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18 Data Scientist Resume Examples for 2024

• Data Scientist Resume
• Data Scientist Resumes by Experience
• Data Scientist Resumes by Role

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 18 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

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.

Data Science Student Resume

• For a splendid data science student resume, demonstrate a diverse skill set, prioritizing in-demand options (think Python, Jupyter Notebook, Pandas, Excel, SQL Server, etc.). Soft skills , ranging from teamwork and leadership to problem-solving, creativity, and adaptability are a welcome addition to your piece.

Entry-Level Data Scientist Resume

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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.
• Then, by writing a great cover letter , you give yourself room to expound on exactly how you reduced process duration as a Python data scientist.

Data Scientist Machine Learning Resume

• 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

• 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

• 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

• 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

• 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

• 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

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

Data Science Director Resume

• 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

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.

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.

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.

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.

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.

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:

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.

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.

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.

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.

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.

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.

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

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.

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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.

Connect with us:

Contact us: 9a-5p, M-F | 134 Mary Gates Hall | Seattle, WA 98195 | (206) 543-0535 tel | [email protected]

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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. 

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. 

Related articles

• How to make an entry level data science resume
• How to explain machine learning projects in resumes

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Data Science Projects for Boosting Your Resume

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.

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). 

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) :

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.

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The Junior Data Scientist's First Month

Data Scientist Resume - Sample & Guide for 2024

You’re a data scientist. You solve complex problems.

Your newest problem: writing a resume for that elusive data scientist role.

Fortunately, you’ve arrived at the best place. This guide will take you through a range of steps, so you can create a data scientist resume that gets results. 

• An example of a finished data scientist resume that works
• How to write a data scientist resume that’ll fill up your interview diary
• How to make your data scientist resume stand out [with top tips & tricks]

Before we get stuck into the data, here’s a data scientist resume example, created with our very own online resume builder :

This resume performs as well as it looks. Just follow the steps in this guide to create a data scientist resume that gets great results, just like the above example.

Besides our data scientist resume example, we've got even more resume examples for professionals in the computer science field:

• Data Analyst Resume
• Data Entry Resume
• Computer Science Resume
• Artificial Intelligence Engineer Resume
• Engineering Resume
• Software Engineer Resume
• Web Developer Resume
• Java Developer Resume

How to Format a Data Scientist Resume

Before you can reveal why you’re the best person for the job, you need to pick the best format.

Now, this is more important than it sounds.

It will allow your best attributes to ‘jump off the page’ into the recruiters' vision. 

The most common resume format is “ reverse-chronological ”, and it’s for good reason. Essentially, it allows the recruiter to immediately see the value that you provide. We recommend the majority of individuals start with this format.

The following resume formats also get our approval:

• Functional Resume – If you have strong skills, but a weak work history, then this resume format is recommended. It’s ideal for skilled scientists that don’t have a lot of experience or have gaps in their employment history
• Combination Resume – Acting as a combination of both the “Functional” and “Reverse-Chronological” formats, you can use a combination resume if you have a wealth of work experience

Once you’ve chosen your format, you need to organize your resume layout .

Use a Data Scientist Resume Template

As a data scientist, you present data in a structured way.

The same needs to happen to your resume.

However, creating a structured file isn’t an easy task!

You could use Word, but then you will have to risk the layout falling apart with every small alternation. 

Want to skip formatting issues? Use a data scientist resume template .

What to Include in a Data Scientist Resume

The main sections in a data scientist resume are:

• Work Experience
• Contact Information

Want to go a step further? You can also add these optional sections:

• Awards & Certification

Interests & Hobbies

What should you write for each section? 

Read on to learn how.

Want to know more about resume sections? View our guide on What to Put on a Resume .

How to Correctly Display your Contact Information

Now, there is no need to get creative in this section. 

The only requirement is accuracy. 

An incorrect contact section may mean the recruiter can’t contact you – disaster! 

The contact information section on your resume must include:

• Title – In this case, “Data Scientist”
• Phone Number – Check this multiple times for errors
• Email Address – Use a professional email address ([email protected]), not your childhood email ([email protected]).
• (Optional) Location - Applying for a job abroad? Mention your location.
• Ellie Branning, Data Scientist. 101-358-6095. [email protected]
• Ellie Branning, Data Scientist Whizz. 101-358-6095. [email protected]

How to Write a Data Scientist Resume Summary or Objective

It’s safe to say that recruiter’s don’t have time to dig into the data of every resume.

Instead, they scan the resume for the main points.

In fact, studies have shown that recruiters spend just a few seconds on each resume! 

So, what can you do?

You need an introduction that makes your value ‘jump off the page’.

To do this, use a resume summary or objective .

These are snappy paragraphs that go on top of your resume, just under your contact information. 

Now, this section is extremely important. This small paragraph could be the deciding factor between scoring an interview and simply having your resume dismissed.

But what is the difference between the two sections?

A resume summary is a 2-4 sentence summary of your professional experiences and achievements.

Certified data scientist with 12 years of experience for a diverse clientele. Achievements include updating data streaming processes for an 18% reduction in redundancy, as well as improving the accuracy of predicted prices by 18%. Highly-skilled in data visualization, machine learning, leadership.

A resume objective is a 2-4 sentence snapshot of what you want to achieve professionally.

Motivated data scientist with 2+ years of experience as a freelance data scientist. Passionate about building models that fix problems. Relevant skills include machine learning, problem solving, programming, and creative thinking.

So, which one is best, summary or objective?

Generally, we recommend that experienced data scientists go with a resume summary. Those who are new to the field, like graduates and career changers, would be better suited to an objective. 

How to Make Your Data Scientist Work Experience Stand Out

Recruiters need to be confident that you will do a good job for the company.

Listing your work experience is the easiest and best way to do this.

Here’s the best way to structure your work experience section:

• Position name
• Company Name
• Responsibilities & Achievements

Data Scientist

03/2016 - 05/2019

• Improved the accuracy of predicted prices by 18%.
• Coordinated a team of 16 data scientists working on 4 different projects.
• Updated data streaming processes for a 18% reduction in redundancy.

To separate your resume from the other applicants, you should talk about your best achievements, not your daily tasks. Doing so will clearly show how you can benefit the company.

Instead of saying:

“Data streaming.”

“Updated data streaming processes for an 18% reduction in redundancy.”

As you can see, the first statement doesn’t effectively convey your achievements. It shows that you streamed data, but it doesn’t show the results of your work. 

The second statement shows that you managed to reduce the redundancy numbers. Hard numbers that prove your skills – can’t argue with that!

What if You Don’t Have Work Experience?

Maybe you’re trying to break into the data science field?

Or maybe, you have already worked in the industry, but never in this specific role?

Your experience is null .

A recruiter will want data scientists that they can rely on. Whether you have job experience or not, being able to show that you have the skills is the most important factor.

If you already have proof of your data science skills, feel free to link to them in your resume.

With that said, there is still time to create a portfolio.

Here are several ways you can show your talents (and even get paid for it):

• Start freelancing.
• Offer your skills to friends and family.
• Contribute to open source projects on GitHub.
• If the above doesn’t work, become your own client! Show your skills by creating mock projects.

Are you recent data scientist graduate? Make sure to check out our student resume guide !

Use Action Words to Make Your Data Scientist Resume POP!

…are all common words that the recruiter sees time and time again.

However, you want to separate your resume from the competition, which means using power words to make your achievements stand out:

• Conceptualized
• Spearheaded

How to Correctly List your Education

Every great resume needs an education section.

But don’t worry, there is nothing too complicated here.

Simply enter your education history in the follow format:

• Degree Type & Major
• University Name
• Years Studied
• GPA, Honours, Courses, and anything else you might want to add

BSc in Statistics

University of Bath

2012 - 2016

• Relevant Courses: Probability and Statistics, Generalised Linear Models, Applied Statistics

Now, you may have some questions on this section. If so, here are the answers to some of the most frequent questions that we get:

• What if I haven’t finished education yet?

Regardless of whether you’re a data science graduate or still studying, you should mention all years studied to date

• Should I include my high school education?

The general rule is to only include your highest form of education. So, include your high school education if you don’t have a relevant degree for data science

• What do I put first, my education or experience?

Experiences are the priority, so those go first. If you’re a recent graduate, you will likely need to start with education.

Need to know more? Check out our guide on how to list education on a resume .

Top 15 Skills for a Data Scientist Resume

When it comes to the skills section, the hiring manager has seen it all before.

In fact, they need a data scientist to help with the entire pile of data scientist resumes!

You see, everyone lists all of their skills, even those that related to the job.

Your skill section should highlight your top skills in a way that is specific to the role.

