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17 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 17 data scientist resume samples below and our  data scientist cover letter templates  can help you build a great job application in 2024, no matter your career stage.

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

Data Scientist Resume Example

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Why this resume works

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

Entry-Level Data Scientist Resume

• 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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How to describe your Personal Projects on your Data Science resume

“I know project work is important to put on my resume, but I don’t know what (or how much) to write”

You’ve been told the Projects section is important to include on on your resume, but the advice stopped there. You have limited Data Science work experience, so are relying on your Projects to convey you can do the job. You’ve spent a lot of spare time grappling with personal Projects to give yourself a step-up and you want to make sure you get credit for all that effort (and head-scratching!).

Most of all, you want all your Project work to start to work for you, but you just don’t know how.

Good news! We’ve run into this question a lot from readers and have some solid tips to share to make sure your Project work shines through :)

At the highest level, you want to make sure that the Project both sounds cool/interesting to someone with zero context, and it is clear what you actually did (i.e., the different steps). Remember, the Hiring Manager has no idea what you’ve been working on, or why, so it has to grab their attention and be easy to comprehend.

Ok, with that in mind, here are some specific suggestions for how to describe each Project that you include on your Data Science resume:

• Objective & Motivation: What you were trying to do, and why
• Role: Make it clear if it is a personal Project or if you were part of a team. If personal give a sense of the effort (e.g. x hours / week outside of core curriculum) you put in; if part of a team clarify your responsibilities
• Data: Detail the approximate dataset size and skew, how (e.g., software and techniques used) to store, extract and clean the data
• Models: Specify models and statistical techniques used, as well as programming languages and libraries used to construct them (paying particular attention here to the requirements noted on the job posting - the more you can cover off keywords/asks for the role the better!)
• Code: It is worth linking to your Github account to give the Hiring Manager the option to check out the code (plus it just makes it all the more credible that you’ve actually done the work!). A bonus option here is to also create a readme.md for the projects you’re featuring on your resume - this template is a good example
• Results: Try whenever possible to demonstrate the outcome with numerical impact or significance (it pops off the resume more than a text-only sentence) and is an indicator of how impact-oriented (or not!) you are in your work

If you follow the above high-level and specific advice your Project work should start to work for you!

How to take action now! The “Elevator Pitch”. Imagine you have 15 seconds in an elevator with the Hiring Manager to describe the Objective & Motivation for one of your projects - making it sound interesting and clear. Write down a version then say it out loud. Repeat until it is 15 seconds or under (likelihood is you’ll be way over first time around!) Use this to complete the Objective & Motivation field when you add the Project on to your resume :)

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The Complete Data Science Resume Guide in 2024

Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.

Recruiters go through hundreds of applications daily, so writing a data science resume that makes an impression is challenging.

Large enterprises like Google receive more than two million applications a year . Nearly all prominent corporations—including over 98% of the Fortune 500 —use Applicant Tracking Systems (ATS). That’s one more barrier your resume needs to jump.

So, how do you write a captivating resume that will land you a data science job interview?

You’ll learn everything you need in this article. Feel free to jump to the sections that are relevant to you.

The Complete Data Science Resume Guide in 2024: Table of Contents

• Data Science Resume Best Practices 
• Types of Resumes for Different Experience Levels
• Entry-Level Data Science Resume
• Data Science Resume for Career Switchers
• Senior Data Scientist Resume
• Do’s and Don’ts in a Data Science Resume
• Resume vs CV
• Should You Use Professional Resume-Writing Services?
• The Best Resume Builder Websites and Resources
• How to Build Your Digital Presence
• The Data Science Resume Writing Process

1. Data Science Resume Best Practices

Regardless of your experience, background, and goals, there are universal rules to follow when creating a resume.

Tailor Your Resume to the Job Offer

For starters, forget the generic resume——tailor it to the position and company you’re applying for. Your resume must meet the employer’s expectations and demonstrate that you’re a data science professional with the right mindset , qualifications , and skills for the job. A successful data science resume contains keywords matching the skills and competencies listed in the job description.

Use Numbers and Metrics

Recruiters seek experience, a specific degree, and skills that match the description. Simply listing your competencies isn’t enough. You must back them up with numbers and details that highlight and add credibility to your accomplishments .

Use Strong Action Verbs

Strong action verbs demonstrate you’re a doer and achiever. For example, instead of being "responsible for data analysis," write that you "executed the (X/Y) data analysis project." To make your achievement even more convincing, add the project outcome: "Executed a customer churn analysis project that led to a 15% increase in retention rates." Format Your Resume Professionally

Once you’ve covered the essentials, you should fine-tune your data science resume to add a professional touch.

Don’t underestimate the power of a consistent, pleasing-to-the-eye format and a clean look. Your data science resume must be appealing, concise, easy to read, and mistake-free.

And make it brief. Employers love a concise resume highlighting the qualifications, skills, and experience needed .

2. Types of Resumes for Different Experience Levels

The sections, outline, writing style, and format of your resume may vary depending on where you are on your data science career path .

The following section is dedicated to those new to data science, especially recent college graduates and professionals transitioning from another field.

We’ll then continue with valuable insights for those with rich experience who wish to move up the data science career ladder or switch employers.

Types of Resumes

• Skills-based/functional resumes focus more on your capabilities and achievements and less on your work experience. It’s preferred for junior professionals, career switchers, and college students at the beginning of their data science career journey.
• Chronological resumes are work experience-oriented. This is the best option for professionals who have held multiple related positions. If you're a recent graduate who doesn't have years on the job, a chronological data scientist resume will only bring attention to this weakness.
• Combination/hybrid resumes are ideal for career switchers and candidates with relevant work experience. It combines elements of the other two formats, allowing you to emphasize your capabilities and experience equally. Even if you haven’t worked as a data scientist, this enables you to showcase your transferrable skills.

There’s no right type of resume. Choose the one that highlights your competencies in the best way.

3. Entry-Level Data Science Resume

This section is devoted to those who have just graduated from college or university and wish to start building a data science career. Discover what you need to craft a resume that will get you a job interview for your first entry-level data science position.

Once you achieve this, we’ll help you prepare for your data science interview .

How to Write a Skills-Based Resume

Write down your relevant experience, including education, internships, job-specific skills, and data science projects. Once you list everything that comes to mind, start organizing the content. Don’t initially become preoccupied with consistent formatting or details when creating an entry-level data science resume.

Resume Sections

A functional data scientist resume typically has an extensive skills section, including a list of your capabilities with a brief description of how you’ve obtained and applied them. The rest depends on your experience—you can list previous jobs, volunteer work, relevant projects, education, languages, interests, etc. Adding a summary is also a good idea.

When organizing your resume, put your skills and achievements first, then continue with data science projects you’ve worked on, and finally, include your relevant employment history and education.

The sections’ order, however, is not set in stone. Tailor the data science resume to your experience and the job requirements. For example, the astrophysics club may not be relevant to the data analyst position in a financial corporation, but it could be a great asset for a software engineer internship.

Resume Headline

Your name should be the first item on the page, followed by a headline briefly describing your experience, education, current job title, and areas of interest. Although the headline represents you, ensure it applies to the job you’re applying for. Keep it short and relevant—a few words or a short sentence fragment are more than enough. Contact Information

The next element on your data science resume should be your contact details, which include your full name, phone number, and professional email address. You can also add links to your LinkedIn, GitHub, Kaggle profile, or other platforms that demonstrate your data science adeptness.

Data Science Resume Objective vs Profile Summary

A data scientist resume can contain an objective or a profile summary section. While the two are similar and sometimes used interchangeably, some key differences exist.

The objective statement is slightly shorter and closely related to the position you’re applying for. It highlights your professional goals and how you would contribute to your desired position and company.

In contrast, the summary focuses on your qualifications. It emphasizes the skills, experience, and achievements that make you a good fit for the job.

These are optional sections, so if you include one, ensure it’s worth the space . Tailor them to the company and position, mirror the language of the job posting, and highlight your most significant strengths.

Although some claim that the objective statement is outdated, if you’re applying for an entry-level job or internship , your data science resume may benefit from such a section.

Objective Statement Examples

• Result-oriented individual with a strong capacity for learning and a bachelor’s in computer science. Seeking to utilize hands-on modeling experience as an entry-level data scientist at BCG Gamma. Possessing expert knowledge of scripting languages and the ability to work in a cross-functional environment.
• A highly analytical economics graduate with strong interpersonal and leadership skills. Possessing a solid statistics background, programming skills, and ability to communicate complex and industry-specific concepts. Looking to apply superior analytics skills as a data analyst at IBM.
• Seeking to gain model development experience and a strong understanding of research design and hypothesis testing as a data scientist at Appsilon Data Science. Providing programming skills and the ability to analyze complex data. A team player with strong communication skills and data science certification.

The education section may appear earlier in your data science resume if you’re an aspiring data science professional who has just finished college. If you’ve graduated with one of the most in-demand degrees for data scientists , you can add it below the contact information or the objective section.

State the name of the college or university, city, state, and degree (data science, statistics, computer science, engineering, etc.). Include your major(s) and minor(s) and the year and month you completed your degree (or expected date of completion). List the highest or most relevant first if you have more than one degree. US graduates can also include their GPA score (optional).

