause of road danger, one of the best methods to avoid it is to install a drowsiness detection system. Another technology that can save many lives is a driver sleepiness detection system that continuously assesses the driver’s eyes and alerts him with alarms if the system detects that the driver closes his eyes very often. A webcam is required for this project for the system to monitor the driver’s eyes regularly. This Python project will require a deep learning model as well as packages such as OpenCV, TensorFlow, Pygame, and Keras to do this.

Source Code – Driver Drowsiness Detection and Driver Drowsiness Detection

5. Project on Diabetic Retinopathy

Diabetic Retinopathy is a primary cause of blindness in people with diabetes. An automated diabetic retinopathy screening system can be developed. On retina photographs of both damaged and healthy people, a neural network can be trained. This research will determine whether or not the patient has retinopathy.

Source Code – Diabetic Retinopathy Detection and Diabetic Retinopathy Detection Topics

In this section, the data science projects for advanced learners are discussed.

1. Project on Detection of Credit Card Fraud

Credit card fraud is more widespread than you might believe, and it’s been on the rise recently. By the end of 2022, we’ll have crossed a billion credit card users, metaphorically. However, credit card firms have been able to successfully identify and intercept these frauds with significant accuracy because of advancements in technology such as Artificial Intelligence, Machine Learning, and Data Science . Simply stated, the concept is to examine a customer’s regular spending pattern, involving locating the geography of such spendings, to distinguish between fraudulent and non-fraudulent transactions. The languages R or Python can be used to ingest the customer’s recent transactions as a dataset into decision trees, Artificial Neural Networks, and Logistic Regression for this project. The system’s overall accuracy would increases if additional data is fed.

Source Code – Credit Card Fraud Detection and Credit Card Fraud Topics

2. Project on Customer Segmentations

One of the most well-known Data Science projects is customer segmentation. Companies build various groupings of customers before launching any marketing. Customer segmentation is a prominent unsupervised learning application. Companies utilize clustering to discover client groupings and target the possible user base. They classify clients based on shared traits such as gender, age, interests, and spending habits to market to each group successfully. Visualization of the gender and age distributions can be done using K-means clustering. Then their annual earnings and spending habits are also analyzed.

Source Code – Customer Segmentations and Customer Segmentations Topics

3. Project on the recognition of traffic signals

Traffic signs and rules are extremely crucial to observe to avoid any accidents. To observe the guideline, one must first comprehend the appearance of the traffic sign. Before receiving a driver’s license, a person must first study all of the traffic signs. However, automated vehicles are on the rise, and in the not-too-distant future, there will be no human drivers. In the Traffic Signs Recognition project, you’ll discover how software can use a picture as input to recognize the type of traffic sign. The German Traffic Signs Recognition Benchmark dataset (GTSRB) is used to train a Deep Neural Network that can identify the class of a traffic sign. A simple graphical user interface (GUI) to communicate with the application can also be created. Python can be used.

Source Code – Traffic Sign Detection , Traffic Sign Detection Using Capsule Networks , and Traffic Sign Recognition

4.Project on recommendation System for Films

In this data science project, the language R can be used to generate a machine learning-based movie recommendation. A recommendation system uses a filtering procedure to send forth suggestions to users based on other users’ interests and browsing history. If A and B enjoy Home Alone and B enjoys Mean Girls, it can be recommended to A; they may enjoy it as well. Customers will be more engaged with the platform as a result of this.

Source Code – Recommendation System for Films

5. Project on Breast Cancer Classification

Breast cancer cases have been on the rise in recent years, and the best approach to combat it is to detect it early and adopt appropriate preventive measures. To develop such a system with Python, the model can be trained on the IDC(Invasive Ductal Carcinoma) dataset, which provides histology images for cancer-inducing malignant cells. Convolutional Neural Networks are better suited for this project, and NumPy, OpenCV, TensorFlow, Keras, sci-kit-learn, and Matplotlib are among the Python libraries that can be utilized.

Source Code – Breast Cancer Risk Prediction , Breast Cancer Classification , and Breast Cancer Classification Topics

A thorough insight about data science, its importance, and the data science projects for beginners and final years are discussed. All of these data science projects’ source code is available on Github. So get started right away and create a Data Science project. Follow the steps from beginner to advanced, and then move on to other projects.

Q. How do you get ideas for data science projects?

The ideas for data science projects can be obtained by following these simple tips:

  • Attending networking events and mingle with people.
  • Make use of your interests and hobbies to come up with new ideas.
  • In your day job, solve problems
  • Get to know the data science toolbox.
  • Make your data science solutions.

Q. What projects do data scientists work on?

There are four different types of projects on which data scientists work:

  • Projects to cleanse up data
  • Projects involving exploratory data analysis.
  • Projects involving data visualization
  • Projects involving machine learning

Q. What projects can I do with R?

The following are the list of projects that can be done using R:

  • Project on Sentiment Analysis 
  • Project on Uber data analysis
  • Project on Movie recommendation systems
  • Project on Customer segmentation
  • Project on Credit card fraud detection
  • Project on wine preference prediction

Q. How do you contribute to open source data science projects?

There are numerous motivations to contribute to an open-source project, including:

  • To make the software, you use every day better
  • If you require a mentor, you should look for one.
  • to get creative knowledge
  • to demonstrate your abilities
  • To learn a lot more about the software you’re working with
  • To improve your reputation and advance your career

Q. How do I start a data science from scratch?

To start the data science journey from scratch, you should follow these steps mentioned below:

  • Learn Python
  • Learn the fundamentals of statistics and mathematics
  • Learn Data analysis using Python
  • Learn machine learning and start doing projects

Q.  How do you put a data science project on your resume?

Projects can be stated as accomplishments below a job description on a resume. Projects, Personal Projects, and Academic Projects can all be listed in a distinct section. Academic work should be listed in the education portion of the resume. You can also make a CV that is focused on a certain project.

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Data science projects for resumes

Are you wondering whether you should work on a data science side project to enhance your resume? Or maybe you have already decided that you want to work on a side project, but you are looking for advice on what type of project you should pursue? Either way, we have the answers that you are looking for!  In this article, we discuss everything you need to know about data science side projects and the role they play in enhancing your resume. 

We start off by explaining why data science projects are useful for resume building. After that, we walk through the steps you need to take to build out your projects and give pointers on where to focus your attention. Finally we discuss what types of applicants benefit most from having data science side projects on their resumes.  The advice provided in this  article  is broad enough that it is  applicable  for all data professionals ranging  from  data analysts to machine learning engineers. 

Competencies you might want to display when woking on data science projects for resumes.

Why work on data science side projects

  • Add new skills to your resume . The first reason that you should work on data science side projects and build out a data science portfolio is to learn new skills. Are you an analyst who primarily works in R but is looking to transition to Python? Are you a data scientist who wants to be able to put time series analysis on your resume? There is no better way to learn new skills than to dive in and get hands-on experience. Once you feel comfortable with the new tool, you can add it to the skills section of your resume. 
  • Demonstrate competencies with real examples . Beyond just being able to add new skills to your resume, the main reason that having side projects listed on your resume is impactful is because you can provide actual code and documentation that proves that you do have the skills listed in your resume. Providing links to complex Python projects you have created with real code is much more persuasive than just saying that you would rate yourself as an advanced Python coder. 
  • Prove that you are an independent learner . Finally, having side projects on your resume demonstrates that you are able to learn independently and you are eager to learn new skills. These are  qualities  that hiring  managers  look for, particularly in more junior candidates and career changers. 

Data science competencies for resumes

So what kind of competencies can you demonstrate on your resume using data science projects? Here are some examples of competencies you can demonstrate using side projects. 