Here are some of the most common data scientist skills:

Hard Skills for a Data Scientist Resume:

• Data Analysis
• Data Visualization
• Quantitative Analysis
• Machine Learning
• Mathematics
• Probability
• Programming

Soft Skills for a Data Scientist Resume:

• Critical Thinking
• Communication
• Time-Management
• Collaboration
• Data scientists frequently use tools, such as Cloudera, PERL, and OpenRefine. If there are any tools or pieces of software that you’re an expert in, include them in your skills section.

Here’s a more comprehensive list of 101+ must-have skills this year .

What Else Can You Include in a Data Scientist Resume?

We’ve now covered every essential resume section .

Is it the absolute BEST it can be?

Doing a great job with the above sections should be enough to get you shortlisted, but adding a few of the following sections could be the major factor in whether you become their new data scientist or not.

Awards & Certifications

Have you won an award for your work in a field that relates to data science?

Have you completed any courses to improve your skills and knowledge?

If you said yes to any of the above, make sure to mention them in your resume!

Don’t worry if you don’t have any awards or certificates, there a few companies that allow users to do online certifications, like Google.

• “IBM Data Science” - Coursera Certificate
• Google Certified Professional Data Engineer – GCP
• Microsoft Professional Program Certificate in Data Science
• “Deep Learning” - Coursera Certificate
• “Critical Thinking Masterclass” - MadeUpUniversity

Even though it is very unlikely to need a second language, you may want to add a small languages section to your resume. 

You see, being able to speak a second language is always an impressive skill to a hiring manager. 

Rank the languages by proficiency:

• Intermediate

Now, you may be wondering, “why would a recruiter need to know about my love for kayaking?”

Well, your hobbies reveal more about who you are as a person.

A hobbies section is an easy way to add personality to your resume, so add one if you have the space.

Here’s which hobbies & interests you may want to mention.

Include a Cover Letter with Your Resume

Here the thing –

Cover letters still play an important role during the application process.

They provide a number of benefits, but the main reason for using a cover letter is to show the recruiter that you care about working for their company.

To create a winning cover letter, we must use the correct structure. 

Here’s what we recommend:

You should complete the following sections:

Personal Contact Information

Your full name, profession, email, phone number, location, and website (or Behance / Dribble).

Hiring Manager’s Contact Information

Full name, position, location, email.

Opening Paragraph

It’s no secret that hiring managers skim through resumes and cover letters. As such, you need to hook the reader within the first few sentences. Use concise language to mention:

• The position you’re applying for
• Your experience summary and best achievement to date

Once you’ve sparked the reader’s interest, you can get deeper into the following specifics:

• Why you chose this specific company
• What you already know about the company
• How your skills relevant for the role
• Which similar industries or positions have you worked in before

Closing Paragraph

Don’t just end the conversation abruptly, you should:

• Conclude the points made in the body paragraph
• Thank the hiring manager for the opportunity
• Finish with a call to action. This is a good way to start a conversation. A simple “At your earliest opportunity, I’d love to discuss more about how I can help company X” will work

Formal Salutations

End the cover letter in a professional manner. Something like “Kind regards” or “Sincerely” will be proficient.

For more inspiration, read our step-by-step guide on how to write a cover letter .

Key Takeaways

If you followed all of the above advice, you’ve given yourself the best possible chance of landing that data scientist role.

Let’s quickly summarize what we’ve learnt:

• Format your data scientist resume correctly by prioritizing the reverse-chronological format and then following the content layout guidelines
• Start your resume with a summary or objective to hook the recruiter
• In your work experience section, give attention to your best achievements, rather than your responsibilities
• Craft a convincing cover letter for an unbeatable application

Suggested Reading:

• How to Ace Interviews with the STAR Method [9+ Examples]
• 22+ Strengths and Weaknesses for Job Interviews
• What Is Your Greatest Accomplishment? [3 Proven Answers]

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12 Data Science Projects for Beginners and Experts

Data science is a profession that requires a variety of scientific tools, processes, algorithms and knowledge extraction systems that are used to identify meaningful patterns in structured and unstructured data alike.

If you fancy data science and are eager to get a solid grip on the technology, now is as good a time as ever to hone your skills to comprehend and manage the upcoming challenges facing the profession. The purpose behind this article is to share some practicable ideas for your next project, which will not only boost your confidence in data science but also play a critical part in enhancing your skills .

12 Data Science Projects to Experiment With

• Building chatbots.
• Credit card fraud detection.
• Fake news detection.
• Forest fire prediction.
• Classifying breast cancer.
• Driver drowsiness detection.
• Recommender systems.
• Sentiment analysis.
• Exploratory data analysis.
• Gender detection and age detection.
• Recognizing speech emotion.
• Customer segmentation.

Top Data Science Projects

Understanding data science can be quite confusing at first, but with consistent practice, you’ll start to grasp the various notions and terminologies in the subject. The best way to gain more exposure to data science apart from going through the literature is to take on some helpful projects that will upskill you and make your resume more impressive.

In this section, we’ll share a handful of fun and interesting project ideas with you spread across all skill levels ranging from beginners to intermediate to veterans.

More on Data Science: How to Build Optical Character Recognition (OCR) in Python

1. Building Chatbots

• Language: Python
• Data set: Intents JSON file
• Source code: Build Your First Python Chatbot Project

Chatbots play a pivotal role for businesses as they can effortlessly   without any slowdown. They automate a majority of the customer service process,  single-handedly reducing the customer service workload. The chatbots utilize a variety of techniques backed with artificial intelligence, machine learning and data science.

Chatbots analyze the input from the customer and reply with an appropriate mapped response. To train the chatbot, you can use recurrent neural networks with the intents JSON dataset , while the implementation can be handled using Python . Whether you want your chatbot to be domain-specific or open-domain depends on its purpose. As these chatbots process more interactions, their intelligence and accuracy also increase.

2. Credit Card Fraud Detection

• Language: R or Python
• Data set: Data on the transaction of credit cards is used here as a data set.
• Source code: Credit Card Fraud Detection Using Python

Credit card fraud is more common than you think, and lately, they’ve been on the rise. We’re on the path to cross a billion credit card users by the end of 2022. But thanks to the innovations in technologies like artificial intelligence, machine learning and data science, credit card companies have been able to successfully identify and intercept these frauds with sufficient accuracy.

Simply put, the idea behind this is to analyze the customer’s usual spending behavior, including mapping the location of those spendings to identify the fraudulent transactions from the non-fraudulent ones. For this project, you can use either R or Python with the customer’s transaction history as the data set and ingest it into decision trees , artificial neural networks , and logistic regression . As you feed more data to your system, you should be able to increase its overall accuracy.

3. Fake News Detection

• Data set/Packages: news.csv
• Source code: Detecting Fake News

Fake news needs no introduction. In today’s connected world, it’s become ridiculously easy to share fake news over the internet. Every once in a while, you’ll see false information being spread online from unauthorized sources that not only cause problems to the people targeted but also has the potential to cause widespread panic and even violence.

To curb the spread of fake news, it’s crucial to identify the authenticity of information, which can be done using this data science project. You can use Python and build a model with TfidfVectorizer and PassiveAggressiveClassifier to separate the real news from the fake one. Some Python libraries best suited for this project are pandas, NumPy and scikit-learn . For the data set, you can use News.csv.

4. Forest Fire Prediction

Building a forest fire and wildfire prediction system is another good use of data science’s capabilities. A wildfire or forest fire is an uncontrolled fire in a forest. Every forest wildfire has caused an immense amount of damage to  nature, animal habitats and human property.

To control and even predict the chaotic nature of wildfires, you can use k-means clustering to identify major fire hotspots and their severity. This could be useful in properly allocating resources. You can also make use of meteorological data to find common periods and seasons for wildfires to increase your model’s accuracy.

More on Data Science: K-Nearest Neighbor Algorithm: An Introduction

5. Classifying Breast Cancer

• Data set: IDC (Invasive Ductal Carcinoma)
• Source code: Breast Cancer Classification with Deep Learning

If you’re looking for a healthcare project to add to your portfolio, you can try building a breast cancer detection system using Python. Breast cancer cases have been on the rise, and the best possible way to fight breast cancer is to identify it at an early stage and take appropriate preventive measures.

To build a system with Python, you can use the invasive ductal carcinoma (IDC) data set, which contains histology images for cancer-inducing malignant cells. You can train your model with it, too. For this project, you’ll find convolutional neural networks are better suited for the task, and as for Python libraries, you can use NumPy , OpenCV , TensorFlow , Keras, scikit-learn and Matplotlib .

6. Driver Drowsiness Detection

• Source code: Driver Drowsiness Detection System with OpenCV & Keras

Road accidents take many lives every year, and one of the root causes of road accidents is sleepy drivers. One of the best ways to prevent this is to implement a drowsiness detection system.

