10+ Data Scientist Resumes Examples 2024 (Complete Guide)

“Entry-level data science jobs don’t exist.” It’s a phrase you’ll hear a lot in the industry, and while it might sound pessimistic, there is truth behind it. 

Employers are searching for top-tier talent, setting the bar high and favoring those with extensive experience. This can make it tough for newcomers to enter the field, often leading them to take on similar roles like a data analyst instead.

But here’s the good news: breaking into data science is far from impossible. Your resume is your first opportunity to prove you have the potential to contribute toward a company’s success — even without a wealth of experience. 

This guide will show you: 

• Data science resume examples across different industries and experience levels. 
• How to choose the right data scientist skills, formatting, and summary for your resume. 
• The best ways to elevate your data scientist resume to land an interview. 

Data Scientist Resume Examples

The World Economic Forum’s Future of Jobs 2023 report predicts data scientist jobs will grow by 30 – 35% by 2027. Demand is increasing, so why is it so difficult to get work as a data scientist? 

A big part of it is the skills gap. With rapid digital transformation and the boom in machine learning, employers have set their expectations sky-high and want candidates with expertise in the latest tools and languages.

I’ll be honest, refining your resume is just one step toward getting noticed — but it’s still an important step to landing your dream job. The goal is to show employers that you understand their expectations and have the skills, drive, and knowledge to meet them. 

Let’s start with resumes tailored to each data scientist’s specialty. Below, you’ll find the key details, technology, and tools to include in your resume. 

Python Data Scientist Resume

For a Python Data Scientist resume, highlight your expertise in Python libraries like Pandas, SciPy, NumPy, Matplotlib, and Scikit-learn. Emphasize your skills in using these libraries for visual data representations, data manipulation, and numerical computing. Demonstrate your problem-solving skills, using Python to streamline tasks and deliver insights. 

NLP Data Scientist Resume

Your NLP Data Scientist resume should emphasize your experience with machine learning and how you’ve applied your knowledge to understand and process human language. Mention your work on chatbots, translation software, or AI personal assistants. Focus on projects where you’ve enabled systems to interpret and respond to natural language. 

Healthcare Data Scientist Resume

With a Healthcare Data Scientist resume, focus on your ability to manage and analyze patient and healthcare data. Highlight your experience creating predictive models using machine learning to forecast medical conditions or treatment outcomes. Show your skills in data cleaning, analysis, and presenting insights that drive better healthcare decisions. 

Marketing Data Scientist Resume

Your Marketing Data Scientist resume should highlight your expertise in data analysis with tools like SQL, R, Python, and Tableau. Emphasize experience in improving marketing campaigns and developing recommendation systems. Show your ability to analyze customer data to craft personalized marketing strategies. 

Full Stack Data Scientist Resume

For a Full Stack Data Scientist resume, emphasize your ability to handle the entire data science process — from data collection and feature engineering to model building, optimization, and deployment. Highlight your skills in creating and refining models, building pipelines, and understanding the business impact of your work. 

Educational Data Scientist Resume 

For an Educational Data Scientist resume, show your proficiency in data analysis and model development to support educators. Emphasize your experience in ensuring data security for students and staff, alongside building systems for data organization. Clarify that you can uncover patterns that drive improvements in educational outcomes.

Consultant Data Scientist Resume 

With a Consultant Data Scientist resume, focus on your ability to provide strategic advice by using data to solve business problems and drive growth. Highlight your skills in identifying relevant data sources, developing customized models, and offering tailored solutions that align with client goals. Showcase your foundation in statistics, programming, and analytics. 

Let’s dive into customizing your resume to align with your experience level. 

Internship Data Scientist Resume

Your Internship Data Scientist resume should focus on your education with relevant coursework, GPA, and data-related projects. Since work experience might be limited, emphasize any volunteer work or passion projects where you applied data science skills with specific tools and methods. Use a resume objective to outline your career goals and aspirations in data science. 

Entry-Level Data Scientist Resume

For an Entry-Level Data Scientist resume, emphasize your skills in programming languages like Python or R and tools such as SQL and Tableau. Highlight relevant coursework or certifications in data science, programming, statistics, or data analysis. Include any notable projects, internships, data boot camps, or publications, and quantify your achievements when possible.