  • Data analysis & visualization . The first competency that data science projects and portfolios can help to demonstrate is general data analysis and data visualization skills. If you want to focus on this competency, you should focus on defining good metrics, checking data integrity, and creating beautiful plots that make complex concepts easy to digest. 
  • Machine learning & statistics . A second competency that you can demonstrate by including data science projects on your resume is machine learning and statistics. Whether you want to demonstrate your proficiency in hypothesis testing or learn more about deep learning, all you need to do is choose an appropriate dataset and code up an analysis . If you are looking for a little bit of a challenge, try working on a project that involves time series, network, text, or image data. 
  • Software engineering . A third competency you can demonstrate with data science projects is software engineering skills. If you want to show off your software engineering chops, you do not necessarily need to work on a project that involves complex machine learning models. Just focus on writing well structured,  modular code that is  version  controlled and  well tested.
  • Languages & tools . Finally, if you want to demonstrate your proficiency with a certain language or tool then you can do that with data science projects on your resume. Some common examples of tools that you can demonstrate your proficiency in with data science projects are Python, R, Java, Spark, SQL, Git, Mlflow, Docker, Flask, Pytorch, Tensorflow, AWS, and CI/CD tools. 

Building data science projects for resumes

What steps do you need to go through in order to create a data science project for your resume? Here are the steps you need to go through to build a data science project for your resume. 

  • Decide what competencies to focus on . This is probably the most important step of the process. Before you work on a data science side project for your resume, you should make sure to decide what specific competencies you want to demonstrate with your project. Most people do not put much thought into this step of the process, but the competencies you choose should inform the dataset that you choose and the type analysis you run, not the other way around.
  • Data analysis & visualization . If you want to demonstrate your competency in data analysis and visualization then you are better off picking a real world dataset that is not perfectly clean. This way you can demonstrate your ability to identify issues with data quality and clean data. You should also think about what visualizations you might want to produce and choose your data set accordingly. For example, if you want to create a heat map that shows geographical trends in data then you should make sure to choose a dataset with geographical variables. 
  • Machine learning & statistics . If you want to demonstrate your capabilities with machine learning and statistics, then you should think about what kind of modeling you want to do. If you are new to the field, then we recommend choosing a tabular dataset that has simple numeric and categorical variables. If you do choose to work with a tabular dataset, we recommend choosing a real world dataset that needs some cleaning. If you have already done a project with tabular data and want to learn something new, you can look for unstructured data like text or image data. 
  • Software engineering . If you want to shop off your software engineering skills then it is not as important to find a messy dataset that needs a lot of cleaning. In fact, it may be better to use a clean dataset so that you can focus more of your effort on writing clean code and using model deployment tools. 
  • Languages & tools . If you want to show off your proficiency in a specific language or tool, the type of dataset you want will depend on the kind of tool you want to use. If you want to show off your proficiency using Python and pandas to manipulate data then you should choose a messy real world dataset. If you want to get practice using flask for model deployment then you are in the clear to use a clean, pre-sanitized dataset. 
  • Find a question to answer . After you choose the dataset you want to work with, you need to find a question to answer with your data. Again, the competencies that you are focusing on should inform the type of question you want to ask.  If you want to demonstrate your competencies in software engineering or a process-related tool then the question you ask is not as important. In this case, it is okay to use a dataset that has an obvious question associated with it and just answer that obvious question (ex. the titanic dataset where the obvious question is whether a passenger  lived  or died). If you want to demonstrate your competency in data analysis or modeling tabular data, you should try choosing a unique question that you thought of yourself. This demonstrates that you have the data awareness to be able to look at a dataset and determine what interesting questions can be answered with that data. The question you choose  should provide valuable and actionable insights to either yourself or a hypothetical company that might work with this kind of data. 
  • Analyze the data . After you choose a question to answer, it is time to analyze the data and answer your question. This step will look different for every project so we will not go into too much detail here. 
  • Document your process . After you have answered your question, you should document your process. This is a step that is sometimes overlooked, but it is very important. Hiring managers will not spend a long time looking at your personal projects, so it needs to be clear to them from a glance what each project is and what competencies you are trying to prove. At bare minimum, you should write up a short introduction that clearly states what dataset you are using, what question you are answering, why the answer to that question provides value (if applicable), and what competencies you are demonstrating with this project. Do not just assume that hiring managers will browse through your project and see that you are trying to demonstrate your proficiency in a certain area. Specifically stating the competencies you are trying to demonstrate will help them determine what parts of your code and analysis to focus on. 

Who are data science projects most useful for?

Having data science projects on a resume will be more helpful for some types of candidates than others. So what groups of people can benefit most from having data science projects on their resume? 

  • Junior candidates . Data science projects on resumes are generally most helpful for junior to mid level candidates where there is more of an emphasis on technical skills and execution. As candidates become more senior, there is more emphasis on interpersonal skills that are not as easy to demonstrate with data science projects on resumes. Additionally, more senior candidates are likely to have more work-related projects on their resumes that they can talk about so they do not benefit as much from having side projects on their resumes. This is not to say that data science projects are not useful for more senior candidates, especially candidates that are aiming to demonstrate highly specialized skills. Junior and entry level candidates that do not have many work-related projects on their resumes will just get more bang for their buck. 
  • Career changers . Data science projects on resumes are also useful if you are in the process of changing careers or fields. Even if you are just trying to make a small jump from an analytics role where you mostly work on reporting and metric definition to a role that involves more machine learning and modeling, side projects can provide you with valuable hands-on experience with new tools that you may not have the opportunity to use at your day jobs. 

Where to display data science projects

Where should you display your data science projects after you have completed them? Here is some advice on where to display your data science projects.

  • On your resume . Of course if you are working on data science projects with the intention of enhancing your resume, you should display your data science projects on your resume. In general, we recommend having a separate section for side projects called something like “personal projects” rather than lumping your projects into a general experience section.  But how much room should you dedicate to personal projects? That depends on what previous experience you have and whether you have work-related projects that demonstrate your data science skills. If you do not have many work-related projects to show off, then you can include a few bullet points per project for the personal projects on your resume. If you have a few work-related projects and you are not changing fields then we recommend only including one high level bullet point per project to leave more room for your work projects. 
  • Github . Beyond listing your projects on your resume, you should also make your code available in a publicly available repository. The easiest way to do this is to upload your code to GitHub. Along with your code, you should upload a file that describes your project and what its goals were. 
  • Personal website . If you have a personal website, then you may choose to make your code and documentation available there rather than on GitHub. 

Tips for data science projects on resumes

What other tips do we have for creating data science projects for resumes? Here are all of the points we haven’t touched on. 

  • It is okay to use school projects . If you are an entry level candidate, it is okay to use projects that you completed in school in your portfolio of data science projects. You already did the work, so you might as well reap some of the rewards. 
  • Navigation and documentation need to be clear . If you are including a link to a public GitHub profile that has a lot of repositories, make sure it is clear which repositories you want hiring managers to look at. Make sure to highlight those repositories and include README files that clearly describe the project and its importance. 
  • Quality over quantity . As with many things in life, you should aim for quality over quantity when you are working on data science projects for resumes. You are better off having one clean, completed, well documented project than a handful of half-completed projects with no documentation. Consider setting GitHub repositories containing half-completed projects to private when you are applying to jobs. 
  • Emphasize data over models . Even if you are working on projects to demonstrate your competency in machine learning and statistical modeling, you should spend more time focusing on your data than your models. For most jobs, you are better off using a simple, stable model that can be easily maintained than using a more complicated model that has 0.1% better accuracy. Let your projects reflect this type of thinking. And even if tiny increases in accuracy are to be desired, there is often more to gain from adding new data and features to your model than testing hundreds of parameter combinations. 