Data Science Projects and Publications

How do you include project details in your resume? This section allows you to compensate for the absence of rich professional experience. You can include significant data science-related coursework and academic projects you’ve completed.

But more importantly, showcase any side projects you’ve worked on to demonstrate initiative and ability to work independently. Add links to GitHub projects in your data scientist resume so potential employers can see what you’ve created and how you’ve done it.

It’s natural to feel anxious if you lack relevant work experience. But there are meaningful ways to fill in this section and plenty of entry-level positions that don’t require years on the job. You can include clubs and societies you’ve actively participated in, internships, academic research, volunteer work, etc.

Choose relevant headings for the listed experiences and add the most relevant ones first. Format them as you would with any work experience—including the name and location of the organization, your role or title, and the period of your work. Add two to five bullet points per experience demonstrating how you applied your skills to the assignment or a project.

This is the most essential part of the functional data science resume. How do you stand out without years of experience? Employers are seeking transferable skills in fresh-out-of-college applicants for entry-level data science positions.

Transferable skills indicate you have what it takes to succeed in a given role. Many of the skills required for data science positions are universal:

• A sharp eye for detail
• Identifying issues and developing effective solutions
• Understanding and confidently presenting technical information to non-technical audiences
• Initiative and ability to learn quickly and work efficiently
• Planning, organizing, and managing multiple projects with competing demands and deadlines
• Being a team player and interacting with employees of all levels of the organization

List all relevant skills to the position and illustrate how you’ve applied them.

Honors and Awards

Honors and awards can also be a stand-alone section on your data science resume. List the name of each honor or award and the date you received it. A brief description emphasizing your accomplishment is optional.

Certificates

Adding data science certificates provides additional credibility to your resume, proving you’re qualified for the position—even if you don’t have much relevant experience. They also demonstrate ambition and commitment to developing industry-relevant and in-demand data science skills.

Updating your data science resume with newly acquired certificates is good practice. If you don’t have a certificate, consider signing up for a data science training program, upgrading your skillset with the specific position in mind.

Volunteer Activities and Community Involvement (Optional)

You can showcase your participation in various on- and off-campus communities. Include the positions you’ve been appointed to, the organizations’ names, locations, and dates. In addition, you can highlight a ‘study abroad’ program you’ve been enrolled in and prominent volunteer experience.

Interests (Optional)

Interests is an optional section. Many employers would like to know more about you outside of your education and job-related experience. This helps them determine what kind of person you are and whether you’d fit the company culture.

But this section should be your last priority. Include it only if your interests are relevant to the position or organization. Be genuine but keep it professional.

Data Science Resume Writing Style

You should follow a few style guidelines to write an impressive resume. First, keep in mind that this is your first introduction to a prospective employer. So, take your time to make it visually appealing and error-free.

Second, be brief and concise. Include only relevant information to the data science position that underscores your qualifications. Don’t overload it. Your resume may get only 15 to 30 seconds of consideration.

Your resume should be:

• Straightforward and comprehensive
• Clear and concise
• Professional and grammatically correct
• Factual and accurately describing your accomplishments
• Written with action verbs

A good data scientist resume utilizes a good dose of power verbs. Refer to this list of action verbs for inspiration.

Now that you know how to write an engaging resume, let’s discuss formatting.

How to Format Your Data Science Resume

After spending long hours writing your resume, the last thing you want is for it to end up in the rejection pile. So, what should it look like to make a good first impression?

Adequate spacing, proper alignment, and neatly organized content are mandatory. And make sure everything fits onto one page.

But how do you create an appealing format?

There are plenty of options online. Most candidates use standard one-page templates. You can download our simple yet stylish data science resume sample and fill in your information.

You could also select a more elaborate format for your data scientist resume. But consider the type of job you’re applying for. Don’t go overboard with a flashy resume design and intricate elements.

And if you wish to design your resume from scratch, follow these formatting tips.

Resume Header

Section headings should be left-aligned and prominent but not distracting. You can bold or capitalize, use italics, or underline them—breaking up the document length and creating emphasis. And remember to be consistent. Stick to the same formatting for identical pieces of content—e.g., bold for the organizations you’ve worked for, italics for the job titles, and so on.

Your data science resume must be easy to read.

So, choose a font size between 10.5 and 12. The only part that should be larger is your name at the top. If your text is size 12, use 14 or 16 for your name.

Choose a font that’s easy to read on- and off-screen. It’s a means to deliver your message, so it shouldn’t be distracting.

Resume Length

The standard resume length is one page, especially for recent graduates and young professionals. You may need two pages if you have a significant amount of relevant experience, advanced degrees, or publications.

But mind that recruiters typically spend only seconds per resume. Anything longer than one page may discourage them from reading it. So, it’s better to narrow it to the crucial information and save the rest for the interview.

Your data science resume should be easy to scan. So, use appropriate margin size, sufficient spacing, proper alignment, and bullet points.

Should You Include a Photo in Your Data Science Resume?

The requirements to add a photo to your resume vary depending on the country.

While the UK, Ireland, Canada, Australia, and the US don’t require a pic, it’s highly recommended in most European countries, including Austria, Belgium, France, Germany, Portugal, and Spain, as well as the Scandinavian countries, the Middle East, Africa, Asia, and South America.

4. Data Science Resume for Career Switchers

As an experienced professional, you’ve already sent many resumes throughout your career. But what if your work history has nothing to do with data science, and you wish to transition into the field?

What’s the best type of resume, skills-based, chronological, or combined?

If you switch to data science from a different industry, a chronological resume will make you look like the wrong person for the job. A combined resume is your best option.

Sure, the skills-based data scientist resume is designed for those who don’t have relevant work experience. But you don’t want to divert the spotlight from your work history completely.

With a combined resume, you can demonstrate the transferable skills you’ve gained through your previous experience.

How to Write a Combined Resume

The key to writing a winning data science resume for career switchers is to tailor it to the job requirements. Instead of focusing on your previous experience, employ the mirroring technique by taking all the keywords and phrases used in the job ad to describe the ideal candidate and integrating them into your resume.

You can further align your skills and career aspirations with the company’s goals and needs. (You can find them in the company’s mission statement.)

Now, let’s go over the essential resume sections.

Your headline must match the role. If you don’t have experience in a similar position, use your desired role as a headline—e.g., Aspiring Data Scientist. This will grab the hiring manager’s attention and help you pass the Applicant Tracking System’s (ATS) scan.

Contact Information

This is straightforward: Add your name, phone number, professional email address, and links to your LinkedIn, GitHub, and Kaggle profiles.

Data Scientist Objective Statement or Summary

The summary focuses on the individual’s experience and qualifications. And while that’s crucial, an objective statement might be more suitable for a career switcher’s resume.

The objective must convey enthusiasm and be tailored to the specific data science position. It also showcases your strengths and capabilities but focuses on how you’ll bring value to the organization.

This is the most crucial section in your data science resume. To make it work in your favor, determine the relevant skills for the position.

All job postings include keywords describing the top skills employers look for. And your resume will probably be rejected if it doesn’t contain any of them.

Many employers utilize Applicant Tracking Systems (ATS) that scan resumes for keywords and automatically eliminate those that don’t contain them. Hiring managers also look for words and phrases that match the job requirements.

All required skills have already been communicated in the job posting. You just need to use them in your resume.

Carefully evaluate your level of expertise in each area. Select three to five essential skills you feel the most confident in and list them in your data science resume. Then, provide relevant, quantifiable evidence of how you’ve obtained and applied them.

Forget the age-old clichés like trustworthy, dynamic, a problem-solver, great communicator, etc. They seem meaningless unless you also write how you’ve demonstrated those skills and how they apply to your desired job.

Remember that organizations are goal-oriented. List the qualities and skills that translate easily across various industries and contexts and use them to show the value you can provide.

Work Experience

In this section of your data science resume, you can add your previous positions like you would for any application, including the dates, job titles, and company names. But instead of listing the tasks you’ve performed, emphasize the transferable skills you’ve gained.

Quantify the experiences to convey the scale of the projects you’ve worked on and your achievements, making a stronger impression. Include the following instances:

• Established new strategies or procedures
• Used resources effectively (e.g., reduced expenses)
• Demonstrated effective project leadership or management
• Managed or supervised others efficiently
• Received promotions and expanded my scope of responsibilities
• Increased profits and improved the company’s services

And be prepared to answer many follow-up questions for these achievements during the data science interview.

Consider the advice of Edouard Harris —a physicist turned successful data scientist and co-founder at SharpestMinds :

If you’ve worked in finance, leverage your knowledge in finance. Don’t erase your past. Don’t say, “ Oh, I was in finance but not anymore .” No, no, no. You want to be like, “Yeah, I was in finance. I was goddamn good at it, and I worked on x, y, and z when I was in finance. And now, look at how I’m applying x, y, and z in interesting and clever ways as appointed to this instance .” You want to tell a story and create a narrative about yourself. And the narrative you create about yourself when you transition isn’t, “ Oh, I’m changing everything about myself .” It’s more like, “ No. I’m moving away to even further increase the value of the experience that I already have . ”

Relevant Certificates/Education

The competition for data science positions is intense. That’s why you need proof that you have the necessary skills for the job. But returning to university to earn a relevant degree could be quite challenging for a working professional, even if you’re in-between jobs.