Junior Data Scientist Resume 

With a Junior Data Scientist resume, emphasize your understanding of mathematics, statistics, programming, and databases. Mention experience with data extraction, model selection, and integrating data. If you lack experience, show your motivation, passion, and eagerness to rise through the ranks. Stress your ability to simplify data problems and present results. 

Senior Data Scientist Resume

For a Senior Data Scientist resume, highlight your expertise in converting raw data into actionable insights for business decisions. Emphasize your strong foundation in machine learning techniques, and your ability to train and mentor junior data scientists. Mention your experience with big data tools like Hadoop, Spark, and NoSQL databases. Stress your skills in data visualization and storytelling to communicate complex information. 

How to Write a Data Scientist Resume

Short answer:
To write an effective data scientist resume, start with a clear header including your name, contact information, and portfolio links. Follow with a concise summary or objective that highlights your key skills, accomplishments, and what you offer. Detail your work experience and data projects, focusing on the tools you used and any quantifiable achievements. Include your education, courses, and certifications relevant to data science. List your technical skills such as Python, R, and SQL. Use a clean, reverse-chronological format for easy readability and emphasize the most recent and impactful details. 

Choose the right data scientist resume format

Recruiters sift through dozens of resumes to find the right candidates — they don’t have time to hunt down the key details. Your skills and experience need to jump off the page, so recruiters can see if you’re a good match. 

Make sure they see your potential by using the reverse chronological format. Recruiters are most familiar with this layout, so they’ll know exactly where to find the information they need. Start with your most recent job and education, then work your way back. This approach highlights your latest achievements and shows your career progression. 

Here’s how to structure your data scientist resume:

1. Header and contact information
2. Summary or objective 
3. Work experience 
4. Notable projects 
5. Education and certifications
6. Technical skills 

Use clear headings to separate each section and bullet points to outline your work experiences — just enough detail to show your accomplishments without overwhelming the reader. Even if you’ve got years of experience, try sticking to one page to stay focused on what matters. 

Learn the best tricks on presenting your resume: How to Format a Resume & What Standard Resume Format to Use

Align your resume with the job description

We’re always adapting — you wouldn’t spill the beans on that bachelor party at a church gathering or rave about your love of meat to a group of vegans. Adapting your resume is about including the details that matter and removing anything unnecessary. 

So, how do you figure out what to include? Check the job ad. 

Read the job description to identify the abilities and qualifications the company wants in a candidate so you can include them throughout your work experience and skills section. Just make sure you can back up everything you say (you don’t want to draw blanks in the interview). 

If the job mentions specific tools, programming languages, or methodologies, weave them into your resume. This not only shows that you’re a good fit but also helps your resume get through applicant tracking systems (ATS) that screen for specific keywords. 

Go the extra mile by checking out the company’s website. Pay attention to their mission, values, and the tone they use so you can tweak your resume’s style and tone to fit what they’re looking for. And yes — customizing your resume takes extra effort, but it shows you’ve done your homework and are genuinely interested in the role.

Discover more about using the job ad to your advantage: How to Target a Job Description With Your Resume

Include your contact information

If you can handle the ins and outs of data, writing your name and contact details should be a breeze. Maybe too easy — it’s not uncommon for candidates to miss an opportunity because of a simple typo (my subtle way of reminding you to double-check). 

Your contact info should be at the top, below your name and header. This is where recruiters will look to get in touch with you, so make sure it’s clear and up-to-date. Here’s a quick rundown:

• Name and headline. Start with your name in a clear, professional font, followed by a resume header that reflects the job you’re aiming for, like “Aspiring Data Scientist” or “Data Science Professional.” 
• Email. Avoid using old emails like [email protected] — it doesn’t exactly scream professionalism. Using an email with your first and last name is a safe bet.
• Phone number. Pretty straightforward — just make sure it’s updated.
• Location. You don’t need to include your full address; just your city, state, and zip code are enough
• Links. Include links to your LinkedIn, GitHub, and any other relevant profiles. Employers often view your portfolio, articles, or side-projects to see your range of data science skills. 

Here’s what your contact information should look like on your data scientist resume: 

John Smith
Experienced Data Scientist

Email: [email protected]
Phone: (123) 456-7890
Location: San Francisco, CA 94105
LinkedIn: linkedin.com/in/johhsmith
GitHub: github.com/johnsmith

Find out more: What Sections to Include on Your Resume?