A driver drowsiness detection system that constantly assesses the driver’s eyes and alerts them with alarms if the system detects frequently closing eyes is yet another project that has the potential to save many lives .

A webcam is a must for this project in order for  the system to periodically monitor the driver’s eyes. This Python project will require a deep learning model and libraries such as OpenCV , TensorFlow , Pygame , and Keras .

More on Data Science: 8 Data Visualization Tools That Every Data Scientist Should Know

7. Recommender Systems (Movie/Web Show Recommendation)

• Language: R
• Data set: MovieLens
• Packages: Recommenderlab, ggplot2, data.table, reshape2
• Source code: Movie Recommendation System Project in R

Have you ever wondered how media platforms like YouTube, Netflix and others recommend what to watch next? They use a tool called the recommender/recommendation system . It takes several metrics into consideration, such as age, previously watched shows, most-watched genre and watch frequency, and it feeds them into a machine learning model that then generates what the user might like to watch next.

Based on your preferences and input data, you can try to build either a content-based recommendation system or a collaborative filtering recommendation system. For this project, you can use R with the MovieLens data set, which covers ratings for over 58,000 movies. As for the packages, you can use recommenderlab , ggplot2 , reshap2 and data.table.

8. Sentiment Analysis

• Data set: janeaustenR
• Source code: Sentiment Analysis Project in R

Also known as opinion mining, sentiment analysis is a tool backed by artificial intelligence, which essentially allows you to identify, gather and analyze people’s opinions about a subject or a product. These opinions could be from a variety of sources, including online reviews or survey responses, and could span a range of emotions such as happy, angry, positive, love, negative, excitement and more.

Modern data-driven companies benefit the most from a sentiment analysis tool as it gives them the critical insight into the people’s reactions to the dry run of a new product launch or a change in business strategy. To build a system like this, you could use R with janeaustenR’s data set along with the tidytext package .

9. Exploratory Data Analysis

• Packages: pandas, NumPy, seaborn, and matplotlib
• Source code: Exploratory data analysis in Python

Data analysis starts with exploratory data analysis (EDA). It plays a key role in the data analysis process as it helps you make sense of your data and often involves visualizing them for better exploration. For visualization , you can pick from a range of options, including histograms, scatterplots or heat maps. EDA can also expose unexpected results and outliers in your data. Once you have identified the patterns and derived the necessary insights from your data, you are good to go.

A project of this scale can easily be done with Python, and for the packages, you can use pandas, NumPy, seaborn and matplotlib.

A great source for EDA data sets is the IBM Analytics Community .

10. Gender Detection and Age Prediction

• Data set: Adience
• Packages: OpenCV
• Source code: OpenCV Age Detection with Deep Learning

Identified as a classification problem, this gender detection and age prediction project will put both your machine learning and computer vision skills to the test. The goal is to build a system that takes a person’s image and tries to identify their age and gender.

For this project, you can implement convolutional neural networks and use Python with the OpenCV package . You can grab the Adience dataset for this project. Factors such as makeup, lighting and facial expressions will make this challenging and try to throw your model off, so keep that in mind.

11. Recognizing Speech Emotions

• Data set: RAVDESS
• Packages: Librosa, Soundfile, NumPy, Sklearn, Pyaudio
• Source code: Speech Emotion Recognition with librosa

Speech is one of the most fundamental ways of expressing ourselves, and it contains a variety of emotions, such as calmness, anger, joy and excitement, to name a few. By analyzing the emotions behind speech, it’s possible to use this information to restructure our actions,  services and even products, to offer a more personalized service to specific individuals.

This project involves identifying and extracting emotions from multiple sound files containing human speech. To make something like this in Python, you can use the Librosa , SoundFile , NumPy, Scikit-learn, and PyAaudio packages. For the data set, you can use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) , which contains over 7300 files.

12. Customer Segmentation

• Source code: Customer Segmentation using Machine Learning

Modern businesses strive by delivering highly personalized services to their customers, which would not be possible without some form of customer categorization or segmentation. In doing so, organizations can easily structure their services and products around their customers while targeting them to drive more revenue.

For this project, you will use unsupervised learning to group your customers into clusters based on individual aspects such as age, gender, region, interests, and so on. K-means clustering or hierarchical clustering are suitable here, but you can also experiment with fuzzy clustering or density-based clustering methods. You can use the Mall_Customers data set as sample data.

More Data Science Project Ideas to Build

• Visualizing climate change.
• Uber’s pickup analysis.
• Web traffic forecasting using time series.
• Impact of Climate Change On Global Food Supply.
• Detecting Parkinson’s disease.
• Pokemon data exploration.
• Earth surface temperature visualization.
• Brain tumor detection with data science.
• Predictive policing.

Throughout this article, we’ve covered 12 fun and handy data science project ideas for you to try out. Each will help you understand the basics of data science technology. As one of the hottest, in-demand professions in the industry, the future of data science holds many promises. But to make the most out of the upcoming opportunities, you need to be prepared to take on the challenges it brings.

Frequently Asked Questions

What projects can be done in data science.

• Build a chatbot using Python.
• Create a movie recommendation system using R.
• Detect credit card fraud using R or Python.

How do I start a data science project?

To start a data science project, first decide what sort of data science project you want to undertake, such as data cleaning, data analysis or data visualization. Then, find a good dataset on a website like data.world or data.gov. From there, you can analyze the data and communicate your results.

How long does a data science project take to complete?

Data science projects vary in length and depend on several variables like the data source, the complexity of the problem you’re trying to solve and your skill level. It could take a few hours or several months.

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How to Build a Data Science Portfolio & Resume

After you complete a degree or certificate program, it’s time to pursue a career in the field of data science. For practically all industries, data scientist positions are expanding with new roles being created every day. According to the Bureau of Labor Statistics, database administrators and computer systems analyst positions are expected to grow by 8% through 2030. The hiring process can be uniquely demanding, from selecting the right companies for application submission and preparing to answer difficult data science interview questions , to facing rejection due to inexperience or limited skills, there are fast-changing challenges applicants must face. While you may feel prepared because of the knowledge base that you’ve cultivated in your academic and professional training, there’s still an important step that requires pinpoint attention: preparing a data science resume and portfolio to appeal to a future employer.

What Is a Data Science Resume?

Resumes, in general, are excellent windows to introduce yourself to a preferred company. Typically limited to a single one-sided page, resumes can be viewed as a surface introduction to who you are as a candidate for a potential position. For those pursuing a career in data science , it’s important to present the information appropriately to appeal to an audience that values professional and academic experience, technical and soft skills, and the awards you’ve received that make your application competitive. Data science professionals who are your direct competitors for hire will be creating and submitting their resumes from different stages of their careers. The resume of a recent college graduate will contain a much different focus than a data science professional who has prior experience in the field. Both candidates can create compelling resume materials, but both need to concentrate on the strengths that they uniquely bring to the table. A successful data science resume will contain general information about the applicant and specific material that appeals to an open position. Creating a new resume for every position for which you apply is an overwhelming, tedious process. To avoid this, it’s important to revise your resume only slightly when a particular position calls for it. For example, if a listing includes that an applicant must have experience working with a more obscure data science programming language , you could win over a recruiter by editing your resume to illustrate that you have experience working in that language (if you do). A balanced, effective, and competitive data science resume should try to include these sections:

Contact Information

The first piece of information a future employer sees on your resume should be your name. In text larger than the rest of your resume’s content, your name should appear in a pronounced, clear way. Your contact information should be attached to your name, either directly beneath it on the page or closely associated, and include these components:

• Email address
• Phone number
• Link(s) to your online data science portfolio

This information is vital as it will be the point of contact should an interested employer choose to reach out to you. The more accessible you can make this information, the better chance you’ll have of hearing back on your application.

Education Experience

Because most employers will require at least a bachelor’s degree for entry level data science positions, highlighting your past educational experience is integral to a successful resume. In the educational experience section, it’s a good idea to include the most recent degree you’ve completed. If you’re still finishing your bachelor’s degree when applying, you should be sure to include your expected graduation date. When you list your education experience, it’s important to make clear which school you went to and when you completed your degree. This information lets an employer know how recently you completed your degree, and how that degree fits into the position of interest. Listing your GPA in this section can also be an important component of this section. Generally, if your GPA from your undergraduate or master’s degree is greater than or equal to a 3.0, you should feel comfortable advertising it. Otherwise, it would probably be best to leave your GPA unlisted. Finally, this is the appropriate section to include any relevant certificates you’ve completed. For example, if you’ve gotten a degree in mathematics but have some kind of certification in a data science-related topic, you should feel confident including that information. This nuanced approach will let an employer know you have strived to advance your career and professionalism in the field and have the academic training to prove it.