Fortunately, alternative ways exist to learn data science at your pace. 365 Data Science’s program covers everything you need to become a certified data scientist—from the fundamentals to advanced topics. And adding a certificate of achievement will give your data scientist resume credibility.

Of course, you should add your education, which is still valuable even if it isn’t in a related field.

Next, add all the data science projects you’ve participated in, showcasing your technical skills. Describe the tools and techniques you used and the projects’ outcomes. But don’t overcrowd the section with technical terms. After all, it should be accessible to hiring managers with a non-technical background.

Make this section of your data science resume brief but rich in content. Don’t cover the project in detail; you’ll have the opportunity to discuss it during the interview. Focus on your contribution and achievements.

Honors and Awards (Optional)

Add this section only if your accomplishments are relevant to the position you’re applying for or if they highlight skills that are invaluable to the employer.

Data Scientist Resume Writing Style

Use powerful, action words to make your resume impactful—e.g., instead of “responsible for,” use “managed.” The former doesn’t reveal your involvement in the task or project, depriving you of any credit. In contrast, “managed” conveys you possess leadership skills.

Back this up with numbers, facts, concrete outcomes, and achievements to increase the desired effect. For instance, writing that you managed a project that led to a 47% increase in profits demonstrates that your leadership results in profit growth, and you get well-deserved credit for that.

Of course, keep everything moderate and be realistic and truthful about your achievements. More importantly, include only things you feel confident discussing during the interview.

5. Senior Data Scientist Resume

If you’re a data science professional who wants to climb the ladder, your resume will look different from that of an intern or junior specialist. It’ll also vary depending on whether you want to start a job at a new company or if you’re applying to your current one.

If you wish to switch positions at your current workplace, it’s crucial to highlight all your contributions to the company.

Emphasize your achievements, the issues you’ve resolved, and the projects you’ve participated in. But through all this, highlight how you’ve obtained and applied the skills necessary for the new role. Otherwise, you risk getting in the “you’re too valuable in your current positions” pile.

If you wish to land a job at a new company, highlight your achievements throughout your career.

Make your data science resume focused and concise. This can be difficult if you have vast experience but try to narrow it down to only the most relevant roles and achievements to the new position.

In both cases, you should aim to craft a resume demonstrating your suitability for the role. Emphasize how you’ve obtained and applied the required skills. Include quantitative evidence of your achievements to illustrate your value to your future employer. For instance, state by what percentage you made a process more efficient, how much revenue you helped generate, etc.

The recruiter may ask you to elaborate on your accomplishments during the data science interview . So, be prepared to back your claims by explaining the situation, actions, outcomes, and how you achieved them.

Which data science resume type is suitable to achieve this?

How to Write a Chronological Resume

The most suitable format for a professional with extensive experience is the chronological resume. It lets you focus on the gained experience and creates a narrative of your career progression.

But it may not be the best option if you have long periods of unemployment or have changed your field of work. A combined or skills-based data science resume is more appropriate in these cases.

The most vital section in the chronological resume is work experience, which you list chronologically—typically from the latest to the oldest position.

Header and Contact Information

If you’re a seasoned data science professional with much to add to your resume, you can skip the summary or objective statement. The summary, however, will add value to your resume. Of course, start with your name, header, and contact information.

Data Science Resume Objective or Summary?

The objective statement aims to demonstrate how your career aspirations meet the employer’s requirements. But a senior professional’s career goals typically become evident from their experience, which makes the objective obsolete.

A summary allows you to create a narrative about your expertise. You can highlight your most valuable qualifications and achievements and give recruiters an idea of your capabilities.

As previously stated, boost your data science resume using the keywords mentioned in the job description, making your resume ATS-friendly. Identify the sought-after skills, select the ones you feel the most confident about, and highlight them in your summary.

You can also go a step further and analyze your target industry and company. Identify your key selling points and tie them to the company issues you’re qualified to solve. Find where your goals meet your target organization's needs and use this to your advantage.

Unlike the skills-based data scientist resume, the chronological one doesn’t need an extensive skills section with bullet points and long descriptions.

Still, it’s a good idea to list the tools and techniques you feel most confident about, especially the ones mentioned in the job description. Of course, you should be able to demonstrate how you’ve mastered them—e.g., via your experience or certification programs.

This is the largest and most important section in your chronological data scientist resume. There is the challenge of trying to fit everything onto one page. But it’s better to omit some of your previous jobs than to have a lengthy resume that no one wants to read.

So, start with your most recent job, then list your previous roles chronologically. If the list is too long, you must make some tough decisions. Ask yourself for every job you list if it’s relevant to the current role. If you’re unsure, don’t include it.

It’s more important to leave space for describing your responsibilities and accomplishments than to list all titles you’ve held. If you haven’t included all your previous jobs, call this data science resume section Relevant Experience.

Instead of listing everything you did in a given role, add three or four bullet points per job describing the tasks and accomplishments relevant to the new position. With each responsibility you describe, add the outcome for the company and a metric to support your contribution.

Relevant honors and awards support your resume and give you the extra edge it needs to stand out. Include academic awards and accolades because it's an excellent way to demonstrate that your work and contributions to the data science field have been recognized.

Trainings/Certifications

Data science is a rapidly changing field. So, demonstrating that you continue to learn and upskill is crucial. Your data science resume will undoubtedly benefit from adding role- and industry-specific training, conferences you've attended, or seminars you’ve conducted.

The Interests section is more effective than many people think.

If something that separates you from the rest and demonstrates a transferable skill—like sports achievements—consider adding it to your resume. But do not include random hobbies that don’t show a relevant quality or capability.

When it comes to resume writing style, less is more.

Eliminate words like ‘numerous,’ ‘approximately,’ and ‘around.’ Instead, use specific numbers and remove redundant pronouns and articles, which make your resume heavy without contributing to its content. Your resume should be simple yet sharp and precise, conveying professionalism and confidence.

6. Dos and Don’ts in a Data Science Resume

No matter how experienced you are in resume writing, making careless errors is easy. Avoid common mistakes by following our recommendations for a job-winning resume.

7. Resume vs CV

What’s the difference between a resume and a curriculum vitae (CV)?

• Resumes are brief, overview your skills and experience, and are tailored to a specific job posting.
• CVs are more detailed (and lengthy) and used explicitly for academic applications.

But note that only the US and Canada make this distinction. In Europe, a CV and a resume are interchangeable, entailing a short document targeting a specific job.

So, the rules for writing a data science resume described above apply unless you’re applying for an academic job in the US or Canada.

8. Should You Use Professional Resume Writing Services?

If the thought of writing a data science resume still frightens you, you can hire a professional service to do it for you. But before choosing a company, conduct detailed research online to ensure it’s a quality service. You then submit the relevant information about yourself, and the firm delivers a resume.

• A professional-looking resume
• No worries about typos and grammatical errors
• Optimal structure for your purposes
• Your data science resume quality is not guaranteed—the outcome is as good as your provided information.
• Need to modify the resume for different job applications—may not have access to the template.
• Could be expensive

Some resume writing companies offer an additional service of resume review and feedback from a professional. So, you can write your resume applying the above principles and submit it for review, which is much cheaper, and you’ll receive advice for areas of improvement.

9. The Best Resume Builder Websites and Resources

There’s a wide choice of resume builders available online. But how do you choose the best one? Check out our list of resume builders that offer the best features and valuable, relevant resources.

• ResumeGenius
• ResumeBuild

10. How to Build Your Digital Presence Using LinkedIn, GitHub, and Kaggle

Job search sites aren’t the only way to find and apply for new roles. Many online platforms exist for professionals to build a network and even find jobs. Used correctly, they are an excellent opportunity to establish yourself as an expert.

Plus, adding links to your LinkedIn, GitHub, and Kaggle profiles to your data science resume allows employers to learn more about you and your work.

But how do you use these links optimally?

How to Optimize Your LinkedIn Profile

LinkedIn is the go-to platform for professional networking, data science job searching, and establishing your online presence as an expert.

In addition, you can learn more about the companies you’re interested in, stay updated on news in your field, and explore career opportunities. It’s also an opportunity to get noticed by recruiters.

Think of your LinkedIn profile as your online data science resume. The platform is an indicator of how thorough your profile is. Follow our tips on further improving it to appear in more searches.

Professional Headline

First, make your headline brief and memorable by highlighting your skills and achievements with strong, impactful words.

Customize your URL using your name or an abbreviated version, making it easier to remember and share. And it will look better on your data science resume.

Choose a high-quality photo in business attire and one where you’re alone, avoiding distracting backgrounds.

Your LinkedIn summary should be similar to your data scientist resume summary. Structure it as a brief first-person narrative (six to nine lines) describing who you are and what you do. Include your education, relevant experience, competencies, and career goals.

You can add your entire job history or only the relevant experiences here. A one-page format doesn’t limit you; you can describe your responsibilities and achievements in more detail. Still, it’s better to be brief and concise.

Add an education section and include your degrees. If you’re at the initial stages of your career and lack sufficient experience, you can have additional information about your completed subjects and projects.

Most online certification programs allow you to add certificates to your LinkedIn profile, which gives extra credibility to your skills. Another way to prove your skills is by completing LinkedIn’s free assessments.

Should You Add Your GitHub Page to Your Data Scientist Resume?