Have any other questions?

Feel free to leave us a comment if you have any general questions about creating data science projects to enhance your resume and build your skillset. 

If you are looking for a mentor to assist you with building a data science project for your resume, feel free to reach out to us at [email protected]! We can help you select an idea for your project, plan out a roadmap, and find solutions for difficult problems that are blocking your progress.  Note that we charge an hourly personal career consulting rate for these services. 

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Data Science Projects

Data Science Projects: Top 5 Projects For A Stronger Resume

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Data Science Projects

In today’s competitive job market, having a strong resume that stands out from the crowd is crucial. Data science has become one of the most sought-after fields, and employers are increasingly looking for candidates with hands-on experience in real-world projects. This is where data science projects come into play. Not only do they provide you with practical knowledge and skills, but they also demonstrate your ability to apply theoretical concepts to solve complex problems.

Data science projects offer several advantages when it comes to bolstering your resume. Firstly, they showcase your expertise in working with various data manipulation and analysis tools, programming languages, and machine learning algorithms. Employers often prioritize candidates who can demonstrate proficiency in these areas, as they indicate the practical skills needed to excel in data-driven roles.

Secondly, data science projects allow you to highlight your problem-solving abilities and demonstrate your approach to tackling real-world challenges. Employers value individuals who can think critically, analyze data, and derive meaningful insights to drive decision-making processes. By showcasing your projects, you provide evidence of your ability to handle complex datasets, implement appropriate algorithms, and deliver actionable results.

Furthermore, data science projects enable you to showcase your creativity and innovation. Employers are not only interested in candidates who can follow established methodologies but also those who can think outside the box and come up with novel solutions. Projects give you the freedom to explore different techniques, experiment with new ideas, and present unique approaches to solving problems.

Data Science Projects: Overview of the article’s content

This article will delve into the top five data science projects that can significantly strengthen your resume. Each project has been carefully selected to cover various aspects of data science, ensuring a well-rounded skill set. You can visit my GitHub page for more Data Science Projects. The projects to be discussed are as follows:

Data Science Projects: Gender Detection Using Python:

This project focuses on building a gender detection model using machine learning techniques. It demonstrates your ability to work with image data and implement classification algorithms.

Data Science Projects: Sentiment Analysis Python:

Sentiment analysis is a crucial task in natural language processing (NLP). This project will guide you through analyzing sentiment in text data using Python, showcasing your proficiency in NLP and text mining.

Data Science Projects: Spam Email Detection:

Spam emails are a nuisance, and companies are constantly seeking effective ways to combat them. This project will walk you through building a spam email detection system, highlighting your skills in data cleaning and classification.

Data Science Projects: Movie Recommendation Systems:

Personalized recommendations are ubiquitous in today’s digital platforms. This project will explore the implementation of a movie recommendation system, demonstrating your expertise in collaborative filtering and recommendation algorithms.

Data Science Projects: Credit Risk Analysis Using Python:

In the financial sector, assessing credit risk is crucial for making informed lending decisions. This project will showcase your ability to build predictive models using Python and analyze credit risk effectively.

By completing these projects, you will gain hands-on experience, develop a diverse skill set, and have tangible examples to showcase on your resume. Whether you are a beginner or an experienced data scientist looking to enhance your portfolio, these projects will undoubtedly strengthen your candidacy in the competitive data science job market.

Data Science Projects: Top 5 Projects for a Stronger Resume

Now we will discuss the Top 5 Data Science projects you can practice in order to make your portfolio stronger. There are maximum chances you will secure a job after practicing these projects and adding them to your resume. Let’s deep dive into the project details.

1: Gender Detection Using Python – Code

Gender Detection using Python is one of the most demanding skills in the data science market. Grab the code for this project and start doing practice.

Explanation of gender detection and its relevance in various applications

Gender detection is the process of determining the gender of individuals based on certain characteristics, typically using machine learning algorithms. This technique finds relevance in a wide range of applications, such as facial recognition, customer segmentation, targeted marketing, and personalized user experiences.

By analyzing features like facial structure, hair length, and clothing styles, gender detection algorithms can make accurate predictions about an individual’s gender. This information can be leveraged in multiple domains. For instance, in facial recognition systems, gender detection plays a crucial role in identifying individuals for security purposes or optimizing user experiences in various applications, such as smart devices, social media platforms, and e-commerce websites.

Description of the project’s objective and methodology

The objective of the gender detection project is to build a machine-learning model that can accurately predict the gender of individuals based on their facial images. The project involves collecting a dataset of labeled images containing the faces of males and females.

To achieve this, the methodology includes several steps. First, the dataset is preprocessed to ensure image quality and consistency. Then, feature extraction techniques are applied to capture relevant facial characteristics, such as shape, texture, and color. Next, a machine learning algorithm, such as a support vector machine (SVM) or a convolutional neural network (CNN), is trained using the labeled dataset.

During the training phase, the algorithm learns to identify patterns and features that differentiate between male and female faces. Once the model is trained, it can be used to predict the gender of new, unseen faces.

Demonstration of the implementation using Python

The implementation of gender detection using Python involves utilizing popular libraries such as OpenCV, sci-kit-learn, and TensorFlow. These libraries provide essential tools and functions for image processing, machine learning, and deep learning tasks.

In Python, the project starts by loading the dataset and preprocessing the images. This includes tasks such as resizing, normalization, and extracting facial landmarks. Next, feature extraction techniques are applied to transform the images into numerical representations suitable for machine learning algorithms.

Once the features are extracted, the dataset is split into training and testing sets. The machine learning algorithm is trained using the training set, and its performance is evaluated on the testing set. Various evaluation metrics, such as accuracy, precision, recall, and F1 score, can be used to assess the model’s performance.

Finally, the trained model can be deployed to predict the gender of new facial images. Python allows for the development of user-friendly interfaces or integration with other applications to make real-time gender predictions.

Highlighting the skills developed through this project

The gender detection project using Python hones several important skills for aspiring data scientists. Firstly, it enhances proficiency in image processing techniques, including image preprocessing, feature extraction, and facial landmark detection.

Moreover, the project strengthens knowledge in machine learning algorithms, as it involves training and evaluating models such as support vector machines or convolutional neural networks. Understanding the strengths and limitations of these algorithms is crucial in achieving accurate predictions.

Additionally, the project fosters proficiency in Python programming, as it utilizes various libraries and frameworks for image processing, machine learning, and deep learning tasks. This experience contributes to a data scientist’s coding skills and the ability to implement complex projects efficiently.

Lastly, the project emphasizes the importance of data preprocessing, dataset management, and model evaluation. These skills are essential in real-world data science scenarios, as data quality, model performance, and interpretation of results are critical factors for success.

In conclusion, the gender detection project using Python provides hands-on experience in developing an image-based machine learning model, offering valuable insights into gender prediction and its applications. The skills acquired through this project set a strong foundation for aspiring data scientists, enabling them to tackle various challenges in the field of computer vision and machine learning.

2: Sentiment Analysis Python – Code

Sentiment Analysis Python is one of the most demanding skills in the data science market. Grab the code for this project and start doing practice.

Introduction to sentiment analysis and its significance in understanding customer opinions

Sentiment analysis, also known as opinion mining, is a technique that aims to determine the sentiment or emotional tone expressed in text data. It holds significant importance in understanding customer opinions, as it enables businesses to gain insights into how their products or services are perceived in the market.