While other parts of the resume have clearer and more rigid conventions, the skill section can be more subjective and is your opportunity to introduce specifications of your professional profile that may not be evident in other areas. A lot of the required skills employers seek in new hires can be found in the job listing. Some technical and soft skills that you should outline on your data science resume include:

• Programming Languages Proficiencies ( Python , R , Tableau , SQL )
• Flexibility and Adaptability to new challenges
• Self-motivation
• Leadership or management styles or approaches
• Data science strategies

These are some general starting points that should be based off information provided in the job posting. Paying close attention to the language used in the posting is key here. Employers will want candidates who have the skills that they call out specifically.

Work Experience

A section that showcases your work experience, regardless of your professional background, is an important step to help employers learn more about you. If you have prior experience in the field, this is the perfect opportunity to outline where you’ve worked and what tasks you performed in your role. In this capacity, you can bolster your proven skillset with evidence from your professional past. When listing your work experience, it’s considered best practice to include the time window for how long you worked for a company or organization. This section may seem light if you’re a recent (or pending) graduate entering your chosen field. If, as a student, you don’t have relevant, field-related work experience, listing your past jobs here is still important. This shows future employers your work ethic, reliability, and track record, as well as the propensity to pick up on a new direction, amassing different experience and adapting to new learning methods. Though the jobs you held may not be data science-specific, they reflect information that employers in the industry may find appealing, illustrating how enterprising a candidate may be.

Honors & Awards

Employers will be eager to know what honors or awards you have received in school or in the workplace. Use this opportunity to outline any occasion in which you have been recognized for your academic and professional performance. General examples of appropriate honors and awards to include in this section can be scholarships and Dean’s List recognition. More field-specific awards can be competitions, technology events, or hackathons that you’ve participated in or won.

Activities and Volunteering Experience

If you find that your professional past may be lacking, a section that includes volunteering or community service experiences can be invaluable and speak greatly to initiative, character, personal responsibility, and involvement. This section can relay to a prospective employer that while you haven’t yet developed in the industry directly, you have still served your community.

Different Resumes for Different Career Stages

Competitive data science resumes will look differently for people who are entering the field at different points in their professional lives. Candidates aiming to land a job in data science will essentially fall into three categories: candidates who have just graduated, candidates who are transferring into the field of data science, and candidates who have already gained experience in the industry. Data analyst and data visualization expert Hana of Trending-Analytics.com encourages, “I strongly recommend creating a data portfolio, even if you are a beginner, as it showcases your skills and competencies in a more effective way than just listing them out in your resume. With that said, focus on quality over quantity when building your portfolio.”

Tips on Data Science Resumes for Applicants Who Just Graduated

Applicants who are fresh out of school will likely have to highlight certain aspects of their academic careers because of a general lack of experience in the field. For the most part, applicants can showcase volunteer experience, work experience that may not appear as relevant, and above all, educational experience. In these instances, it’s important to lean on the skills you have gained and your recently completed, organized data science portfolio. The key here is effectively communicating to a prospective employer that you are capable of carrying the fundamentals of data science into the role and help guide their organization forward..

Data Science Resumes for Career Transfers

Finding the right career can be difficult for everyone. Fortunately, there is always the opportunity to change directions and explore a new field. Data science is an attractive option for many candidates in other technical fields because of the way the industry is expected to grow as well as how widely applicable the related skills. Applicants who aim to transfer into the data science space are seen as more naturally prepared than transfers in other nuanced industries. Data skills translate and scale widely, you need only to create application materials that reflect a propensity to learning new technical, programming, and soft skills. For those aiming to pursue a data science career in a move from a different field, composing a competitive portfolio may prove especially challenging. In this capacity, it’s greatly beneficial for a candidate to pursue a certificate program or bootcamp in data science to build out this component more cohesively.

Resume Tips for Applicants with Prior Experience

While this may appear to be the most advantageous position from which to create and submit a data science resume, it still poses unique challenges and requires a unique focus. You can give greater context to the skills and educational experience that you outline in the resume by including prior work information that details the specific tasks you’ve completed.

How to Build a Data Science Portfolio

If you’re following this guide in sequence, then you’ve already included a link to your data science portfolio in the contact information of your resume. This measure will ensure that the projects you’ve completed – either in the classroom or at work – can be viewed by prospective employers. One aspect that practically all job candidates should be sure to include in a data science portfolio is a link to your Github profile. While using Github alone can sometimes serve as a suitable portfolio option, having a standalone data science portfolio can communicate additional skills to your prospective employer. Through either approach (or both), a well-organized, cohesive, and digestible Github profile will serve as a vital extension to any candidate’s application materials. Creating and uploading projects to a Github profile should be part of the coursework you completed as a student in whatever data science program you attended. For those aiming to transition into a career in data science from another field, creating a Github profile and proving your skill in pushing and pulling projects is a must. Having an active or previously used Github will send the message to future employers that you’re capable of employing programming languages that will be used on the job. These kinds of languages should include at least one of the following:

By clearly showcasing projects on your Github to which you’ve contributed, you’ll be able to make the case to your prospective employer that you have the technical expertise and collaboration skills to ensure you can successfully contribute to team projects. But you shouldn’t rely solely on the content in your Github profile. Instead, consider building out other materials for a comprehensive data science portfolio website that underscores an additional attention to detail.

Tips for Building a Data Science Portfolio

Applicants have free reign over what they want to include in a competitive, accessible portfolio, but there are some aspects to include that will set your portfolio apart. Jason Goodman, a data scientist for Airbnb, suggested that successful data science portfolio projects tend to include the following components :

• A Demonstration of Working with Real Data : By using real, raw data in the projects contained in your portfolio, you will demonstrate to your prospective employer that you can clean, organize, and build out visualizations with data.
• Scraped Data – By Your Own Means : Goodman contends that scraping data from most pages on the web is not as daunting as it may seem. By showing that you can collect data from sports statistics or housing price ranges, you will communicate that you can use clever means to gather data.
• Work from Public APIs : By pulling data from a public application programming interface (API), you will illustrate how you can build data sets from publicly available information.
• Projects that Incorporate New Data, New Conclusions : Projects that invoke boring or well-known data points won’t help your data science portfolio. Conversely, as Goodman exemplifies, using unconventional data like rap lyrics will communicate to your future employer that you have an investigative eye and a novel approach to data science.
• Your Personal Interests in Data Science Projects : Goodman stresses that the best data science project to have in your portfolio will tell a story about your personal or professional interests. It will also be infinitely more valuable than projects you think employers will find impressive. These projects will often fall flat and will relay tired, boring conclusions. Your future employer will want to know you, so this is the perfect opportunity to curate projects around what you find important.
• Data Visualization : A key portion to building a data science portfolio will be how you decide to include data visualizations. By putting effort into the aesthetics of your data science projects through graphic design and layout, you will show your future employer your capability for careful and cosmetic attention to detail .
• Concise, Clear Conclusions : Successful projects in data science portfolios must include brief, direct conclusions. As Goodman states, “people have short attention spans,” and this is especially true of those conducting the hiring process, sifting through hundreds if not thousands of viable candidates. Get to the point quickly, and your projects will leave a longer lasting impression.
• Interactive Projects : A data science portfolio that includes interactive elements will engage the user more effectively. For example, data visualizations that prompt users to click to learn more, or projects that include a quiz will prove memorable for employers reviewing your application materials.

One of the best ways to build a portfolio as a student is to dive into the relevant coursework that will give you an opportunity to create unique, independent data science projects. By getting a Master’s in Data Science , you’ll gain the skills necessary to expand your portfolio with a proven basis of knowledge and pursue a dynamic and vibrant new focus. Explore graduate-level data science programs today and begin your journey into finding a job as a data scientist .

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Data Scientist Resume – Guide and Sample

When you’re on the job hunt, having an impressive resume that showcases your skills and experiences is super important. In this guide, we’ll take you through the steps to create a standout resume tailored to the field of Data Scientist.

Data Scientist Resume

Now, first of all, we should know what to include in a Data Scientist’s resume. So, let’s start by knowing it first.

The most important sections that we should include are:

• Contact Information
• Work Experience

If you want to go a step further then you can also include the following sections:

• Awards & Certifications
• Interest & Hobbies

So, those are the sections to use, but what should you write for each of them? Let’s find out and see how we can tailor a Data Scientist Resume.

Table of Content

How to correctly display the contact information

How to write the resume objective or summary.

• How to write work experience that stands out

How to Write the Skills

Top skills for data scientist resume, how to showcase your projects in resume, how to list your education correctly, what else to include in the data scientist’s resume, sample resume of data scientist.

For this section, it is not necessary to showcase your creativity. The only requirement is to provide accurate and factual information.