Absolutely. Every data science professional needs a GitHub profile. Adding it to your resume is a great way to showcase your skills and work.

Your project doesn’t have to be extraordinary to make it to your data science resume. If you’re happy with the code, publish it. It’ll show employers you have the skills and motivation to complete side projects.

And ensure your code follows the best practices. Companies prefer to hire a specialist who writes good, clean, and well-tested code.

Should You Add Kaggle Competitions to Your Data Science Resume?

Participating in Kaggle competitions doesn’t automatically turn you into an expert. Still, it broadens your experience and enhances your skills. And this is particularly important if you lack job experience.

11. The Data Science Resume Writing Process: Final Words

While it requires substantial preparation and work, resume writing can be pleasant. It lets you step back, view your experiences differently, and create your ideal narrative.

Determine the crucial skills for the job and showcase how you’ve obtained and applied them. Consider how hiring managers perceive your qualifications and accomplishments and convince them you can bring value to their business. Finally, ensure your layout, grammar, and formatting are impeccable to make a great first impression.

And remember that your data science resume will always be a work in progress that changes and develops as you upskill and gain experience.

Are you excited to begin your data science career?

Our course on Starting a Career in Data Science: Project Portfolio, Resume, and Interview Process will help you take the next step to land your dream job. Sign up for our learning program and try the course for free.

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Marta Teneva

Senior Copywriter

Marta is a former Senior Copywriter at 365 Data Science. Digging into her own experience of transitioning into a new field and all the uncertainty that initially goes with it, she creates informative and fun to read content that helps our readers expand their career options in data science and achieve the goals they have set for themselves.

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Data Scientist Resume Example

This guide provides you with Data Scientist resume examples to use to create your own resume with our easy-to-use resume builder. Below you'll find our how-to section that will guide you through each section of a Data Scientist resume and you'll be closer than ever to landing your dream job.

Want to write a great Data Scientist resume?

You should know this. Most data science resumes that hiring managers receive scream:

• “Wrote a digit recognition algorithm with 95% accuracy”
• “Used Tensorflow to do this really simple detection”
• “Used this off the shelf software for ‘X’”

Reality is, most entry level data science resumes rarely go beyond the common pattern listed above. The experienced data science resumes on the other hand fail to communicate the complexity, scale or innovation performed.

Fixing just that would make your data science resume stand out from 90% of the other applications that a hiring manager would receive.

In this guide, we are going to take you a step ahead though. Whether you are looking to land a FAANG/MAANG data science role or work for an innovative startup - we are going to show you how to create a Data Scientist resume that will win 99% of the time!

FAANG Data Scientist Resume Example

Senior Data Scientist Resume Example

Let’s start with an overview of what it takes to create a great Data Scientist resume.

How to write a Data Scientist Resume?

To write a Data Scientist resume:

• Highlight either your business impact or data science innovation.
• Provide context to what type of ML work you performed
• Make sure to add the programming languages you use
• If applicable, show your ability to architect ML systems
• Highlight your publications

If you avoided those, you would struggle to justify how your work made an impact. For example, it isn’t uncommon for us to come across statements like these in data science resumes: “Leverage my skills in data cleaning, data analysis and predictive modeling to achieve business goals” - statements like these are bad for your resume.

However, if you are seeking an entry level data science position - consider the following while writing your entry level data science resume:

• Highlight your thesis and projects - they make a big difference when there’s no work experience.
• While listing your projects, display your thoughtfulness in approaching the problem and solving it.
• Adding programming languages adds weight to your data science resume. However, do not list yourself as an “expert” if you are a recent graduate.
• Add a link to your portfolio or Github.

Do you know about FAANG data science roles - a Github profile is the most commonly sought after resource to see how proactive you are, what you’ve built on your own and your code quality.

The Best Data Science Resume Format

The quality of a good data science resume format would be:

• A format that allows you to list your skills and experience in one (or max two pages).
• Consistent throughout leveraging not more than two fonts and shouldn’t have too many colors on it.
• Uses bullet lists instead of large paragraphs to highlight a Data Scientist’s skills and experience.

Keeping those three qualities of a good Data Scientist resume’s format, the best format for you would be:

• Reverse chronological resume format - if you are an experienced Data Scientist.
• Hybrid resume format - if you are an entry level Data Scientist who lacks the experience, but has skills and data science projects to show.

Experienced Data Scientist’s Resume vs Entry Level Data Scientist’s Resume?

What separates an experienced Data Scientist’s resume from an entry level resume is: #1 Business impact: An entry level Data Science resume can often only display a thoughtful approach to solving a problem, but a job winning Data Scientist resume should be able to show the impact of work performed.

E.g. an entry level Data Scientist resume would have “Leverage data cleaning, database management and deep learning for text classification”

Vs an experienced Data Scientist’s resume would say “Created real time text classification capabilities through hybrid deep learning models (attention mechanism position and focal loss) for City of Chicago to handle traffic violation in low light conditions. Convolution attention mechanism used was Bi-LSTM with CABO model.”

#2 Technically descriptive: As most entry level Data Scientist resumes don’t involve innovating and leveraging sophisticated technologies. It isn’t too difficult to find phrases like “Wrote a machine learning model to recognize Chinese characters”

Vs an experienced Data Scientist’s resume should say “Led digitization of 3TB of Chinese character data by using RAN of aggregation module, mapping encoder and a character analysis decoder. Outperformed existing DenseRAN by 33.6%, with 57.9% higher computing efficiency.”

As you can see, a good data science resume would change radically with the experience of a Data Scientist. But, it isn’t uncommon to see experienced Data Scientists write their resumes as if they are an entry level professional.

When you write meaningfully, a hiring manager not only is able to see the impact you made, but is also able to see if you have worked on similar business or technology projects in the past as theirs.

Data Scientist Resume: Summary or Objective?

Here’s a rule of thumb for you - write a data science resume objective only when you are an entry level professional or when you are transitioning from another role (e.g SWE) to data science. If you are already working as a Data Scientist, write a resume summary instead.

With that in mind, let’s take a look at how to write an excellent Data Scientist resume summary.

How to Write a Data Scientist Resume Summary (with Examples)

To write a great Data Scientist resume summary, include the following information:

• State your years of data science experience (e.g. 10+ years of experience in…”).
• List your top technical specialization (e.g. LSTM, GAN, etc).
• List your top business skills (e.g. customer segmentation, image processing, pricing analysis, market basket analysis, etc).
• Finally, add relevant certifications and awards that you have received.

Let’s check two examples of good and bad Data Scientist resume summary samples that will illustrate better.

Entry Level Data Science Resume Summary - Bad

I am a Data Scientist with experience of analytics and applied data science experience with a focus on strategic initiatives targeting business scalability, process improvement, and efficiency.

Entry Level Data Science Resume Summary - Professional

Data Scientist with 9 months of analytics and applied data science experience to support $100M maintenance operations using survival models and PowerBI dashboards. Business expertise: performance drift, revenue leakage and regression analysis for cost estimation.

In the two Data Scientist resume examples above, we see that both have noticeable entry level experience. But when you read the second Data Scientist’s resume summary, one can clearly see why the second data science resume would win.

If you are an entry level Data Scientist too, here’s a template that you can copy to write your resume summary: “Data Scientist with {x} {months/years} of analytics and applied data science experience to support {operations} using {data science technique}. Business expertise: {expertise 1}, {expertise 2} and {expertise 3}.”

Experienced Data Science Resume Summary - Bad

Experienced Data Scientist experienced in designing, building and deploying fast, accurate, scalable and secure machine learning applications in the cloud.

We list this as a bad data science resume summary mainly because it won’t help you stand out. Let alone beat 99% of the other data science resumes. Every word added to your Data Scientist resume allows you to leave an impact - in this case you won’t make any.

Experienced Data Science Resume Summary - Professional

Data Scientist with 10+ years of experience in building high performing NLP products. Expert at neural architecture optimization of large feature spaces for performance gains. Author of Lin-ML - used by more than 100,000+ machine learning developers.

How to Write a Data Scientist Resume Objective (with Examples)

The most important factors to consider when writing your Data Scientist resume objective are:

• Add your top skills, area of expertise or specialization in it.
• Mention what you are passionate about.
• List your top recognizable achievements.

Entry Level Data Science Resume Objective - Bad

An enthusiastic entry-level data scientist, a NCSU graduate. I have hands-on work experience in machine learning models and a portfolio of Data Science projects.

Entry Level Data Science Resume Objective - Professional

An enthusiastic entry-level data scientist with hands-on work experience in creating RNN and Modular NNs to text and speech problems. Kaggle Master, Top 5% on Stackoverflow for Python and winner of Google Universal Image Embedding challenge(GAN).

When you compare those two Data Science resume examples above it isn’t too hard to see the following:

• Good Data Scientist resumes will be very specific about their past projects and top technologies.
• Poor Data Scientist resumes will be generic or verbose without any specific skills.

Common mistakes to avoid while writing a resume summary or objective include:

• Writing more than 3 lines in a resume summary or objective. If it is a wall of text, it’s going to negatively impact your application.
• Listing yourself as an expert - it is better to let your skills and accomplishments do the job instead.
• Being too vague about your interest and technology used in projects/work experience.

The idea here is to leave a good first impression, a hook that will allow the hiring manager to continue to read further with interest.