By analyzing customer feedback, reviews, social media posts, and other textual data, sentiment analysis can provide valuable information about the overall sentiment—positive, negative, or neutral—associated with a particular product, brand, or topic. This understanding helps businesses make data-driven decisions, improve customer satisfaction, and identify areas for product or service enhancement.

Explanation of the project’s aim and approach

The aim of the sentiment analysis project using Python is to develop a machine learning model that can accurately classify text documents into positive, negative, or neutral sentiments. The project utilizes a supervised learning approach, where a labeled dataset consisting of text documents with sentiment labels is used for training the model.

The approach involves several steps. First, the dataset is preprocessed to remove noise, such as punctuation, stop words, and special characters. Then, text normalization techniques like stemming or lemmatization are applied to reduce words to their base form.

Next, features are extracted from the preprocessed text, typically using techniques like bag-of-words or TF-IDF (Term Frequency-Inverse Document Frequency). These features represent the important words or phrases that contribute to the sentiment of the text.

Using the labeled dataset, a machine learning algorithm, such as Naive Bayes, Support Vector Machines, or a deep learning model like Recurrent Neural Networks (RNN) or Transformer, is trained on the extracted features. The trained model can then be used to classify new, unseen text documents into sentiment categories.

A step-by-step guide to performing sentiment analysis using Python

Here is a step-by-step guide to performing sentiment analysis using Python:

  • Data Preparation : Collect or obtain a dataset of text documents with sentiment labels (positive, negative, or neutral). Preprocess the text by removing noise, stopwords, and special characters.
  • Text Normalization : Apply text normalization techniques like stemming or lemmatization to reduce words to their base form.
  • Feature Extraction : Use techniques such as bag-of-words or TF-IDF to extract features from the preprocessed text. These features capture the important words or phrases that contribute to sentiment.
  • Split the Dataset : Split the dataset into training and testing sets. The training set is used to train the sentiment analysis model, while the testing set is used to evaluate its performance.
  • Model Training : Choose a machine learning algorithm or a deep learning model and train it on the training set using the extracted features. Popular choices include Naive Bayes, Support Vector Machines, RNN, or Transformer models.
  • Model Evaluation : Evaluate the trained model’s performance on the testing set using metrics like accuracy, precision, recall, and F1 score. This assessment provides insights into the model’s ability to classify sentiments accurately.
  • Sentiment Classification : Use the trained model to classify new, unseen text documents into sentiment categories, enabling real-time sentiment analysis.

Showcasing the impact of this project on enhancing analytical and NLP skills

The sentiment analysis project using Python has a profound impact on enhancing analytical and natural language processing (NLP) skills. Firstly, it strengthens analytical skills by requiring the collection, preprocessing, and analysis of textual data. This project provides hands-on experience in data cleaning, text normalization, and feature extraction, fostering proficiency in data preprocessing techniques.

Moreover, the project hones NLP skills by introducing techniques for sentiment analysis, such as text classification and feature extraction. It familiarizes individuals with the application of machine learning algorithms and deep learning models for NLP tasks, enabling them to gain expertise in this specialized domain.

The project also showcases the utilization of Python for data science projects, specifically in sentiment analysis. Python’s extensive libraries, such as NLTK (Natural Language Toolkit) and sci-kit-learn, facilitate various NLP tasks and machine learning implementations, solidifying skills in Python programming for data science.

Furthermore, the project allows practitioners to work with datasets related to data science projects, exposing them to the challenges and nuances of real-world data. This experience contributes to an understanding of data management, dataset exploration, and the iterative nature of model development.

In conclusion, the sentiment analysis project using Python serves as a valuable exercise for enhancing analytical and NLP skills. By performing sentiment analysis on textual data, individuals gain practical experience in data preprocessing, feature extraction, machine learning model training, and evaluation. These skills equip them to tackle diverse data science projects and leverage the power of sentiment analysis for understanding customer opinions and making informed business decisions.

3: Spam Email Detection – Code

Spam Email Detection is one of the most demanding skills in the data science market. Grab the code for this project and start doing practice.

Overview of the problem of spam emails and the need for detection

Spam emails have long been a nuisance and pose significant challenges for individuals and businesses alike. They clutter inboxes, waste valuable time, and potentially expose users to scams, malware, and phishing attacks. Spam email detection plays a crucial role in mitigating these risks and ensuring the security and efficiency of email communication.

The sheer volume and diversity of spam emails make manual identification and filtering impractical. Therefore, automated techniques are employed to detect and classify spam emails based on their content, structure, and other relevant features. By accurately identifying and filtering spam emails, individuals and organizations can improve their productivity, protect sensitive information, and enhance the overall email experience.

Description of the project’s goal and techniques used for the detection

The goal of the spam email detection project is to develop a machine learning model that can effectively classify emails as either spam or legitimate. The project utilizes a supervised learning approach, where a labeled dataset consisting of spam and non-spam emails is used for training the model.

The techniques employed for spam email detection encompass several steps. Firstly, the dataset is preprocessed to clean the emails and extract meaningful features. Common preprocessing tasks include removing stop words, performing stemming or lemmatization, and handling special characters and numerical values.

Next, feature extraction techniques are applied to represent the emails in a numerical format suitable for machine learning algorithms. These techniques may involve bag-of-words, TF-IDF, or word embeddings, which capture the frequency, importance, or contextual relationships between words.

Once the dataset is prepared and features are extracted, a machine learning algorithm, such as Naive Bayes, decision trees, or support vector machines, is trained on the labeled dataset. These algorithms learn to recognize patterns and characteristics that distinguish spam from legitimate emails.

Walkthrough of the implementation process using Python

The implementation process of spam email detection using Python involves several steps:

  • Data Preparation : Obtain or create a labeled dataset consisting of spam and non-spam emails. Preprocess the emails by removing stop words, performing stemming or lemmatization, and handling special characters and numerical values.
  • Feature Extraction : Apply techniques such as bag-of-words, TF-IDF, or word embeddings to extract features from preprocessed emails. These features capture the important words, frequency, or contextual relationships that can differentiate between spam and legitimate emails.
  • Split the Dataset : Split the dataset into training and testing sets. The training set is used to train the spam detection model, while the testing set is used to evaluate its performance.
  • Model Training : Choose a machine learning algorithm, such as Naive Bayes, decision trees, or support vector machines, and train it on the training set using the extracted features.
  • Model Evaluation : Assess the trained model’s performance on the testing set using metrics such as accuracy, precision, recall, and F1 score. This evaluation helps determine the model’s ability to accurately classify spam emails.
  • Spam Email Classification : Utilize the trained model to classify new, unseen emails as spam or legitimate, allowing for real-time spam email detection.

Emphasizing the contribution of this project to data cleaning and classification skills

The spam email detection project contributes significantly to the development of data cleaning and classification skills. Firstly, data cleaning is crucial in preparing the email dataset, as it involves removing noise, handling special characters, and applying to stem or lemmatization techniques. This experience enhances proficiency in data preprocessing and ensures the quality and consistency of the dataset.

Additionally, the project strengthens classification skills by employing machine-learning algorithms for spam email detection. By training and evaluating models, individuals gain hands-on experience in applying classification techniques to real-world problems. This includes understanding the trade-offs between different algorithms, tuning hyperparameters, and interpreting evaluation metrics.

Moreover, the project reinforces the use of Python for data science projects, offering an opportunity to leverage popular libraries such as sci-kit-learn, pandas, and Numpy. Python’s extensive ecosystem provides the necessary tools for data preprocessing, feature extraction, model training, and evaluation.