The contact information section should include:

• Professional Title:  This should align with the role you are applying for i.e., “Data Scientist”.
• Phone Number:  Double-check for errors
• Professional Email:  Use email like  [email protected]  avoid using personal or childhood emails like  [email protected]
• Location:  It is optional you can include it or not. It’s up to you.

It is the first thing recruiters read because they do not have much time to review the whole resume. So think of the summary as your elevator pitch because it is the best way to hook the reader, so make it count.

A resume summary is a summary of your professional experience and accomplishments, typically consisting of 2-4 sentences. It gives a quick overview of your skills, qualifications, and achievements.

Tell them who you are, highlight your key skills, and convey your passion for development. So, you can grab their attention right from the start.

Summary Example:

With over 4 years of experience, a focus on the development of data-intensive applications, and resolution of complex architectural and scalability issues across diverse industries is brought to the table. Expertise is centered around predictive modeling, data processing, and the implementation of data mining algorithms, complemented by proficiency in scripting languages such as Python and Java. The capability to create, test, and deploy highly adaptive services is demonstrated, translating business and functional requirements into substantial deliverables.

How to write work experience that stands out:

Not much can beat a candidate with a wealth of relevant work experience that’s why it is important to spend time perfecting this section.

Here is the best way to structure your work experience:

• Position Name
• Company Name
• Responsibilities & Achievements

When describing your accomplishments, it’s important to provide specific details highlighting the value you bring. This not only demonstrates your technical skills but also shows the impact of your work.

Instead of simply stating:

“Data streaming”

You could say:

“Updated data streaming processes, resulting in an 18% reduction in redundancy.”

As you can see, the first statement is not effective in conveying your achievements. It only mentions that you streamed data but does not provide any information about the results of your work.

On the other hand, the second statement demonstrates that you managed to reduce the redundancy numbers. It includes hard numbers that prove your skills, which is difficult to argue against.

Work Experience Example:

What if you do not have work experience?

Maybe, you are a recent graduate seeking employment as a Data Scientist. No matter the reason for your lack of experience, a recruiter is looking for a Data Scientist with the necessary skills. 

The best way to do this is to create a portfolio of work that shows your skills.

Here are a few strategies to build an appealing portfolio (and even get paid for it):

• Take up freelance work.
• Inquire if anyone in your social network needs a web developer’s assistance.
• You can become your customer if none of the above strategies work! Develop your app or website to demonstrate your abilities.

List your technical skills in a dedicated section. Be honest about your proficiency, and sprinkle keywords from the job description to get past those pesky applicant-tracking systems (ATS).

Example of Technical Skill:

 example of soft skills:.

The skills section of your resume is very important as it highlights your abilities to the hiring manager. However, hiring managers receive many resumes and have seen numerous skills sections before. 

As a result, it’s important to make sure that your skills section is tailored to the job you’re applying for. You should only list the skills that are relevant to the role and avoid including generic skills that don’t add any value to your application. 

By highlighting your top skills in a way that is specific to the job, you’ll stand a better chance of getting noticed by the hiring manager.

Here are some of the most common skills for a Data Scientist Resume:

Hard Skills for Data Scientist Resume:

Soft skills for data scientist resume:.

Provide a brief description of key projects you’ve worked on, including the technologies used and outcomes achieved. Include links to your GitHub or portfolio for recruiters to explore your work in more detail.

Showing your projects is the best way to tell the recruiter that you are into web development and how much you are passionate about it.

Here are some you can keep in mind while writing about the projects:

• Include the right keywords in the project description to pass the Applicant Tracking System (ATS).
• Quantify your impact with metrics in the project whenever possible.
• Write in points and try to keep it clear and concise.

Here is the Example:

Once the Work Experience and Technical Skill sections are completed we can start writing about the education section. You can list your educational background. If you’ve got a degree or a certification, show it off proudly.

Just enter the education history in the following format:

• Degree Type & Major
• University Name
• Years Studied
• GPA, Honours, Courses, and anything else you might want to add

Alright, we have covered the essential aspects for now. However, have you considered if your resume is impressive enough? While covering the basics is important to get shortlisted, the following sections of your resume could be the deciding factor in whether you get hired for the job or not.

Awards and Certifications

Have you received any awards for your Data Scientist work? Have you completed any courses to enhance your skills? Make sure to include any noteworthy accomplishments in this section of your resume. Here’s an example:

If you are a Data Scientist, you are likely familiar with various languages. However, in this context, we are referring to spoken languages. If you are someone who can speak multiple languages, you may want to consider adding a section to your profile that highlights your language proficiency. You can rank your proficiency level as follows: 

• Intermediate

Now, that we have gone through all the sections that are required to add to the Data Scientist Resume. we will now see the sample of the resume and get an idea of how to create your resume.

Now that you know how to create a resume and how it can benefit you, it’s time to see how can you build a powerful resume for free with the GeeksforGeek’s Free Online Resume Builder.

GeeksforGeeks offers a Free Online Resume Builder that allows you to create professional-looking resumes that are ATS-friendly. Simply answer some basic questions about your academic skills, achievements, interests, and other relevant sections. Within 5 minutes or less, you can build a brand-new resume that can help you get hired at your dream company.

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Data Scientist Resume Examples For 2024 (20+ Skills & Templates)

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Looking to score a job as a Data Scientist?

You're going to need an awesome resume. This guide is your one-stop-shop for writing a job-winning Data Scientist resume using our proven strategies, skills, templates, and examples.

All of the content in this guide is based on data from coaching thousands of job seekers (just like you!) who went on to land offers at the world's best companies.

If you want to maximize your chances of landing that Data Scientist role, I recommend reading this piece from top to bottom. But if you're just looking for something specific, here's what's included in this guide:

• What To Know About Writing A Job-Winning Data Scientist Resume
• The Best Skills To Include On A Data Scientist Resume

How To Write A Job-Winning Data Scientist Resume Summary

How to write offer-winning data scientist resume bullets.

• 3 Data Scientist Resume Examples

The 8 Best Data Scientist Resume Templates

Here's the step-by-step breakdown:

Data Scientist Resume Overview: What To Know To Write A Resume That Wins More Job Offers

What do companies look for when they're hiring a Data Scientist?

Companies look for candidates with strong technical skills in programming languages like Python or R and experience with data manipulation, statistical analysis, and machine learning models. Companies are also looking for data scientists with problem-solving skills who can obtain actionable insights from complete datasets.

Your resume should show the company that your personality and your experience encompass all these things.

Additionally, there are a few best practices you want to follow to write a job-winning Data Scientist resume:

• Tailor your resume to the job description you are applying for: Tailor your resume for each application, aligning your skills with the specific requirements of each job description.
• Detail previous experiences: Provide detailed descriptions of your roles, emphasizing hard and soft skills related to the job description.
• Bring in your key achievements: Showcase measurable achievements in previous roles and share your best work.
• Highlight your skills:   Highlight your skills in Sales, Marketing, Communication, Customer Experience, and Management.
• Make it visually appealing: Use a professional and clean layout with bullet points for easy readability. Also, ensure formatting and font consistency throughout the resume and limit it to one or two pages.
• Use keywords: Incorporate industry-specific keywords from the job description to pass through applicant tracking systems (ATS) and increase your chances of being noticed by hiring managers.
• Proofread your resume: Thoroughly proofread your resume to eliminate errors (I recommend Hemingway App and Grammarly ). Consider seeking feedback from peers or mentors to ensure clarity and effectiveness!

Let's dive deeper into each of these so you have the exact blueprint you need to see success.

The Best Data Scientist Skills To Include On Your Resume

Keywords are one of the most important factors in your resume. They show employers that your skills align with the role and they also help format your resume for Applicant Tracking Systems (ATS).

If you're not familiar with ATS systems, they are pieces of software used by employers to manage job applications. They scan resumes for keywords and qualifications and make it easier for employers to filter and search for candidates whose qualifications match the role.

If you want to win more interviews and job offers, you need to have a keyword-optimized resume. There are two ways to find the right keywords:

1. Leverage The 20 Best Data Scientist Keywords

The first is to leverage our list of the best keywords and skills for a Data Scientist resume.

These keywords were selected from an analysis of real Data Scientist job descriptions sourced from actual job boards. Here they are:

• Data Science
• Communication
• Machine Learning
• Engineering
• Cross-Functional
• Organization
• Collaboration
• Descision Making

2. Use ResyMatch.io To Find The Best Keywords That Are Specific To Your Resume And Target Role

The second method is the one I recommend because it's personalized to your specific resume and target job.

This process lets you find the exact keywords that your resume is missing when compared to the individual role you're applying for.

Here's how it works:

• Open a copy of your updated Data Scientist resume
• Open a copy of your target Data Scientist job description
• In the widget below, paste your resume on the left, paste the job description on the right, and hit scan!