Need more examples? Here are 6 Data Scientist resume objective examples .

How to Describe your Data Scientist Experience on Resume?

Describing your data science experience on your resume should not be taken lightly. It is always one of the top few items on a hiring manager’s checklist. Despite that importance, it isn’t uncommon to see very poorly written work history on a Data Scientist’s resume.

To write a winning Data Scientist resume, you should describe your experience by following the STAR method. Using the STAR method it is very easy to highlight a problem you solved, how thoughtful you were in solving the data science problem and what results you achieved.

Let’s checkout a couple of examples to see how

Bad Data Scientist Resume Experience Sample

Data Scienstist

• Worked within the Data Science team in the SF office.
• Taking responsibility for coordinating data partnerships, and improving existing modeling processes.
• Spearheading data for new lines of business.
• Support internal data modeling needs for stakeholders and cross functional teams.
• Utilizing a plethora of technologies in my day-to-day work.

Looking at this Data Scientist’s resume, any hiring manager would wonder:

• If they have the right experience to solve the data science challenges they are looking to solve?
• They failed to communicate the impact of their work - would they be able to communicate their insights in a way that everyone can understand?
• What functions did they serve in this role?

Hiring managers spend as little as 7 seconds scanning a resume. They scan your summary/objective, job titles, work experience and your skills. If they don’t find what they are looking for, they discard your application - all in 7 seconds!

That’s why we suggest you write your work history section in a way that reduces their efforts to find the information they are looking for and leave an impact at the same time.

Let’s now look at a few examples of work history sections of good data science resumes.

Data Scientist Resume Work Experience

Data Scientist

• Optimized existing geospatial query to improve performance by 20%.
• Cleaned car image data with 10,000+ different types of cars to create a new vehicle identification API supporting over 80,000+ car dealerships.
• Worked with compliance teams to implement an AI algorithm (entity resolution algorithm) to protect against cyber threats.
• Data Science lead for DPro (dealer product) initiatives and managed ~20+ data science initiatives.
• Tech stack used: Pandas, PySpark, MCMC, GCP, Databricks, and SQL

Machine Learning Data Science Resume Work Experience

ML Data Scientist

• Created multiple deep neural network architectures to improve robotic instrument segmentation.
• Saved $15.3M in annual spend by deep learning focused histology image analysis with 93.8% accuracy.
• Implemented U-net architecture replacing existing ImageNet neural network with 10.9% higher performance. Consumed by $200M LOB products as of 2022.
• Restructured internal database of >3TB production records to improve performance.

FAANG/MAANG Data Science Resume Work Experience

Meta Data Scientist

• Identified top metrics, collected data, modeled data using SEM, and provided recommendations for the operational performance of 20+ Meta data centers located throughout the world.
• Drive Advertiser value through LSTM implementation and improve the existing understanding of Facebook’s system understanding.
• Risk control - 8.5% higher click-farm identification which led to $10M in wasted ad spend from advertisers.
• Key partner for the product team to collaborate on new insights for the Advertiser product portfolio.

How to Write a Data Science Resume With No Experience?

When you have no data science specific experience, consider writing a section that focuses on your portfolio of data science projects instead. The type of projects that you can include are:

• Recognizable competitions like Kaggle
• Projects listed on your Github profile
• Any significant academic projects performed

Platforms like Kaggle are often used by companies that are hiring entry level/experienced data science talent. And, your Github projects will enable an employer to see what you are capable of, along with your code quality.

Companies like Uber, Microsoft, etc actively collaborate with universities in the form of academic partnerships. That’s why academic data science projects bring in substantial weight to your data science resume for a hiring manager.

How to List your Data Science Projects on Resume?

To list your data science projects on your resume, create a separate section for your projects. For each project add the following information:

• Title of the project
• Short description of the project involving the problem you solved, the solution you used and technology involved.

Data Scientist Resume Example - Projects

Instacart Market Basket Analysis Model building - used XGBoost with two gradient boosted tree models (predicting reorders, predicting zero orders). Characteristic of each of these models include:

• Reorder model - XGBoost with 6 gradient boosted tree models (GBDT, random seed)
• Zero order model - XGBoost with 17 boosted tree models (with a step shrinkage)

Project insights involved:

• Identified patterns where a user won’t repurchase an item.
• Days since reorder plays an important role.
• Items reordered more frequently vs those that aren’t.
• When a user is unlikely to make a reorder.

How to List Your Education on your Data Science Resume

To list your education on your Data Scientist Resume create a new section for education and list your education credentials in it. Your education section should be concise if you are not an entry level Data Scientist.

Example Education Section in an Experienced Data Scientist Resume

Masters in Data Science, 3.9 GPA Texas A&M University

BS, Data Science, 4.0 GPA Texas A&M University

Example Education Section in an Entry Level Data Scientist Resume

• Coursework taken: Big Data 101, GeoSpatial Computing 309 and Machine Learning.
• Thesis: Leveraging GeoSpatial computing with LIDAR data to predict flooding for urban environments.
• Elected as President of Texas A&M Data Science club of 500+ members.

Top 20 Data Science Resume Skills for 2022

• Machine Learning
• Deep Learning
• Data Visualization
• Neural Networks
• Distributed Computing

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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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How To Create An Impressive Data Science Resume For Entry Level Jobs

Crafting a resume that lands interviews for coveted entry level data science roles can be challenging, especially for recent graduates or career switchers new to the field. This comprehensive guide provides expert tips and examples for developing an impressive data science resume tailored to entry level opportunities.

It covers key sections and content to highlight, formatting best practices, important skills and keywords, and mistakes to avoid.

If you’re short on time, here’s a quick answer: An effective entry level data science resume should showcase relevant coursework, projects, and skills in statistical programming, machine learning, and analyzing large datasets .

Quantify achievements, optimize with key tech/data buzzwords, and highlight both hard and soft skills to demonstrate potential.

Crafting an Impactful Data Science Resume Objective or Summary

When applying for entry-level data science jobs, your resume objective or summary is your chance to make a strong first impression. This section should effectively communicate your skills, qualifications, and career goals to potential employers.

Here are some tips to help you craft an impactful data science resume objective or summary:

What to Include in a Resume Objective/Summary

Your resume objective or summary should be concise yet compelling. It should clearly state your career goals and highlight your relevant skills and qualifications. Here are some key elements to include:

• Your career goals: Clearly state your objective or aspiration in the field of data science. For example, you might mention your desire to apply your analytical skills to solve complex business problems.
• Skills and qualifications: Highlight the technical skills and knowledge you possess, such as proficiency in programming languages like Python or R, experience with machine learning algorithms, and familiarity with data visualization tools.
• Educational background: Include your degree, major, and any relevant coursework or projects. If you have completed any data science certifications or attended relevant workshops, mention those as well.
• Relevant experience: If you have any previous work experience or internships in the field of data science, briefly mention your responsibilities and achievements. Even if you don’t have direct experience, highlight any transferable skills or experiences that demonstrate your ability to excel in a data-driven environment.

Customizing for Specific Data Science Roles

When applying for different data science roles, it’s important to tailor your resume objective or summary to match the specific requirements of each position. Research the job description and company to understand what skills and qualifications they are seeking.

Here are a few tips for customizing your objective or summary:

• Keywords: Incorporate relevant keywords from the job description into your objective or summary to show that you have the specific skills they are looking for.
• Highlight relevant experiences: Emphasize any experiences or projects that align with the requirements of the role. For example, if the job focuses on natural language processing, mention any projects you have worked on in that area.
• Show enthusiasm: Demonstrate your passion for the field and the company by expressing your excitement for the opportunity to contribute to their data science team.

Example Objective and Summary Statements

Here are a couple of examples to give you an idea of how to craft your own data science resume objective or summary:

Objective: Highly motivated data science graduate seeking an entry-level position where I can apply my strong analytical skills and knowledge of machine learning algorithms to solve real-world business problems.

Eager to contribute to a dynamic team and leverage data-driven insights to drive innovation and growth.

Summary: Recent data science graduate with a strong foundation in programming, statistical analysis, and data visualization. Proficient in Python and R, with experience in applying machine learning algorithms to analyze complex datasets.

Passionate about leveraging data to drive strategic decision-making and optimize business performance.

Remember, your resume objective or summary is your chance to make a strong impression and stand out from other applicants. Tailor it to the specific role you are applying for, highlight your relevant skills and experiences, and showcase your enthusiasm for the field of data science.

Highlighting Relevant Data Science Skills and Keywords

When creating an impressive data science resume for entry-level jobs, it is crucial to highlight your relevant skills and keywords that will catch the attention of potential employers. By showcasing your expertise in technical skills, soft skills, and utilizing key buzzwords, you can demonstrate your proficiency in the field and increase your chances of landing a job in data science.

Technical Skills to Include

Employers in the data science industry are looking for candidates with a strong foundation in technical skills. Some essential technical skills to include on your resume are:

• Proficiency in programming languages such as Python, R, or SQL
• Knowledge of statistical analysis and modeling techniques
• Experience with data visualization tools like Tableau or Power BI
• Familiarity with machine learning algorithms and frameworks
• Understanding of big data technologies such as Hadoop or Spark

By highlighting these technical skills, you are showing potential employers that you have the necessary knowledge and tools to excel in the field of data science.