In conclusion, the spam email detection project using Python enhances data cleaning and classification skills while addressing the persistent problem of spam emails. By employing machine learning algorithms, individuals can develop accurate models to automatically identify and filter spam, contributing to a more secure and efficient email communication experience.

4: Movie Recommendation Systems – Code

Movie Recommendation System is one of the most demanding skills in the data science market. Grab the code for this project and start doing practice.

Introduction to recommendation systems and their role in personalized user experiences

Recommendation systems play a crucial role in providing personalized user experiences in various domains, including e-commerce, music streaming, and movie platforms. These systems analyze user preferences, historical data, and item characteristics to suggest relevant and tailored recommendations to individual users.

In the context of movie recommendation systems, the goal is to provide movie suggestions to users based on their preferences and similarities to other users. By leveraging machine learning algorithms and data analysis techniques, these systems can enhance user satisfaction, engagement, and discoverability of new movies.

Explanation of the project’s objective and algorithms employed

The objective of the movie recommendation system project is to develop a model that can accurately suggest movies to users based on their viewing history and preferences. The project employs collaborative filtering algorithms, which analyze user-item interactions and similarities to make recommendations.

Collaborative filtering techniques can be further divided into two main types: user-based and item-based. User-based collaborative filtering identifies similar users based on their movie preferences and recommends movies that are popular among those similar users. Item-based collaborative filtering, on the other hand, identifies similar movies based on user ratings and recommends movies that are similar to the ones the user has already rated positively.

In addition to collaborative filtering, other algorithms such as content-based filtering, matrix factorization, and deep learning models can also be utilized in movie recommendation systems to improve recommendation accuracy and diversity.

Illustration of building a movie recommendation system with Python

Building a movie recommendation system with Python involves the following steps:

  • Data Collection : Obtain or gather a dataset of movie ratings and user preferences. Popular sources include movie databases, online platforms, or publicly available datasets like MovieLens.
  • Data Preprocessing : Clean and preprocess the dataset by handling missing values, removing duplicates, and normalizing ratings if necessary.
  • Feature Engineering : Extract relevant features from the dataset that can contribute to movie recommendations. This may include movie genres, directors, actors, and user demographic information.
  • Model Training : Choose an appropriate collaborative filtering algorithm, such as user-based or item-based collaborative filtering, and train the model on the preprocessed dataset. Alternatively, other algorithms like content-based filtering or matrix factorization can be implemented.
  • Model Evaluation : Evaluate the performance of the trained model using metrics such as precision, recall, or mean average precision. This assessment provides insights into the accuracy and effectiveness of the movie recommendations.
  • Generating Recommendations : Utilize the trained model to generate movie recommendations for individual users. The recommendations can be based on user preferences, similarities to other users, or similar movies.

Highlighting the collaborative filtering and machine learning expertise gained through this project

The movie recommendation system project fosters expertise in collaborative filtering and machine learning techniques. By implementing collaborative filtering algorithms, individuals gain insights into user-based and item-based recommendations, understanding how to leverage user-item interactions and similarities to make accurate suggestions.

Moreover, the project provides hands-on experience in preprocessing movie datasets, handling missing values, and performing feature engineering to enhance recommendation accuracy. This experience strengthens data analysis skills and familiarity with data manipulation techniques in Python.

Furthermore, the project showcases the application of machine learning algorithms for personalized recommendations, allowing individuals to gain expertise in training and evaluating models. This includes understanding different evaluation metrics, hyperparameter tuning, and interpreting model outputs.

In conclusion, the movie recommendation system project using Python enables individuals to develop expertise in collaborative filtering algorithms, data analysis, and machine learning. By building a recommendation system, practitioners enhance their understanding of personalized user experiences, recommendation techniques, and the utilization of Python for data science projects.

5: Credit Risk Analysis Using Python – Code

Credit Risk Analysis Using Python is one of the most demanding skills in the data science market. Grab the code for this project and start doing practice.

Discussion on credit risk analysis and its importance in the financial sector

Credit risk analysis plays a vital role in the financial sector, where lenders and financial institutions need to assess the risk associated with extending credit to individuals or businesses. By analyzing historical data and relevant factors, credit risk analysis enables lenders to make informed decisions, manage their portfolios, and mitigate potential losses.

Accurate credit risk analysis helps financial institutions evaluate the creditworthiness of borrowers, determine appropriate interest rates, and establish lending limits. It allows lenders to identify potential defaulters, minimize non-performing loans, and maintain a healthy loan portfolio.

Explanation of the project’s aim and the predictive models used

The aim of the credit risk analysis project is to develop predictive models that can assess the creditworthiness of borrowers based on historical data and relevant features. The project utilizes various predictive modeling techniques, including logistic regression, decision trees, or ensemble methods such as random forest or gradient boosting.

These predictive models analyze historical data containing information about borrowers’ characteristics, credit history, income, employment status, and other relevant factors. By training the models on this data, they learn to identify patterns and relationships that indicate whether a borrower is likely to default or repay their loans.

Overview of implementing credit risk analysis using Python

Implementing credit risk analysis using Python involves the following steps:

  • Data Collection and Preprocessing : Gather a dataset containing historical credit data, including borrower attributes, loan information, and repayment outcomes. Clean the dataset by handling missing values, removing duplicates, and transforming categorical variables into numerical representations.
  • Feature Selection and Engineering : Select relevant features that have a significant impact on credit risk analysis. These may include credit scores, debt-to-income ratios, employment stability, and past delinquencies. Additionally, create new features or derive meaningful insights from existing features to enhance the predictive power of the models.
  • Model Training and Evaluation : Split the dataset into training and testing sets. Train the predictive models on the training set using algorithms such as logistic regression, decision trees, or ensemble methods. Evaluate the models’ performance on the testing set using metrics such as accuracy, precision, recall, or area under the ROC curve.
  • Model Deployment and Risk Assessment : Once a satisfactory model is identified, deploy it to assess the credit risk of new loan applications. The model will predict the likelihood of default or repayment, enabling lenders to make informed decisions based on the risk appetite and policies of their institution.

Showcasing the understanding of risk assessment and financial analytics acquired through this project

The credit risk analysis project fosters a deep understanding of risk assessment and financial analytics. By implementing predictive models, individuals gain insights into evaluating creditworthiness, identifying risk factors, and assessing potential defaulters. This project provides hands-on experience applying statistical and machine-learning techniques to real-world financial data.

Moreover, the project emphasizes using Python for data science projects, specifically in credit risk analysis. Python’s extensive libraries, such as pandas, sci-kit-learn, and Numpy, facilitate data preprocessing, feature selection, model training, and evaluation. The project strengthens proficiency in Python programming for data science and equips individuals with the skills necessary to tackle other data analysis projects.

Furthermore, the credit risk analysis project showcases the application of data science in the financial sector, highlighting the importance of leveraging historical data and predictive models to make informed decisions. This experience enhances my understanding of risk management principles, credit risk assessment, and the regulatory landscape surrounding lending practices.

In conclusion, the credit risk analysis project using Python enables individuals to develop expertise in risk assessment, financial analytics, and the utilization of Python for data science projects. By analyzing historical credit data, building predictive models, and evaluating risk, practitioners gain valuable insights into creditworthiness assessment and enhance their skills in data analysis for the financial sector.