ResyMatch is going to scan your resume and compare it to the target job description. It's going to show you the exact keywords and skills you're missing as well as share other feedback you can use to improve your resume.

If you're ready to get started, use the widget below to run your first scan and get your free resume score:

Copy/paste or upload your resume here:

Click here to paste text

Upload a PDF, Word Doc, or TXT File

Paste the job post's details here:

Scan to compare and score your resume vs the job's description.

Scanning...

And if you're a visual learner, here's a video walking through the entire process so you can follow along:

Employers spend an average of six seconds reading your resume.

If you want to win more interviews and offers, you need to make that time count. That starts with hitting the reader with the exact information they're looking for right at the top of your resume.

Unfortunately, traditional resume advice like Summaries and Objectives don't accomplish that goal. If you want to win in today's market, you need a modern approach. I like to use something I can a “Highlight Reel,” here's how it works.

Highlight Reels: A Proven Way To Start Your Resume And Win More Jobs

The Highlight Reel is exactly what it sounds like.

It's a section at the top of your resume that allows you to pick and choose the best and most relevant experience to feature right at the top of your resume.

It's essentially a highlight reel of your career as it relates to this specific role! I like to think about it as the SportsCenter Top 10 of your resume.

The Highlight Reel resume summary consists of 4 parts:

• A relevant section title that ties your experience to the role
• An introductory bullet that summarizes your experience and high-level value
• A few supporting “Case Study” bullets that illustrate specific results, projects, and relevant experience
• A closing “Extracurricular” bullet to round out your candidacy

For example, if we were writing a Highlight Reel for a Data Scientist role, it might look like this:

The first bullet includes the candidate's years of experience in the role and wraps up with a value-driven pitch about how they've helped companies in the past.

The next two bullets are “Case Studies” of specific results they drove at their company. The last bullet wraps up with extracurricular information.

This candidate has provided all of the info any employer would want to see right at the very top of their resume! The best part is that they can customize this section for each and every role they apply for to maximize the relevance of their experience.

Here's one more example of a Data Scientist Highlight Reel:

The content of this example showcases a candidate transitioning from sales to data science, leveraging their experience with sales and bringing in measurable results in each bullet point. Then, they wrap up with a high-value extracurricular activity that's related to their target position.

If you want more details on writing a killer Highlight Reel, check out my full guide on Highlight Reels here.

Bullets make up the majority of the content in your resume. If you want to win, you need to know how to write bullets that are compelling and value-driven.

Unfortunately, way too many job seekers aren't good at this. They use fluffy, buzzword-fill language and they only talk about the actions that they took rather than the results and outcomes those actions created.

If you apply this framework to each of the bullets on your resume, you're going to make them more compelling and your value is going to be crystal clear to the reader. For example, take a look at these resume bullets:

❌ Data Scientist with 5+ years of experience.

✅ Leveraging 5+ years of experience in data science, specializing in predictive modeling to improve decision-making accuracy by 40%.

The second bullet makes the candidate's value  so much more clear, and it's a lot more fun to read! That's what we're going for here.

That said, it's one thing to look at the graphic above and try to apply the abstract concept of “35% hard skills” to your bullet. We wanted to make things easy, so we created a tool called ResyBullet.io that will actually give your resume bullet a score and show you how to improve it.

Using ResyBullet To Write Crazy Effective, Job-Winning Resume Bullets

ResyBullet takes our proprietary “resume bullet formula” and layers it into a tool that's super simple to use. Here's how it works:

• Head over to ResyBullet.io
• Copy a bullet from your resume and paste it into the tool, then hit “Analyze”
• ResyBullet will score your resume bullet and show you exactly what you need to improve
• You edit your bullet with the recommended changes and scan it again
• Rinse and repeat until you get a score of 60+
• Move on to the next bullet in your resume

Let's take a look at how this works for the two resume bullet examples I shared above:

First, we had, “Data Scientist with 5+ years of experience.” 

ResyBullet gave that a score of 35/100.  Not only is it too short, but it's missing relevant skills, compelling language, and measurable outcomes:

Now, let's take a look at our second bullet,  “Leveraging 5+ years of experience in data science, specializing in predictive modeling to improve decision-making accuracy by 40%”.

ResyBullet gave that a 61 / 100. Much better! This bullet had more content focused on the experience in the Data Scientist role, while also highlighting measurable results:

Now all you have to do is run each of your bullets through ResyBullet, make the suggested updates, and your resume is going to be jam-packed with eye-popping, value-driven content!

If you're ready, grab a bullet from your resume, paste it into the widget below, and hit scan to get your first resume bullet score and analysis:

Free Resume Bullet Analyzer

Learn to write crazy effective resume bullets that grab attention, illustrate value, and actually get results., copy and paste your resume bullet to begin analysis:, 3 data scientist resume examples for 2024.

Now let's take a look at all of these best practices in action. Here are three resume examples for different situations from people with different backgrounds:

Data Scientist Resume Example #1: A Traditional Background

Data Scientist Resume Example #2: A Non-Traditional Background

For our second Data Scientist Resume Example, we have a candidate who has a non-traditional background. In this case, they come from a background in sales but leverage experiences that have helped them transition to a Data Scientist role. Here's an example of what their resume might look like:

Data Scientist Resume Example #3: Data Scientist New Grad

For our third Data Scientist Resume Example, we have a new graduate who's never worked for a company before but has worked on several self-initiated projects. Here's an example of what their resume might look like when applying for Data Scientist roles:

At this point, you know all of the basics you'll need to write a Data Scientist resume that wins you more interviews and offers. The only thing left is to take all of that information and apply it to a template that's going to help you get results.

We made that easy with our ResyBuild tool . It has 8 proven templates that were created with the help of recruiters and hiring managers at the world's best companies. These templates also bake in thousands of data points we have from the job seekers in our audience who have used them to land job offers.

Just click any of the templates below to start building your resume using proven, recruiter-approved templates:

Free Job-Winning Resume Templates, Build Yours In No Time .

Choose a resume template below to get started:.

Key Takeaways To Wrap Up Your Job-Winning Data Scientist Resume

You made it! We packed a lot of information into this post so I wanted to distill the key points for you and lay out next steps so you know exactly where to from here.

Here are the 5 steps for writing a job-winning Data Scientist resume:

• Start with a proven resume template from ResyBuild.io
• Use ResyMatch.io to find the right keywords and optimize your resume for each role you apply to
• Open your resume with a Highlight Reel to immediately grab your target employer's attention
• Use ResyBullet.io to craft compelling, value-driven bullets that pop off the page
• Compare the draft of your resume to the examples on this page to make sure you're on the right path
• Use a tool like HemingwayApp or Grammarly to proofread your resume before you submit it

If you follow those steps, you're going to be well on your way to landing more Data Scientist interviews and job offers.

Now that your resume is taken care of, check out my guide on how to get a job anywhere without applying online!

Paula Martins

Paula is Cultivated Culture's amazing Editor and Content Manager. Her background is in journalism and she's transitioned from roles in education, to tech, to finance, and more. She blends her journalism background with her job search experience to share advice aimed at helping people like you land jobs they love without applying online.

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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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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..

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

Data Scientist Resume Tips

• Action Verbs to Use
• Bullet Points on Data Scientist Resumes
• Frequently Asked Questions
• Related Data & Analytics Resumes

Get advice on each section of your resume:

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.

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).

   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.

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.

   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.

   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.

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.

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.

   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.

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.

   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.

   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.

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.

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.

   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.

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.

   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.

   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.

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.

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.

   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".

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.

   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.

   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.”

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.

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.

   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.

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).

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.

   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.

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.

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.

   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.

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.

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.

   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.

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

We reached out to hiring managers and recruiters at top companies like Google, Amazon, and Microsoft to gather their best tips for creating a standout data scientist resume. Here's what they shared:

   Highlight your technical skills

Make sure to showcase your proficiency in the key technical skills required for data science roles, such as:

• Programming languages (Python, R, SQL)
• Machine learning frameworks (TensorFlow, PyTorch, scikit-learn)
• Data visualization tools (Tableau, PowerBI, Plotly)
• Big data technologies (Hadoop, Spark, Hive)

Don't just list the skills, but provide specific examples of how you've used them in projects or previous roles. Quantify your impact whenever possible, like 'Built machine learning models using Python and scikit-learn to improve customer churn prediction accuracy by 25%.'

   Showcase your projects and their impact

Hiring managers want to see evidence of your ability to apply data science techniques to real-world problems. Include 2-3 of your most impressive projects, highlighting:

• The business problem or question you were trying to solve
• The datasets and techniques you used (e.g., data cleaning, feature engineering, model selection)
• The results and impact of your work, quantified if possible (e.g., increased revenue, reduced costs, improved efficiency)

Even if the projects were part of coursework or personal learning, they can still effectively demonstrate your skills and problem-solving approach.