Soft Skills Valued in Data Science

In addition to technical skills, employers also value candidates with strong soft skills. These skills can demonstrate your ability to work effectively in a team and communicate complex ideas. Some important soft skills to include on your resume are:

• Analytical thinking and problem-solving
• Strong communication and presentation skills
• Attention to detail and accuracy
• Ability to work independently and in a team
• Adaptability and willingness to learn new technologies

By showcasing your soft skills, you are demonstrating that you not only have the technical expertise but also the interpersonal skills necessary to succeed in the field of data science.

Optimizing with Key Buzzwords

When crafting your data science resume, it is important to optimize it with key buzzwords that are commonly used in the industry. These buzzwords can help your resume stand out and show that you are familiar with current trends and technologies. Some popular buzzwords in data science include:

By incorporating these buzzwords into your resume, you are showing that you are up-to-date with current industry trends and technologies, making you a more attractive candidate to potential employers.

Remember, creating an impressive data science resume is all about showcasing your relevant skills and keywords. By highlighting your technical skills, soft skills, and utilizing key buzzwords, you can make your resume stand out and increase your chances of landing an entry-level job in data science.

Featuring Academic Projects and Courses

Describing relevant coursework.

When creating an impressive data science resume for entry-level jobs, it is important to feature your academic projects and courses. One way to do this is by describing the relevant coursework you have completed.

Highlight the courses that are directly related to data science, such as statistics, machine learning, and data mining. Provide a brief summary of the topics covered in these courses and any hands-on experience you gained.

This will show potential employers that you have a solid foundation in data science.

Detailing Practical Data Science Projects

In addition to showcasing your coursework, it is crucial to detail practical data science projects you have completed during your academic journey. These projects demonstrate your ability to apply the knowledge and skills you have acquired.

Include a brief description of each project, the techniques and tools used, and the results achieved. Be sure to highlight any unique approaches or challenges you encountered. This will give employers a better understanding of your capabilities and problem-solving skills.

Linking to Project Code and Examples

To further enhance the impact of your data science resume, consider linking to the project code and examples you have developed. This allows employers to see your work firsthand and assess your technical proficiency.

Provide URLs or GitHub repositories where your code and project documentation can be accessed. Additionally, include any relevant visualizations or data analysis outputs that you have created. This not only validates your skills but also adds a visual element to your resume, making it more engaging and memorable.

Remember, when featuring your academic projects and courses, make sure to prioritize those that are most relevant to the position you are applying for. Tailor your descriptions to highlight the skills and knowledge that align with the job requirements.

By showcasing your academic achievements in data science, you can greatly increase your chances of landing an entry-level job in this exciting field.

Listing Work Experience and Leadership

When creating an impressive data science resume for entry-level jobs, it is important to effectively list your work experience and highlight any leadership positions you have held. This section of your resume allows employers to see your practical experience and how you have contributed to previous organizations.

Including Internships and Volunteering

One way to showcase your work experience is by including any relevant internships or volunteering experiences you have had. These opportunities provide valuable hands-on experience in the field of data science and demonstrate your dedication and commitment to learning.

Be sure to mention any specific projects or tasks you were involved in during these experiences, as this will give employers a better understanding of your skills and abilities.

Emphasizing Transferable Skills

While you may not have extensive work experience in the data science field as an entry-level candidate, you can still emphasize transferable skills that are relevant to the role. For example, if you have experience in coding or programming languages such as Python or R, be sure to highlight this on your resume.

Additionally, skills such as problem-solving, critical thinking, and attention to detail are highly valued in the data science industry, so be sure to showcase these skills as well.

Showcasing Leadership Positions

If you have held any leadership positions, whether it be in a student organization or a part-time job, it is important to showcase these experiences on your resume. Leadership positions demonstrate your ability to take initiative, manage teams, and make important decisions.

These qualities are highly sought after in the data science field, as professionals often work in collaborative environments and need to effectively communicate and lead others.

According to a study conducted by LinkedIn, 41% of hiring managers consider leadership experience as a crucial factor when evaluating entry-level candidates for data science positions.

By effectively listing your work experience and highlighting any leadership positions you have held, you can create an impressive data science resume that stands out to employers. Remember to tailor your resume to each job application, focusing on the experiences and skills that are most relevant to the specific role you are applying for.

Formatting and Design Best Practices

Using clear, scannable formatting.

When it comes to creating an impressive data science resume, formatting is key. Hiring managers often receive a large number of applications, so it’s important to make your resume easy to read and scan.

Use clear headings and subheadings to organize your information and make it easier for the reader to navigate. Use bullet points to highlight your skills and achievements, and use a consistent font and formatting style throughout the document.

Remember, you want to make it as easy as possible for the hiring manager to quickly grasp your qualifications and the value you can bring to the position.

Selecting the Right Length

When it comes to resume length, it’s important to strike a balance. While you want to include all relevant information, you also don’t want to overwhelm the reader with a lengthy document. For an entry-level data science resume, it’s generally recommended to keep it to one page, unless you have extensive experience or additional relevant certifications.

Keep in mind that hiring managers typically spend just a few seconds scanning each resume, so it’s crucial to prioritize the most important information and keep it concise and impactful.

Crafting Section Headings Strategically

Section headings are an essential part of your resume’s organization and can help highlight your qualifications and achievements. When crafting your section headings, be strategic in your approach. Use clear and concise headings that accurately reflect the content of each section.

For example, instead of using a generic heading like “Work Experience,” consider using specific headings like “Data Science Internship” or “Research Assistant” to showcase your relevant experience. Additionally, consider using bold or a larger font size to make your section headings stand out and catch the reader’s attention.

Remember, your resume is your chance to make a strong first impression and stand out from the competition. By using clear, scannable formatting, selecting the right length, and crafting section headings strategically, you can create an impressive data science resume that grabs the attention of hiring managers and increases your chances of landing that entry-level job.

For more tips and examples of well-formatted resumes, check out websites like The Muse or Indeed .

In summary, an exceptional data science resume for entry level candidates highlights technical expertise, soft skills, hands-on projects, and a passion for data to stand out. A tailored resume objective, measurable achievements, optimizing keywords, and strong presentation will demonstrate value to employers hiring for data science roles.

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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..

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Data Scientist Resume Templates

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Data Scientist Resume Tips

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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.

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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%.

Other Data & Analytics Resumes

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• Skills and Keywords to Add
• Sample Bullet Points from Top Resumes
• All Resume Examples
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• Data Scientist Cover Letter
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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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16 Data Science Projects with Source Code to Strengthen your Resume

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For the original article click here. 

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

Do you know?

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

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

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

Top Data Science Project Ideas

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

1. Beginner Data Science Projects

1.1 fake news detection.

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

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

Language:  Python

Dataset/Package:  news.csv

1.2 Road Lane Line Detection

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

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

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

1.3 Sentiment Analysis

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

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

Language:  R

Dataset/Package:  janeaustenR

1.4 Detecting Parkinson’s Disease

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

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

Language:  Python

Dataset/Package:  UCI ML Parkinsons dataset

1.5 Color Detection with Python

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

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

Dataset:  Codebrainz Color Names

1.6 Brain Tumor Detection with Data Science

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

Dataset:  Brain MRI Image Dataset

1.7 Leaf Disease Detection

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

Dataset:  Leaf Dataset

2. Intermediate Data Science Projects

2.1 speech emotion recognition.

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

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

Dataset/Package:  RAVDESS dataset

2.2 Gender and Age Detection with Data Science

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

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

Dataset/Package:  Adience

2.3 Diabetic Retinopathy

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

Dataset:  Diabetic Retinopathy Dataset

2.3 Uber Data Analysis in R

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

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

Dataset/Package:  Uber Pickups in New York City dataset

2.4  Driver Drowsiness detection in Python

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

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

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

2.5 Chatbot Project in Python

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

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

Dataset:  Intents json file

2.6 Handwritten Digit Recognition Project

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

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

Dataset:  MNIST

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

3. Advanced Data Science Projects

3.1 image caption generator project in python.

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

Dataset:  Flickr 8K

Framework:  Keras

3.2 Credit Card Fraud Detection Project

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

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

Dataset/Package:  Card Transactions dataset

3.3 Movie Recommendation System

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

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

Dataset/Package:  MovieLens dataset

3.4 Customer Segmentation

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

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

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

Dataset/Package:  Mall_Customers dataset

3.5 Breast Cancer Classification

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

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

Dataset/Package:  IDC_regular

3.6 Traffic Signs Recognition

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

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

Dataset:  GTSRB (German Traffic Sign Recognition Benchmark)

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

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• Entry Level Data Scientist Resume Example

Resume Examples

• Common Tasks & Responsibilities
• Top Hard & Soft Skills
• Action Verbs & Keywords
• Resume FAQs
• Similar Resumes

Common Responsibilities Listed on Entry Level Data Scientist Resumes:

• Data Cleaning and Preprocessing
• Identify and handle missing values, outliers, and inconsistencies in data
• Transform data into a format suitable for analysis
• Exploratory Data Analysis
• Visualize and summarize data to gain insights and identify patterns
• Conduct statistical tests to validate hypotheses
• Model Building and Evaluation
• Develop and implement predictive models using machine learning algorithms
• Evaluate model performance and refine models as needed
• Data Visualization
• Create visualizations to communicate insights and findings to stakeholders
• Use tools such as Tableau or Power BI to create interactive dashboards
• Data Mining
• Identify patterns and relationships in large datasets using techniques such as clustering and association rule mining
• Natural Language Processing
• Develop models to analyze and understand human language, such as sentiment analysis or topic modeling
• Data Integration
• Combine data from multiple sources to create a unified dataset for analysis
• Data Warehousing
• Design and implement data warehouses to store and manage large amounts of data
• Data Governance
• Ensure data quality and accuracy by establishing data governance policies and procedures
• Collaboration and Communication
• Work with cross-functional teams to understand business requirements and communicate findings and insights to stakeholders.