Outline your data science experience and impact 

Your resume is your tool for getting your foot in the door. How you frame your professional background shows recruiters you have the potential to succeed in their company — even without a ton of jobs under your name. 

Begin by listing your roles in reverse chronological order, with your most recent job at the top. Include internships or apprenticeships if you’re light on experience — they count too. For each role, start with your job title, the company name, and the dates you worked there.

Now, you could just write a bulleted list of duties and responsibilities, but you’ll start sounding like the job listing rather than a candidate. Your goal is to show you’re not just capable of doing the job — but you’ve made a real difference in your past roles and can do the same for them with tangible benefits. 

Here’s an example of a NLP data scientist work experience section: 

Junior ​​Data Scientist 
Innovate, Boston, MA
June 2023 – July 2024

• Assisted in the creation of a text summarization model using deep learning techniques, resulting in a 30% reduction in the time needed to generate reports.
• Supported senior data scientists in analyzing large datasets of unstructured text to extract insights, contributing to a 15% improvement in client reporting accuracy.
• Researched and implemented the latest NLP algorithms to boost the performance of existing models, leading to a 10% increase in processing efficiency.
• Presented insights from NLP models, helping to guide strategic decisions on customer communication and engagement strategies.

Back up your claims with evidence. Don’t just say you improved something — show it with numbers. For instance, mention how the machine learning model you built reduced costs by 20% or how your analysis led to a 30% increase in sales. These concrete metrics help the employer see the value you could bring to their company.

Use strong action verbs like “developed,” “implemented,” or “analyzed” to describe your duties. For instance, instead of saying you “worked on data models,” say you “developed predictive models that increased revenue by 15%.” See how much more powerful that sounds? 

Learn more about framing your achievements: How to Describe Your Work Experience on a Resume.

Mention any projects or publications 

Employers want to see your project portfolio to understand the variety and depth of your work. Skipping this section is frankly a missed opportunity. Dedicating a section to projects works especially well for two key reasons: 

1. You want to showcase your skills with concrete examples and real results.
2. You’re light on work experience but want to show employers your potential. 

Projects hold a lot of weight in data science. They paint a picture of how you’ve used your knowledge and skills in real-world scenarios. Seasoned data scientists won’t need to lean on this section as much — but for those entering the field, it’s a great way to bulk up your resume.

Choose the most relevant and impactful projects, especially those that align with the job. For each project, give a brief but specific rundown of the data sources, technologies, programming languages, and tools you used. The goal is to be clear and informative without drowning the reader in technical jargon.

Here’s how to present your data scientist projects: 

Predictive Maintenance System for Manufacturing Equipment
• Developed a predictive maintenance system using Python, TensorFlow, and Scikit-learn to reduce downtime for manufacturing equipment by predicting failures. 
• Achieved 92% accuracy in predicting equipment failures, resulting in a 15% reduction in unplanned maintenance and an increase in production efficiency.

Customer Segmentation for E-commerce Platform
• Created a customer segmentation model using K-means Clustering, and Pandas to enhance targeted marketing efforts.
• Identified key customer groups, leading to a 25% increase in conversion rates and a 20% boost in overall sales.

Personal projects also tell employers that you’re proactive and eager to learn. Have you dabbled in data science blogging or contributed to open-source projects? Drop those in this section to show off your skills. 

Got any data science publications under your name? List them with full citations. You’re showing your expertise and commitment to the field — and anything that gives you an edge is worth adding to stand out from other candidates. 

Find out more: How to Make Projects on a Resume Look Good

Add your education and certifications

Many data scientists hold at least a master’s degree, so there’s no need to mention your high school days. Start with your most recent degree and work your way back. For each one, include the name of your university, your major, and the year you graduated.

If you’re a fresh graduate, you can place your education and project sections before your work experience to switch the focus. Got a standout GPA? List it. You can also highlight relevant coursework like machine learning, statistics, or data visualization to catch the recruiter’s eye.

Have you won any awards or been part of any academic societies? Don’t be afraid to sell yourself and show recruiters you’ve been recognized for your hard work. 