   Tailor your resume to the job description

Data science roles can vary significantly between companies and industries. Carefully review the job description for each position you apply to, and customize your resume accordingly.

Look for key skills, tools, and domain knowledge mentioned in the job requirements, and make sure to emphasize your relevant experience in those areas. For example, if the job heavily focuses on natural language processing (NLP), highlight any NLP projects or coursework you've completed.

   Provide context for your achievements

When describing your accomplishments, provide enough context to help the hiring manager understand the significance of your work. Instead of simply stating what you did, explain why it mattered to your team or organization.

• Developed a machine learning model to predict customer churn
• Developed a machine learning model to predict customer churn, enabling proactive retention efforts that reduced churn by 20% and saved the company $500K annually

By connecting your work to business outcomes, you demonstrate your ability to drive meaningful impact and think strategically.

   Show your communication and collaboration skills

Data scientists rarely work in isolation; they need to effectively communicate insights to stakeholders and collaborate with cross-functional teams. Highlight experiences that showcase these critical soft skills:

• Presenting findings to executive leadership
• Collaborating with engineers to deploy models in production
• Partnering with domain experts to define business problems and requirements

Worked closely with product and marketing teams to develop customer segmentation models, leading to personalized marketing campaigns that increased conversion rates by 30%.

By emphasizing your communication and collaboration abilities, you show that you can bridge the gap between technical and non-technical audiences.

   Demonstrate continuous learning and growth

The field of data science is constantly evolving, with new techniques and tools emerging regularly. Hiring managers want candidates who are committed to ongoing learning and staying up-to-date with industry trends.

Highlight any relevant coursework, certifications, or independent learning you've undertaken to expand your data science skills. This could include:

• Online courses (e.g., Coursera, edX, Udacity)
• Participation in data science competitions (e.g., Kaggle)
• Attendance at conferences or workshops
• Contributions to open-source projects

By showcasing your continuous learning efforts, you demonstrate your passion for the field and your ability to adapt to new challenges and technologies.

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.

Writing Your Data Scientist Resume: Section By Section

  header, 1. put your name front and center.

Your name should be the most prominent element in your header, typically styled in a larger font than the rest of your contact details. This makes it easy for hiring managers to remember who you are.

Here's an example of how to format your name:

Avoid nicknames or unprofessional email handles:

• Johnny 'The Data Wizard' Smith
• [email protected]

2. Include essential contact details

Under your name, provide your key contact information:

• Phone number
• Professional email address
• Location (City, State)
• LinkedIn URL

Example of how to format this:

[email protected] | 555-123-4567 | Seattle, WA | linkedin.com/in/johnsmith

Avoid providing unnecessary personal details like your full mailing address or multiple phone numbers, which can clutter your header.

3. Optionally include your top data science credential

If you have an impressive, industry-recognized data science certification or credential, consider featuring it after your name to immediately boost your credibility. For example:

John Smith, CFA [email protected] | 555-123-4567 | Seattle, WA | linkedin.com/in/johnsmith

However, avoid listing multiple credentials or irrelevant certifications that may distract from your core qualifications as a data scientist.

  Summary

A resume summary is an optional section that sits at the top of your resume, just below your name and contact information. While not required, it can be a valuable addition for data scientists, particularly those with extensive experience or looking to transition into the field. A well-crafted summary provides context and highlights your most relevant qualifications, setting the stage for the rest of your resume.

When writing your summary, focus on your key strengths, experience, and accomplishments that align with the data scientist role you're targeting. Avoid using an objective statement, as it tends to focus on your goals rather than what you can bring to the employer. Instead, think of your summary as a snapshot of your professional profile, showcasing why you're the ideal candidate for the position.

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 .

1. Highlight your technical expertise

As a data scientist, your technical skills are crucial to your success in the role. Use your summary to showcase your proficiency in key areas such as:

• Programming languages (e.g., Python, R, SQL)
• Machine learning algorithms and frameworks
• Data visualization tools (e.g., Tableau, PowerBI)
• Big data technologies (e.g., Hadoop, Spark)

For example:

Data Scientist with 5+ years of experience leveraging Python, R, and SQL to build and deploy machine learning models. Proficient in data visualization using Tableau and PowerBI, with expertise in big data technologies like Hadoop and Spark.

2. Quantify your impact

Hiring managers love to see concrete examples of how you've driven results in your previous roles. Use metrics and data to quantify your impact, demonstrating the value you've brought to your past employers. For example:

• Experienced data scientist with a passion for solving complex problems
• Collaborated with cross-functional teams to develop and implement data-driven solutions

While these statements provide some insight into your experience, they don't give the hiring manager a clear sense of your impact. Instead, try something like:

• Developed machine learning models that increased customer retention by 15% and reduced churn by 20%
• Led a team of 5 data scientists to optimize supply chain processes, resulting in $2M in annual cost savings

3. Showcase your industry knowledge

Demonstrating your understanding of the industry you're targeting can help you stand out from other applicants. Use your summary to highlight your experience working with industry-specific datasets, tools, or challenges. For example:

Data Scientist with 7+ years of experience in the financial services industry. Expertise in developing predictive models for fraud detection, risk assessment, and customer segmentation. Proficient in using industry-specific tools like Bloomberg Terminal and FactSet.

By showcasing your industry knowledge, you demonstrate to the hiring manager that you understand the unique challenges and opportunities within their sector, making you a more compelling candidate.

  Experience

Your work experience section is a key part of your data scientist resume. After all, it's where you show that you have the skills and experience to excel in the role.

Here are some tips to make sure your work experience section is as strong as it can be:

1. Highlight your technical skills

As a data scientist, you likely have experience with a variety of programming languages, tools, and frameworks. Make sure to highlight the ones that are most relevant to the job you're applying for.

Here are some examples of how you might showcase your technical skills:

• Developed machine learning models using Python, scikit-learn, and TensorFlow to predict customer churn with 95% accuracy
• Analyzed large datasets using SQL and Tableau to identify opportunities for cost savings and process improvements
• Built and maintained data pipelines using Apache Spark and Hadoop to process and analyze terabytes of data

Not sure if your resume highlights your technical skills effectively? Try using Targeted Resume to see how well your resume matches up with the job description. It can help you identify any key skills or keywords you may be missing.

Whenever possible, use numbers and metrics to quantify the impact of your work. This helps hiring managers understand the value you brought to your previous roles.

Here are some examples of how you might quantify your impact:

• Increased revenue by 20% by developing a predictive model to identify high-value customers
• Reduced data processing time by 50% by implementing a new data pipeline architecture
• Improved model accuracy by 10% by feature engineering and hyperparameter tuning

Contrast this with examples that don't quantify impact:

• Developed predictive models to identify high-value customers
• Implemented a new data pipeline architecture
• Improved model accuracy through feature engineering and hyperparameter tuning

If you don't have access to specific metrics, you can still quantify your impact by using numbers. For example, you might say "Analyzed data from over 10,000 customers to identify trends and patterns."

3. Showcase your problem-solving skills

Data scientists are often tasked with solving complex problems using data. Use your work experience section to showcase examples of how you've used your problem-solving skills to make an impact.

Here are some examples:

• Identified and resolved data quality issues that were causing inaccurate reporting, resulting in a 15% increase in data accuracy
• Developed a machine learning model to predict equipment failures, reducing downtime by 20% and saving the company $500k annually
• Collaborated with cross-functional teams to identify opportunities for process improvements, resulting in a 25% reduction in cycle time

When describing your problem-solving skills, try to focus on the impact of your work. How did your solutions benefit the company or your team?

4. Highlight your leadership and collaboration skills

While technical skills are important for data scientists, leadership and collaboration skills are also highly valued. Use your work experience section to showcase examples of how you've led projects or collaborated with others.

• Led a team of 5 data scientists to develop a new customer segmentation model, resulting in a 15% increase in marketing campaign effectiveness
• Collaborated with cross-functional teams including marketing, product, and engineering to develop and launch a new product feature that increased user engagement by 20%
• Mentored junior data scientists on best practices for data analysis and modeling, resulting in a 25% improvement in team productivity

If you're applying for a senior-level data scientist role, highlighting your leadership and collaboration skills can help you stand out from other applicants. Consider using Score My Resume to get feedback on how well your resume showcases these skills.

  Education

Your education section shows hiring managers that you have the necessary training and knowledge for the data scientist role. It also helps them gauge your career level. Here are some tips to write an effective education section on your data scientist resume.

1. Put your education at the top if you're a recent grad

If you graduated within the last 1-3 years, place your education section above your work experience. This is because your degree is likely your strongest qualification for the job at this stage in your career.