Recap of the top 5 data science projects for a stronger resume

In conclusion, the article highlighted the top 5 data science projects that can significantly strengthen a resume and showcase valuable skills to potential employers. These projects include:

  • Gender Detection Using Python: Demonstrating proficiency in image processing and machine learning, this project showcases the ability to classify and identify gender based on facial features.
  • Sentiment Analysis Python: By analyzing and understanding customer opinions through sentiment analysis, this project highlights expertise in natural language processing and text analysis.
  • Spam Email Detection: Showcasing data cleaning and classification skills, this project focuses on developing models to automatically detect and filter out spam emails, improving email communication efficiency.
  • Movie Recommendation Systems: By leveraging collaborative filtering and machine learning algorithms, this project demonstrates the ability to provide personalized movie recommendations based on user preferences.
  • Credit Risk Analysis Using Python: Highlighting risk assessment and financial analytics expertise, this project involves building predictive models to assess the creditworthiness of borrowers.

Reinforcement of the skills and knowledge gained through these projects

These data science projects provide hands-on experience and reinforce various skills and knowledge. By working on these projects, individuals develop proficiency in Python programming for data science, data preprocessing, feature engineering, model training, and evaluation. Moreover, these projects enhance skills in machine learning algorithms, data analysis, and interpretation of results.

Furthermore, these projects contribute to the development of specific domain expertise. For instance, the sentiment analysis project focuses on natural language processing, while the credit risk analysis project emphasizes risk assessment in the financial sector. Each project equips individuals with practical skills and knowledge that are highly valuable in the data science field.

Encouragement to continue exploring and working on data science projects

Lastly, the article encourages readers to continue exploring and working on data science projects. Engaging in projects available on platforms such as GitHub, Kaggle, or data science capstone projects allows individuals to further hone their skills, collaborate with the data science community, and gain exposure to real-world datasets and challenges. By continuously working on data analysis projects, individuals can expand their portfolios, stay updated with industry trends, and demonstrate their commitment to professional growth in the field of data science.

In conclusion, the top 5 data science projects discussed in this article serve as excellent additions to a resume. Showcasing a diverse range of skills and knowledge.

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New York Institute of Technology

Data Science Projects: Building a Strong Portfolio

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In your data science career , you'll find that a lot of your background and reputation is based on the technical skills and soft skills that you can demonstrate in real-world situations. Potential employers will want to know that you have a proven track record of doing what your resume purports that you do: data analysis, data visualizations, programming languages , deep learning, and other relevant skills. One of the best ways to demonstrate how you can use those skills is through a data science portfolio.

With an estimated 17,700 data science job openings through 2023, more and more companies need to hire data scientists who can positively contribute to business decisions. 1 During the application process, a strong data science portfolio can get a hiring manager’s attention and put your resume at the top of the pile. The projects you include can help answer important questions: Can you be successful in a data science role at their company? Do you understand the business problems that data science can help solve and how to approach them?

Whether you’re looking for your first job or dream job in data science, portfolios matter. Regardless of your niche—artificial intelligence, big data, or machine learning—choosing a variety of interesting projects for your portfolio can make you stand out from the crowd.

In this post, we guide you through the process of creating an impressive data science portfolio, including planning projects, communicating their value and impact, and showcasing them on the right online platforms.

Selecting Relevant Data Science Projects

Compiling your data science portfolio is your chance to display how you have applied data science sources, tools, and techniques to solve real-world problems. If you’re finishing a master’s or Ph.D. in data science, including capstone projects or a thesis is entirely accepted and encouraged. However, any projects you include should tell a compelling yet concise story about your value to an organization. Make sure to select your best portfolio projects to showcase.

Rojesh Shikhrakar, who helps data science students upskill in their career path , advises selecting data science portfolio projects that closely align with your target industry and desired role. 2 For example, if you want to work in e-commerce and you’re applying to be a data analyst, include projects that show your ability to analyze customer behavior, product performance, or sales trends. 2

William Chen, a Data Science Manager at Quora, adds that the most important criteria is whether the project details have interesting datasets and results. 2 Besides the results you achieved, it’s equally important to display how you have worked with teams to address challenges or incorporated feedback from mentors and industry experts. 2

Project types to include: 2,3

  • Code-based: Replicate real-world data science projects by taking a dataset and solving a problem around it. This could involve scraping and analyzing a dataset, training a model, or analyzing data on a trending topic, such as a news story
  • Content-based: Demonstrate your communication and writing skills. Write blog posts or record podcasts where you break down data science topics for non-technical audiences
  • Capstone: Integrate, synthesize, and display all your data science knowledge. Some options include analyzing satellite images or historical weather data for storm patterns, as well as exploring solutions to fake news and misinformation

Project Planning and Scope

Crafting a well-defined and impactful idea is the first step in any industry-level data science project. Experts recommend choosing a domain of interest where you have some prior experience. 4 This will simplify the process of identifying a business problem or task you want to address using data science techniques and tools. 4

To define the project area, conduct market research, review case studies, and talk with experts in the field to determine what challenges industry leaders face. If you align your data science project with these challenges, then you will learn more about the technology stack used by these companies and boost your chances of employment. 4

Data Collection: Methods and Considerations

Once the project idea is defined, it’s time to collect real-world data to answer the business questions and solve the problem at hand. First, identify real-world datasets that align with your business/research needs. Avoid well-known datasets, such as Titanic or Iris, commonly used in beginner-level or educational projects. Second, it’s important to ensure the accuracy of the data, either as it's collected or as part of data preparation. 5

To locate diverse datasets, these resources are a good place to start: 4

  • Awesome Public Datasets GitHub Repo
  • Google Datasets
  • Kaggle Competitions
  • Reddit R Datasets
  • UCI Machine Learning Repository

Exploratory Data Analysis (EDA)

Exploratory Data Analysis is a technique used to improve the reliability and performance of machine learning models. By using this approach, you can identify data quality issues, such as missing values, outliers, and other problems with your data. 6 For example, if you’re dealing with an emotional stock like Tesla and wish to build a model that can predict the stock market direction, you might want to consider as many data sources as possible. 7 Exploring data from Google Analytics and Twitter Insights could reveal hidden patterns and relationships, which can ultimately drive better business decisions. 7

Applying Data Science Techniques and Tools

According to the Data Science Institute, organizations who have failed to invest in data science roles now realize that this expertise can do wonders for their business. 8 From the use of algorithms to data visualization, data scientists know how to leverage tools and processes to collect, extract, and analyze data in a way that leads to greater business efficiency and innovation.

Techniques you can showcase include:

  • Feature Engineering: Transform raw data into features that can be used in supervised learning. This could involve designing and training better features, which can range from the color of an object to sounds 9
  • Machine Learning and Modeling : Use algorithms to identify patterns or make predictions on unseen datasets
  • Statistical Analysis: Draw meaningful conclusions from raw and unstructured data , which often facilitate business-decision making
  • Data Visualization: Effectively communicate insights from your data to different stakeholders using tools, such as Tableau or Power BI 10

Real-World Problem Solving

Link your projects to real-world applications within specific industries. Showcase your ability to address challenges and solve problems that resonate with your target audience. Then, demonstrate the practical impact of your projects by providing actionable recommendations. Communicate how your work contributes to informed decision-making.

Collaboration and Open-Source Contributions

Contributing to open-source projects can make transitioning from academia to industry easier. This will help you connect with other top data engineers and scientists in the data science community. Take the case of David Robinson, Chief Data Scientist at Data Camp. As he completed a Ph.D. program, he worked on open-source development and regularly contributed to the programming site Stack Overflow, which provided evidence of his expertise. 2 An engineer found Robinson’s concise explanation of beta distribution online and was so impressed that he sent a job lead to Robinson via Twitter. 2 A few interviews later, Robinson was hired. 2

Presentation and Portfolio Building

Before publicly sharing your work, take a look at the portfolio website of other data scientists to draw inspiration and see how they approach the process. You'll likely notice that they craft a compelling story and engaging description for each of their projects. You should do this as well by clearly articulating the business problem you were given, your unique approach, and the impact you made. Use data visualization and storytelling techniques to make your projects memorable.