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Entry Level Data Scientist Resume Example:

• Improved database models and querying techniques, increasing query efficiency by 20%.
• Applied machine learning models to forecast customer demand, enabling business to better manage inventory levels.
• Enhanced reporting solutions by developing an innovative data visualization platform, resulting in a 10% increase of meaningful analysis efficiency.
• Automated data analysis pipelines, reducing manual processes and errors by 10%
• Developed A/B tests and experiments to measure the effectiveness of data-driven decisions, leading to a 25% improvement in effectiveness
• Spearheaded the implementation a cybersecurity protocol, safeguarding data and maintaining secure operations
• Built customer segmentation models to enhance the organization’s knowledge of customer demographics and preferences
• Processed and prepared large data sets from four different sources, merging the data into one comprehensive database
• Constructed comprehensive data dashboards for the effective and timely visualization of data, increasing work efficiency by 20%
• Database Modeling
• Machine Learning
• A/B Testing
• Cybersecurity
• Segmentation Modeling
• Data Preparation
• Database Management
• Data Analysis
• Data Dashboards
• Statistical Modeling
• Data Wrangling
• Programming
• Logical Thinking
• Communication
• Problem Solving
• Time Management
• Attention to Detail
• Data Science
• Artificial Intelligence

Top Skills & Keywords for Entry Level Data Scientist Resumes:

Hard skills.

• Statistical Analysis
• Programming (Python, R, SQL)
• Predictive Modeling
• Big Data Technologies (Hadoop, Spark)
• Time Series Analysis
• Deep Learning

Soft Skills

• Analytical and Problem-Solving Skills
• Attention to Detail and Accuracy
• Communication and Presentation Skills
• Collaboration and Teamwork
• Time Management and Prioritization
• Adaptability and Flexibility
• Critical Thinking and Decision Making
• Creativity and Innovation
• Empathy and Customer-Centric Mindset
• Technical Writing and Documentation
• Data Visualization and Storytelling
• Continuous Learning and Self-Improvement

Resume Action Verbs for Entry Level Data Scientists:

• Implemented
• Communicated
• Experimented
• Collaborated
• Transformed

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Top Python Projects to Boost Your Data Science Resume

• Introduction

In the fast-evolving field of data science, showcasing your practical skills can set you apart from other candidates. Python, being a cornerstone programming language in data science, offers a broad spectrum of project opportunities that can significantly enhance your resume. This article explores essential Python projects that every data scientist candidate should consider for their portfolio.

• Key Highlights

Importance of Python in data science

Projects that demonstrate analytical and programming skills

Real-world applications of Python projects

Strategies for selecting and presenting projects on your resume

Tips for documenting and sharing your Python projects

• Understanding the Role of Python in Data Science

Python's simplicity and versatility make it a preferred language for data science. This section delves into why Python holds a pivotal role in the data science domain, exploring its popularity and the essential libraries that power data science projects across the globe.

Python’s Popularity and Applications

Python has emerged as a titan in the data science landscape, thanks to its straightforward syntax and powerful libraries. This programming language's popularity stems not only from its ease of learning but also from its vast ecosystem that supports various data science applications. From startup prototypes to complex machine learning algorithms in multinational corporations, Python finds its utility across the board.

Practical applications of Python in data science include, but are not limited to:

• Automating tasks : Python scripts can automate the mundane data processing tasks that are time-consuming, enabling data scientists to focus on analysis.
• Data analysis and wrangling : With libraries like pandas , Python is instrumental in cleaning, transforming, and analyzing data.
• Machine Learning : Python's scikit-learn library is a go-to for implementing machine learning algorithms efficiently.
• Deep Learning : Frameworks such as TensorFlow and PyTorch enable building complex neural networks for tasks like image and speech recognition.

These applications underscore Python's versatility and why it's the preferred choice for data science projects.

Key Python Libraries for Data Science

Python's strength in data science is significantly attributed to its powerful libraries that cater to different phases of a data science project. Here's a rundown of essential Python libraries and their applications:

• NumPy : Ideal for numerical computing, it provides support for complex mathematical operations and large array processing.
• pandas : A cornerstone for data manipulation and analysis, pandas offer data structures and operations for manipulating numerical tables and time series.
• Matplotlib : This plotting library is perfect for creating static, interactive, and animated visualizations in Python.
• scikit-learn : A tool for predictive data analysis, scikit-learn is versatile in handling classification, regression, clustering, and dimensionality reduction tasks.

Each of these libraries has unique features making them indispensable tools in a data scientist's arsenal. For instance, pandas can be used to merge, reshape, and pivot datasets, while Matplotlib aids in visualizing the data, allowing for insights at a glance. NumPy accelerates the computation process, and scikit-learn offers a straightforward approach to applying machine learning algorithms. Together, they form the backbone of Python's application in data science, enabling professionals to tackle a wide range of data challenges efficiently.

• Project Ideas to Showcase Analytical Skills

In the realm of data science, showcasing your analytical prowess is paramount. This section uncovers a suite of project ideas that not only highlight your ability to dissect and interpret complex datasets but also demonstrate your mastery in leveraging Python for insightful data analysis. From visualizing data narratives to unraveling patterns through statistical methodologies, these projects are your ticket to a standout data science resume.

Crafting Data Visualization Projects

Data visualization is an art and science, pivotal for communicating complex analyses effortlessly. Python, with its rich library ecosystem, offers unparalleled tools for crafting vivid, interactive visual narratives. Here are some project ideas that can significantly enhance your data storytelling capabilities:

• Time Series Analysis Visualization : Dive into financial datasets or climate patterns to depict trends, seasonality, and outliers. Tools like Matplotlib and Seaborn can help illustrate these time-bound nuances.
• Geospatial Data Mapping : Leverage Geopandas alongside Folium to map out geographical datasets. Whether it's visualizing global trade routes or the spread of a pandemic, geospatial visuals can offer profound insights.
• Interactive Dashboards with Dash/Plotly : Construct dashboards that allow users to explore data through interactive elements. Retail sales data, sports statistics, or even election results can be made engaging and exploratory.

For each project, ensure to start with a compelling dataset and a clear question you aim to answer. Documentation and narrative are key; weave a story around your data to make your visualizations not just seen, but also understood and remembered.

Embarking on Statistical Analysis Projects

Statistical analysis stands at the core of data science, enabling the extraction of meaningful insights through rigorous methodologies. Python's statistical ecosystem, spearheaded by libraries such as SciPy and StatsModels , provides a robust foundation for conducting sophisticated analyses. Here are project ideas that can showcase your statistical acumen:

• A/B Testing for Website Optimization : Utilize historical website data to conduct A/B tests, aiming to improve user engagement or conversion rates. This project not only demonstrates your grasp of experimental design but also your ability to impact business outcomes directly.
• Market Basket Analysis : Implement the Apriori algorithm to uncover associations between products from retail transaction data. Such analyses can inform cross-selling strategies and product placement decisions, vital for retail analytics.
• Econometric Modeling : Explore economic datasets to model relationships between variables using regression analysis. Projects can range from predicting housing prices to analyzing the impact of policy changes on economic indicators.

The key to a successful statistical project is in framing a clear hypothesis and employing the appropriate statistical tests to validate your assumptions. Comprehensive documentation that details your methodology, findings, and implications will make your project stand out to potential employers.

• Showcasing Machine Learning Mastery with Python Projects

In the rapidly evolving field of data science, machine learning stands out as a critical competency. Mastering machine learning with Python not only demonstrates your analytical prowess but also your ability to predict and influence future trends. This section outlines project ideas that spotlight your machine learning skills through Python, offering a blend of predictive modeling and natural language processing projects.

Crafting Predictive Models with Python

Predictive modeling projects are the cornerstone of showcasing your machine learning expertise. These projects involve analyzing historical data to forecast future events, which is invaluable across various industries.

• Stock Market Prediction: Use Python libraries like pandas for data manipulation and scikit-learn for building regression models to predict stock prices. Incorporate time series analysis to enhance your model's accuracy.
• Customer Churn Prediction: Develop a model that predicts customer churn for businesses. This involves analyzing customer behavior data to identify patterns that precede churn, enabling businesses to take preemptive action.

For a detailed guide on building a predictive model, consider exploring resources such as Towards Data Science . Remember, clearly documenting your methodology and findings, and hosting your code on platforms like GitHub, will significantly boost your project's professional appeal.

Exploring Natural Language Processing (NLP) with Python

Natural Language Processing (NLP) projects leverage Python to analyze and interpret vast amounts of text data. These projects demonstrate your ability to extract meaningful insights from unstructured data, a highly sought-after skill in today’s data-driven world.