Include the following details for each degree:

• Name of institution
• Degree earned
• Graduation year
• Relevant coursework, projects, or academic achievements

Education Master of Science in Data Science, ABC University, 2022 Relevant Coursework: Machine Learning, Data Mining, Big Data Analytics, Statistical Modeling Capstone Project: Developed a predictive model for customer churn using Python and TensorFlow

2. Emphasize advanced degrees and certifications

If you have a master's degree, PhD, or professional certifications in data science or a related field, make sure to highlight these in your education section. Advanced credentials demonstrate specialized expertise that can set you apart from other candidates.

Examples of data science certifications to include:

• Certified Analytics Professional (CAP)
• SAS Certified Data Scientist
• IBM Data Science Professional Certificate
• Microsoft Certified: Azure Data Scientist Associate

Education PhD in Computer Science, XYZ University, 2018 Dissertation: A Novel Approach to Sentiment Analysis Using Deep Learning Certifications SAS Certified Data Scientist, 2020 Microsoft Certified: Azure Data Scientist Associate, 2021

3. Keep it brief if you're a senior data scientist

If you have several years of work experience as a data scientist, your education section should be concise. Hiring managers will be more interested in your professional accomplishments than your academic background at this stage.

Here's what a bad example might look like for a senior data scientist:

• Bachelor of Science in Mathematics, DEF University, 2005-2009. Graduated summa cum laude. Relevant coursework: Calculus, Linear Algebra, Probability Theory, Mathematical Statistics. Senior thesis on applications of graph theory.

Instead, keep it short and sweet:

• BS Mathematics, DEF University

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 .

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

How To Write Your Skills Section On a Data Scientist Resumes

You can include the above skills in a dedicated Skills section on your resume, or weave them in your experience. Here's how you might create your dedicated skills section:

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.

How to use these skills?

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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EMCC STEM Students Pursue Pollinator Projects

Undergrads Study Wildflower Growth; Conduct Native Bee Survey

Estrella Mountain Community College (EMCC) STEM students are busy, busy bees having engaged in Undergraduate Research Experiences, or UREs this semester. Some of our Mountain Lions just wrapped up a study of wildflower growth across different soil types while others are conducting a native bee survey — two things that can’t live without the other.

The wildflower study started last fall when  Quail Forever , a wildlife habitat conservation group, donated a rather large sum of wildflower seeds to EMCC Biology Professor Dr. Catherine Parmiter to use in her classes. They couldn’t have come at a better time as her colleague, Professor Thasanee Morrissey, who also teaches biology and is the Program Analyst for the STEM Center, just happened to be looking for a research opportunity for her students.

They decided to create a URE for five of their students and the Pollinator-Wildflower Research Initiative was born. The goal of the initiative was to determine which type of soil wildflowers would grow best in, with the understanding that more healthy wildflowers attract pollinators such as bees.

First, with the help of their Life Sciences Division colleagues Drs. Neil Raymond, Rachel Smith, and Jarod Raithel, along with the Facilities Department, an area was cleared next to the EMCC Community Teaching Garden where they constructed 16 research plots with four different soil types — native soil, pea gravel, compost, and sand. Next, they asked the MakerSpace to create some appealing signage to mark off the area. Then they planted nine different varieties of wildflower seeds and turned on the irrigation. After that, they monitored the plots weekly and kept track of the plants’ growth with written observations and digitized images.

Natalia Quinones, one of Dr. Parmiter’s students who is graduating this spring with an Associate in Biological Sciences and then transferring to  Arizona State University (ASU) to study microbiology, said one of the reasons she signed up for the URE was to boost her resume.

“I hope that this experience will allow me to join other research projects when I transfer to ASU,” she said.

Dr. Parmiter said the selection process for research opportunities at the university level is very competitive.

“Gaining research experience at the pre-Associate Degree level is essential for students such as Natalia as she navigates her transfer to ASU and later to medical school,” Dr. Parmiter said. 

For this URE, Natalia and her lab partner were responsible for identifying the types of flowers in each substrate of soil and measuring the nitrogen, phosphorus, potassium, and pH content in each plot. 

“I learned more about plant growth and development,” Natalia said. “I gained more knowledge and new vocabulary about the subject. And I learned how to edit and rewrite procedures.”

Dr. Parmiter said Natalia’s field observations and attention to detail were an asset to the team.

“She is an excellent student researcher,” Dr. Parmiter said.

Natalia also works as a part-time lab technician in EMCC’s Life Science Lab, another gold star on her resume.

“I started as a student worker in September 2022 and the lab technicians were always patient and allowed me to make mistakes and learn from them,” she said. “And since they knew I wanted to pursue an education in microbiology, they educated and taught me skills that would apply to my field of study.”

Students who participated in the Pollinator-Wildflower Research Initiative will earn  Western Alliance to Expand Student Opportunities (WAESO) scholarships after they submit their research summaries.

“This scholarship is encouragement for all of the hard work that has gone into this project,” Natalia said. “It also shows that the school supports undergraduate students to work outside the classroom and gain hands-on experiences.”

Cierra Herrera, one of Professor Morrisey’s students who participated in the Pollinator-Wildflower Research Initiative, is also big on hands-on experiences. 

“I learn best when I am doing, and I learned a lot,” Cierra said. “I love to learn and put that knowledge into practice and that is exactly what UREs do.”

Cierra, who is also one of EMCC’s  Animal Ambassadors , will graduate this spring and then transfer to the  University of Hawaiʻi at Mānoa . She plans to double major in Animal Science and either Plant and Environmental Protection Services or Marine Biology.

“I’ve always been caring and conserving before I even knew what that meant,” she said. As unusual as it might sound for a 10-year-old, I hated wasting paper, always recycled, loathed littering, and it always hurt me to see animals suffering, especially because of us, and when we can do something about it. As I continued to go to school and learned more about biology, endangered species, and why they are being endangered, there was no doubt in my mind that I wanted to help these animals.”

Naturally, when Cierra heard about the native bee survey URE, she signed up for that one, too. A perfect complement to the Pollinator-Wildflower Research Initiative, the EMCC Native Bee Project officially kicked off in March. It’s part of a collaborative effort with community colleges in Arizona and California conducting surveys to find out how many different types of bees exist across the two states, something that is currently unknown.

“One out of every three bites of your food you owe to bees,” Dr. Raithel said. “We don’t even have a baseline to know how many bees we have. They are crucial to our survival, yet we know so little about them.”

The EMCC Native Bee Project began over spring break with Drs. Parmiter, Raithel, and Smith spending four days at the  College of the Canyons in Santa Clarita, Calif., learning how to identify, or “key,” native bees so that they could pass that knowledge on to their URE students. Since then, they have begun teaching their students how to catch, clean, dry, pin, key, and photograph native bees caught on and around campus. It’s a lengthy and sometimes nerve-racking process, but for Cierra, the keys are the bee’s knees.

“Looking at the bee under the microscope is my favorite part,” she said. “They are majestic creatures and so beautiful. It is crazy to see the variation of bees in our lab! They are all so unique.”

The  National Science Foundation -funded native bee URE will last three years with six students participating each semester. The data collected will be verified and entered into  Symbiota , a public database, and each bee will have an identification number that corresponds to the student who keyed it.

“It is mind-blowing just thinking about the fact that a native bee that I, myself, keyed will go into a national database with my name!” Cierra said. “That’s absolutely surreal to me, but it is really happening. It makes me a little emotional just thinking about it because I see it as a big deal and I’m only 20 years old and this is happening along with my fellow peers. I can only think about my future and what it has in store for me.”

Cierra’s professors describe her as a problem solver who never hesitates to roll up her sleeves and dive into the action.

“She was like our wildflower research group’s secret weapon — always diving fearlessly into problems and asking all the right questions,” Professor Morrisey said. “With her sharp critical thinking skills, she was like the Sherlock Holmes of our research team! But what’s even better is her team spirit — she’s the ultimate collaborator, bringing fresh ideas to the table.”

Professor Morrisey’s students wrapped up their wildflower growth URE and presented their findings at the  Arizona-Nevada Academy of Science Annual Meeting on April 13 at Glendale Community College. 

“They did great and had a great experience at their first science conference,” Professor Morrisey said.

Cierra said she was nervous but ultimately enjoyed herself.

“It was really good!” she said. “One of the judges said our poster was eye-catching and easy to follow. He was really happy with our experiment in the design aspect — how we eliminated a lot of bias, controlled all of our variables well, and the quadrat sampling. It was really rewarding to hear that feedback.”

Are you an Estrella Mountain Community College student who would like to join the EMCC Native Bee Project or any other STEM Undergraduate Research Experience? Email Dr. Catherine Parmiter at  [email protected] .