Also, be sure to share some personal anecdotes or information that explain why and how you began your data science journey. With all of the technical skill and expertise that you'll bring, hiring managers will also want to know that you have a passion for the job and can work well with others.

Then, choose the right platforms to start building a presence. DagsHub, which caters to data scientists who want to host machine learning projects, allows uploads of Jupyter notebooks, Python code, and other documentation. 4 GitHub is ideal for contributing to open-source data science projects and you can link to it from your resume, portfolio, and LinkedIn profile. Communities such as LinkedIn, Quora, and Medium provide spaces for sharing data science knowledge and expertise with others. 2,4 You may even create a personal website on a site like Wordpress or SquareSpace.

Whatever platforms you choose, make it easy for hiring managers to find and explore your work by adding links to your projects. Creating projects and updating your portfolio takes time, but the increased visibility and potential career opportunities are worth the effort.

Get a Competitive Edge in Data Science

Invest in your future by pursuing an Online Data Science, M.S. at New York Institute of Technology. Top faculty designed our cutting-edge online courses to prepare you for the evolving demands of the data science job market. You’ll develop sophisticated technical and leadership skills to make you stand out from other data science applicants and build a strong data science foundation to maximize your earning potential. You can further distinguish yourself by taking specialized electives in artificial intelligence, information security, and other areas.

Get in touch with an admissions outreach advisor today to learn more.

  • Retrieved on December 26, 2023, from bls.gov/ooh/math/data-scientists.htm
  • Retrieved on December 26, 2023, from towardsdatascience.com/how-to-build-a-data-science-portfolio-5f566517c79c
  • Retrieved on December 26, 2023, from samchaaa.medium.com/data-science-capstone-ideas-and-how-to-get-started-46d607194ce2
  • Retrieved on December 26, 2023, from linkedin.com/pulse/building-industry-level-data-science-projects-guide-karimi-christine/
  • Retrieved on December 26, 2023, from techtarget.com/searchcio/definition/data-collection
  • Retrieved on December 26, 2023, from towardsdatascience.com/can-we-predict-teslas-rise-fall-using-ai-2a892ba1aee1
  • Retrieved on December 26, 2023, from linkedin.com/advice/0/what-role-exploratory-data-analysis-cleaning-machine
  • Retrieved on December 26, 2023, from usdsi.org/data-science-insights/data-science-skills-vs-tools-what-matters-the-most-for-data-scientists
  • Retrieved on December 26, 2023, from kdnuggets.com/2022/11/data-science-projects-help-solve-real-world-problems.html
  • Retrieved on December 26, 2023, from geeksforgeeks.org/what-is-feature-engineering/

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How to Mention Data Science Projects in a Resume

Aman Kharwal

  • June 22, 2023
  • Machine Learning

Mentioning projects in a resume is of the utmost importance as it allows potential employers to assess your practical skills and abilities. For Data Science jobs, it is essential to mention projects in your resume the right way, as it can significantly increase your chances of landing a desirable position. So, if you want to know how to mention projects in a Data Science resume, this article is for you. In this article, I’ll take you through a step-by-step guide on how to mention Data Science projects in your resume .

What Kind of Projects Should You Mention in Your Data Science Resume?

When deciding which projects to mention in your resume, it is essential to focus on those that are directly related to data science and demonstrate your expertise in the relevant fields. Consider projects that showcase your proficiency in data analysis, statistical modelling, machine learning algorithms, data visualization, and other relevant skills. Additionally, prioritize projects that match the specific job requirements and industry you are targeting, as this will further underscore your suitability for the position.

As a fresher, you should mention projects that can show:

  • your ability to work with data
  • your ability to understand the business problem behind the problem statement you are solving in your project
  • your proficiency in data analysis, statistical modelling, machine learning algorithms, data visualization, and other relevant skills
  • and your ability to summarize and end the project with a solution

And as an experienced professional, you should never mention the projects that helped you get your first job a year ago (or maybe years ago). Forget those projects. Now you are experienced, you should mention projects that can show:

  • your understanding of the domain you have already worked
  • your expertise in using various data science tools
  • your expertise in using big data tools
  • your expertise in solving business problems

Now Here’s How to Mention Data Science Projects in a Resume

When mentioning your projects in a resume, it’s crucial to provide concise yet comprehensive information that effectively conveys the key aspects of each project.

Start by providing a clear, descriptive title for the project, followed by a brief summary or goal statement that describes the purpose and scope of the project. Then, describe the methodologies and techniques employed, emphasizing innovative or unique approaches you have used. Highlight the datasets used, the data preprocessing techniques applied, and the algorithms or models implemented.

In addition, it is essential to highlight the outcomes and results of your project. Quantify the impact of your work by mentioning key metrics, such as accuracy, precision, recall, or other performance indicators relevant to your project topic. If applicable, highlight any improvements or advances you have made over existing methods or previous benchmarks.

An Example of How to Mention Data Science Projects in a Resume

To look for a concrete example, consider the project description of my Data Science project below.

Project Title: Development & Deployment of Content-Based Recommendation Systems

  • Summary: Led the development and successful deployment of a content-based recommendation system at  Statso.io  and its subsidiary platform. The project focused on providing accurate and personalized content recommendations based on users’ reading preferences, resulting in increased user engagement, the increased website traffic and improved content relevance.
  • Methodology: Used a content-based filtering approach to analyze user reading preferences and recommend content accordingly. Developed advanced models leveraging natural language processing techniques to identify content features and match them to user interests.
  • Results: The recommendation system got exceptional results, including a remarkable 35% increase in monthly website traffic and a 25% increase in the average time spent per user on the platform. Additionally, the recommendation system improved the click-through rate for recommended content by 30%. The recommendation system also contributed to a 20% reduction in user churn, indicating better user retention.
  • The project’s success highlights the enhanced user engagement, higher traffic, and increased content relevance resulting from the recommendation system’s deployment.

By incorporating such detailed project descriptions into your resume, you effectively showcase your expertise, problem-solving skills, and the value you can bring to potential employers as a data science professional.

So while mentioning projects in your Data Science resume, start by providing a clear, descriptive title for the project, followed by a brief summary or goal statement that describes the purpose and scope of the project. Then, describe the methodologies and techniques employed, emphasizing innovative or unique approaches you have used. Highlight the datasets used, the data preprocessing techniques applied, and the algorithms or models implemented. I hope you liked this article on how to mention Data Science projects in a resume. Feel free to ask valuable questions in the comments section below.

Aman Kharwal

Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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Title: learnable prompt as pseudo-imputation: reassessing the necessity of traditional ehr data imputation in downstream clinical prediction.

Abstract: Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks to directly model the patient's health status based on EHR. Existing deep learning training protocols require the use of statistical information or imputation models to reconstruct missing values; however, the protocols inject non-realistic data into downstream EHR analysis models, significantly limiting model performance. This paper introduces Learnable Prompt as Pseudo Imputation (PAI) as a new training protocol. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all EHR analysis models. Additionally, our experiments show that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, in a real-world application involving cross-institutional data with zero-shot evaluation, PAI demonstrates stronger model generalization capabilities for non-overlapping features.

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arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Everyone says trees are good for us. This scientist wants to prove it.

Nearly 8,000 trees and shrubs in southern louisville and health data from about 500 residents fill out the urban science experiment.

data science project on resume

Aruni Bhatnagar looked up.