• Sentiment Analysis: Create a sentiment analysis model to gauge public sentiment from social media posts or product reviews. Utilize libraries like nltk or spaCy for text processing and sentiment classification.
• Chatbot Development: Build a Python-based chatbot that understands and responds to human queries. This project tests your skills in both NLP and machine learning, challenging you to implement models that can understand context and nuances in language.

Projects in NLP not only showcase your technical capabilities but also your creativity in problem-solving. Resources such as Natural Language Processing with Python provide excellent starting points for diving into NLP projects. Remember, a well-documented project, shared on platforms like GitHub, can serve as a powerful testament to your skills.

• Real-World Python Projects to Elevate Your Data Science Resume

In the bustling field of data science, showcasing your proficiency in Python through real-world projects can set you apart. This section dives into practical project ideas that not only demonstrate your Python skills but also solve tangible problems, significantly boosting your resume.

Mastering Web Scraping with Python for Comprehensive Data Collection

Web scraping is a potent tool in a data scientist's arsenal, allowing for the extraction of vast amounts of data from the web. Utilizing libraries such as BeautifulSoup and Scrapy , you can collect data that's crucial for market analysis, sentiment analysis, and more.

For instance, scraping product reviews from e-commerce platforms can provide invaluable data for sentiment analysis, helping businesses understand customer satisfaction. Similarly, gathering financial data from various sources can aid in market trend analysis.

A practical application could be building a scraper to collect real estate listings to analyze housing market trends. This project not only demonstrates your ability to gather data but also your understanding of its practical applications in real-world scenarios.

Remember, while web scraping is powerful, it's essential to adhere to legal and ethical standards. Always check a website's robots.txt file and ensure you're compliant with their terms of service.

Designing a Recommendation System with Python

Recommendation systems are at the heart of the user experience in many popular platforms like Netflix and Amazon, making them an excellent project to showcase on your resume. By leveraging Python's machine learning libraries, such as scikit-learn and TensorFlow , you can create systems that analyze user behavior and preferences to suggest relevant items.

A project idea could involve developing a recommendation system for a bookstore, suggesting books based on a user's previous purchases and browsing history. This project not only highlights your machine learning skills but also your ability to apply those skills to enhance user engagement and satisfaction.

For a more in-depth project, you could incorporate natural language processing (NLP) techniques to analyze book reviews and ratings, further refining your recommendations. This demonstrates not only your technical proficiency but also an understanding of how to leverage data to drive business value.

Such projects are not only technically challenging but also highly relevant in today's data-driven market, making them perfect additions to your data science portfolio.

• Finalizing and Presenting Your Projects

In the realm of data science, the culmination of your hard work often materializes in the form of projects. However, the journey doesn't end with just completing these projects; how you finalize and present them can make a significant difference. This segment offers adept advice on documenting and sharing your projects, ensuring they stand out to potential employers.

Documenting Your Projects

Documenting your projects is not just about ticking a box; it's an opportunity to narrate the story of your project. Clear, comprehensive documentation ensures that your project is accessible, understandable, and usable by others. Here are some tips to elevate your project documentation:

Start with a README : Begin with an engaging README file that outlines the project scope, objectives, and outcomes. Use Markdown for formatting to make it visually appealing.

Include Comments in Your Code : Making your code self-explanatory with comments can significantly aid understanding. Brief comments explaining the logic behind crucial code segments can be very helpful.

Use Jupyter Notebooks : For Python projects, Jupyter Notebooks can be a powerful tool to combine code, outputs, and narrative in a single document. This can make your project more interactive and easier to grasp.

Provide Installation and Running Instructions : Ensure that anyone trying to replicate your project knows exactly how to set it up. Include details about dependencies, environment setup, and execution instructions.

Remember, the goal is to make your project as approachable and understandable as possible. Good documentation not only demonstrates your technical abilities but also your communication skills, a crucial asset in data science.

Using GitHub to Showcase Your Work

GitHub has become the de facto portfolio platform for developers and data scientists alike. It's not just a repository to store your projects; it's a showcase of your coding journey. Here’s how to make the most of GitHub for your data science projects:

Create a Clean Repository for Each Project : Each project should have its own repository with a clear, descriptive name. This makes it easier for potential employers to navigate your work.

Make Use of GitHub Pages : GitHub Pages allows you to turn your project repositories into sleek websites. This is especially useful for projects like data visualizations or interactive applications.

Leverage the 'README' File : The README file is your first interaction with visitors. Use it to explain your project, what problem it solves, and how it works. Incorporating visuals or links to live demos can be very engaging.

Include a Link to Your GitHub on Your Resume : Make it easy for employers to find your work. Including a direct link to your GitHub profile on your resume or LinkedIn profile can increase visibility.

Remember, your GitHub profile is an extension of your resume. Keeping it organized, updated, and rich with interesting projects can significantly boost your chances of catching an employer's eye. For more insights on optimizing your GitHub presence, consider exploring resources like GitHub's own guides .

Python projects not only demonstrate your technical skills but also your problem-solving capabilities and creativity. Selecting the right projects for your resume and presenting them effectively can significantly impact your job search success as a data scientist candidate. Embrace the challenge and let your projects speak volumes about your capabilities.

Q: Why are Python projects important for a data scientist's resume?

A: Python projects demonstrate practical skills, problem-solving abilities, and creativity, which are crucial for distinguishing yourself as a data scientist candidate. They provide tangible evidence of your expertise and can significantly enhance your resume.

Q: What type of Python projects should I include on my resume?

A: Include projects that showcase your analytical skills, machine learning proficiency, and ability to apply Python in solving real-world problems. Projects involving data visualization, statistical analysis, predictive modeling, and natural language processing are highly recommended.

Q: How can I showcase my Python projects to potential employers?

A: You can showcase your Python projects by documenting them comprehensively and sharing them on platforms like GitHub. Ensure your documentation is clear and includes an overview, objectives, methodologies, results, and conclusions. A well-maintained GitHub repository can act as a portfolio for potential employers.

Q: What are some key Python libraries I should be familiar with for data science projects?

A: Key Python libraries for data science projects include NumPy for numerical computations, pandas for data manipulation, Matplotlib for data visualization, and scikit-learn for machine learning. Familiarity with these libraries can significantly enhance the quality of your projects.

Q: How do I select the right Python projects for my resume?

A: Select projects that align with your career goals and the job requirements you're targeting. Projects should demonstrate a breadth of skills and a depth in areas where you specialize or wish to specialize. Consider projects that solve real-world problems or showcase innovative solutions.

Q: Can working on Python projects improve my chances of getting hired as a data scientist?

A: Absolutely. Working on Python projects not only improves your technical skills but also demonstrates your ability to apply those skills to solve complex problems. This practical experience is highly valued by employers and can significantly improve your chances of getting hired as a data scientist.

Q: How important is documenting my Python projects for my resume?

A: Documenting your Python projects is crucial for your resume. It allows you to communicate the significance of your work, the problems you solved, and how you approached them. Good documentation makes it easier for potential employers to understand your projects and assess your skills.

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Data Science Projects to Boost Your Resume

• April 18, 2024
• Machine Learning

As a Data Science fresher, you should mention and work on projects that can show most of your skills in solving a Data Science problem. Always make sure you are working on a project based on a real-time business problem. So, if you want to know what kind of Data Science projects will help you to boost your resume, this article is for you. In this article, I’ll take you through a list of Data Science projects based on real-time business problems from various domains that will help you boost your resume.

Below is a list of Data Science projects based on real-time business problems from various domains that will help you boost your resume.

Data Analytics & Data Manipulation:

• Electric Vehicles Market Size Analysis
• Food Delivery Cost and Profitability Analysis
• Delhi Metro Network Analysis
• Quantitative Analysis
• Stock Market Comparison Analysis
• Fitness Watch Data Analysis
• RFM Analysis
• B2B Courier Charges Accuracy Analysis
• Supply Chain Analysis
• App Reviews Sentiment Analysis
• Cohort Analysis

Machine Learning and Statistical Modelling:

• Music Recommendation System using Spotify API
• Dynamic Pricing Strategy
• User Profiling & Segmentation
• End-to-End Predictive Model
• Car Insurance Modelling
• Ads CTR Forecasting
• Search Queries Anomaly Detection
• Stock Market Anomaly Detection
• Classification on Imbalanced Data
• Demand Forecasting & Inventory Optimization
• Credit Scoring & Segmentation

Deep Learning and Advanced NLP:

• Fashion Recommendation System Using Image Features
• Text Generation Model
• Next Word Prediction Model
• End-to-End Chatbot

Data Engineering:

• Data Collection with APIs for Dataset Creation
• Web Data ETL Pipeline
• Data Preprocessing Pipeline

So these are Data Science projects based on real-time business problems from various domains that you should try to mention on your resume. Identify the right projects that are based and relevant to the domain and company you are preparing for and work on them.

I’ll keep updating this list with many more projects. You can find many more projects to practice your Data Science skills here .

So, as a Data Science fresher, you should mention and work on projects that can show most of your skills in solving a Data Science problem. Always make sure you are working on a project based on a real-time business problem.

I hope you liked this article on Data Science projects to boost your resume. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.

Aman Kharwal

Data Strategist at Statso. My aim is to decode data science for the real world in the most simple words.

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