“This tree right here, it’s got a lot of good leaves so you can stick a lot of air pollutants in it,” Bhatnagar, a cardiology researcher, said as he gestured toward a magnolia tree on the U.S. Capitol grounds.

Bhatnagar, silver haired and wearing a black turtleneck, was in D.C. for the World Forum on Urban Forests to speak about his $15 million Green Heart Louisville project — an initiative aimed at showing a causal connection between greenness and human health, and a potential model for U.S. cities looking to measure the effects of their tree planting.

In 2018, Bhatnagar, a University of Louisville medical school professor, decided that he wanted to “do something” about air pollution in Louisville, which has repeatedly earned failing grades for air quality from the American Lung Association. His contribution, he decided, would be to find the connection between trees and better heart health using the gold standard for evidence: clinical trials.

“The idea is to learn to examine everything, no matter how obvious they may seem,” he says.

Bhatnagar is well aware of the massive forest of urban tree research available, but much of it involves observational health studies, in which scientists measure potential correlations between urban trees and residents’ health.

“What I thought was we really don’t know if trees are beneficial for health,” Bhatnagar said.

To get beyond that, he proposed the Green Heart Louisville initiative, which launched in 2018. Over time, contractors and volunteers have planted nearly 8,000 trees and shrubs in a cluster of lower-to-middle-income neighborhoods in southern Louisville and measured health data from nearly 500 residents.

Today, the project involves more than 50 researchers, four universities, four nonprofit groups, five state and local government agencies, and the U.S. Forest Service. It began as a collaboration between Bhatnagar; Louisville philanthropist Christina Lee Brown; former Louisville mayor Greg Fischer; and Ted Smith, Louisville’s then-chief innovation officer. Roughly $9 million from the Nature Conservancy got things moving. The National Institute of Environmental Health Sciences provided another $3 million, and local donors contributed $3 million as well.

The work is focused in neighborhoods that — like many poor urban areas — have fewer trees compared with more affluent parts of the city. The neighborhoods are mixed racially and ethnically: 54 percent White, 29 percent Black and 11 percent Hispanic. A highway runs right through the areas — providing an unhealthy baseline of air pollution.

Bhatnagar collects an almost-obscene amount of data that includes blood panels, urine, hair samples, wastewater runoff, air pollution samples, soil and leaf samples, bat sounds, LiDar scans, temperature and humidity measurements, crime data, psychological surveys and sleep surveys. It is all being parsed, and relationships are starting to emerge, he said.

Among the tantalizing hypotheses Green Heart is testing: whether trees filter air pollution that can stiffen human arteries. Another is whether trees reduce stress and improve sleep by buffering noise. Some trees seem to be better at filtration than others — evergreens, for instance, filter air throughout the year and those with needles absorb harmful pollutants more efficiently than broad-leafed trees.

Another hypothesis is that trees release a suite of chemicals into the air that reduce blood pressure and stress . Bhatnagar has seen these chemicals’ metabolites show up in urine samples at higher concentrations where people have more exposure to trees and other greenery.

Cities around the country are set to receive funding from the Inflation Reduction Act this year to plant trees, and already many local governments spend millions every year on planting and maintaining trees. Cities often maintain detailed records of size and health of every tree for every block, and LiDar scans from aircraft paint a more complete model of the urban tree canopy. Medical professors also study green spaces and trees’ effects on aging . And psychologists have observed that stress levels and depressive states are less in greener areas of the city.

But Bhatnagar’s research will add new, concrete health data. “There’s this idea that we should just plant some trees and things will be better,” Bhatnagar said, “but who, what, where, and how? These are the questions.”

“We can’t just go, ‘Oh, look, this is greener place and people are happier’ because most places that are greener are richer, etc.,” he said, noting that other factors could play a role in overall health.

In a 2018 literature review , Forest Service research social scientist Michelle Kondo found that — before Green Heart — there had been few randomized controlled trials looking into the effects of greening interventions on human health or safety.

Bhatnagar’s study — planting mature trees throughout a neighborhood and measuring many variables — is the first of its kind, said Kondo, who collaborates with Green Heart in her job. “Almost all of the studies being done four years ago were what you call observational and cross-sectional,” she added.

Bhatnagar’s study is famous in urban forestry circles, said Kathleen Woolf, a research social scientist at the University of Washington, who has also reviewed the urban tree and health literature.

Green Heart “is very important because it is that very intentional, systematic introduction of an intervention on a neighborhood scale, in a community that has been identified to have numerous health challenges. And so it’s in a sense, following the random controlled trial model,” Woolf said.

In conversation, Bhatnagar likes to reference the Bradford Hill criteria of causation which states, among other things, that a cause must precede an effect in time and there must be a dose-response relationship.

Bhatnagar wanted this deeper level of causal understanding. He wanted clinical trial experimental data, in which “doses” of trees were introduced to a population with a nearby control group and extensive measurements over time.

Establishing cause would not be easy. Bhatnagar needed expensive, mature trees — $1,000 each — to have a measurable effect, willing participants, a small army of scientists gathering data and another legion of researchers to analyze it.

Scientists, he said, can pursue research in isolation, but they can also actively engage with colleagues in wide-ranging fields, share a vision with community leaders and philanthropists, speak at conferences around the world, write articles for the World Economic Forum, host a podcast and read Virginia Woolf — which, coincidentally describes Bhatnagar.

“We need to be in the world … able to synthesize ideas, to be able to create some larger vision and not to be put in your place,” he said.

So far, Bhatnagar has published a few studies about the immediate effects of introducing trees. His team has found higher air pollution near fast food restaurants, and better sleep and sense of well-being near green spaces — all interesting, but the most meaningful results are the changes over time. And Bhatnagar is finally able to see some long-term, longitudinal effects of the trees on health.

Even as he cautioned against expectations influencing Green Heart’s results, he said he expects to find positive effects on the health of the people in the target clusters compared with the control clusters.

“We think we have a strong signal that there is some health benefits in a longitudinal way, ” Bhatnagar said, noting that he has yet to have his results peer reviewed.

Cecil Konijnendijk has spent 30 years in urban forestry. As co-director of a think tank called the Nature Based Solutions Institute, he advises governments where to plant trees. And he’s firmly behind studies like Green Heart.

“That will really help us of course, not only arguing for the need for trees, but also really how to make interventions for specific outcomes and benefits,” Konijnendijk said. “We can be more targeted toward specific communities of people, targeted toward certain types of green.”

Bhatnagar is hopeful that other scientists will take note of his study and pursue similar experiments. If that happens, more scientists can be actively involved in policies and recommendations to create healthier urban neighborhoods, he said.

“Right now, we have no idea that what sort of what a healthy neighborhood looks like,” Bhatnagar said. “This is one step in that direction.”

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data science project on resume

NASA Telescope Data Becomes Music You Can Play

Since 2020, the “sonification” project at NASA’s Chandra X-ray Center has translated the digital data taken by telescopes into notes and sounds. This process allows the listener to experience the data through the sense of hearing instead of seeing it as images, a more common way to present astronomical data. Working with composer Sophie Kastner, the team has developed versions of the data that can be played by musicians. Sophie Kastner’s Galactic Center piece is entitled “Where Parallel Lines Converge.”

If you are a musician who wants to try playing this sonification at home, check out the sheet music at: https://chandra.si.edu/sound/symphony.html.

Chandra Snofication sheet music

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  24. How to Mention Data Science Projects in a Resume

    Summary. So while mentioning projects in your Data Science resume, start by providing a clear, descriptive title for the project, followed by a brief summary or goal statement that describes the purpose and scope of the project. Then, describe the methodologies and techniques employed, emphasizing innovative or unique approaches you have used.

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