Add certifications from learning platforms like Coursera, edX, or Udacity to show your commitment to continuous learning. Make sure these certifications are relevant to the job you’re applying for, such as certifications in Python or SQL. 

Here’s an example of how to structure your data scientist education section:

Master of Science in Data Science
University of California, Berkeley (Berkeley, CA)
Graduated: May 2023
GPA: 3.9/4.0
Coursework: Machine Learning, Advanced Statistics, Data Visualization, Big Data Analytics
Achievements: Dean’s List, Member of the Data Science Society

Bachelor of Science in Computer Science
University of Michigan (Ann Arbor, MI)
Graduated: May 2021
GPA: 3.8/4.0
Coursework: Algorithms, Python Programming, Database Systems, Artificial Intelligence
Achievements: Graduated with Honors, President of the AI Club

Certifications:
Google Data Analytics Professional Certificate (Coursera)
Python for Data Science (edX)

As you gain more experience, you can lose the bachelor’s degree and narrow down your descriptions. Replace your education achievements with accomplishments at the workplace, and your college coursework with real-life professional situations. 

Get the full lowdown: How to List Education on a Resume

List your data-related hard skills 

Data science is all about your technical prowess. While your hard skills should be woven into your work experience, adding a straightforward list to your resume makes it easy for recruiters to see if you tick the right boxes. 

Focus on the in-demand skills directly relevant to data science and the job description. List programming languages, tools, libraries, and frameworks you’re truly proficient with — just make sure you can them back up with real experiences.

Here are some popular data scientist skills to include in your resume: 

• Programming and Coding (Python, SQL, R)
• Machine Learning (TensorFlow, PyTorch, Scikit-Learn)
• Data Visualization (Tableau, Power BI, D3.js)
• Big Data (Spark, Kafka, Snowflake)
• Model Training (Google AutoML, Supervisely, Domino)
• Data Wrangling (Alteryx APA, Trifacta, Datameer)
• Natural Language Processing (Gensim, SpaCy, Natural Language Toolkit)
• Data Analytics (Local Jupyter, BI Tool, Databricks)

Don’t go overboard by listing every skill you’ve ever touched — that usually backfires. If your skills list feels over-the-top or doesn’t align with your work experience, you’ll lose credibility fast. Stick to the skills you’re truly comfortable with and can discuss confidently in an interview. 

Now, what about soft skills? Employers aren’t as concerned about whether you can hold a conversation. Sure, soft skills matter, but they should be implied naturally in your work experience instead of taking up valuable space in your skills section.

For example, have you presented data findings to non-technical stakeholders or collaborated on cross-functional teams? You’re already showing strong communication skills. Managed a project? Mention how you led the team or mentored others to highlight your leadership abilities.

Learn more about making your skills shine: How to Put Skills on a Resume

Wrap up with a strong summary

Whether it’s a flashy storefront or a welcoming smile, there are plenty of ways to grab attention and spark interest. On your resume, that job falls to your summary.

Your resume summary sits under your name and header, giving a quick snapshot of your key skills and achievements in 2 to 3 sentences. Mention the specific position you’re targeting and how you can benefit the company. 

Don’t hold back — this is your moment to hook the recruiter and stand out from the stack of resumes they’re wading through.

Here’s an example of a data scientist summary: 

Experienced Data Scientist with a strong background in machine learning, predictive analytics, and data visualization. Proven track record of developing data-driven solutions that boost efficiency and drive business growth, including a predictive model that increased customer retention by 20%. Passionate about leveraging big data to solve complex problems and eager to contribute my expertise to [Company Name] in the role of [Specific Position].

If you’re a recent graduate or someone with limited data science experience, opt for a resume objective instead. An objective lets you focus on future career goals and the value you aim to bring to the company. 

Here’s what a data scientist’s objective could look like: 

Recent graduate with a Master’s degree in Data Science and hands-on experience in Python, R, and SQL through academic projects and internships. Eager to apply my analytical skills and passion for data to help [Company Name] optimize processes and uncover actionable insights. Seeking an entry-level Data Scientist role where I can grow my skills and contribute to meaningful projects. 

Got writer’s block? Save your resume summary for last. Once you’ve got the rest of your resume down, it’ll be easier to pick out your most impressive skills and achievements that align with the job. 

You can also try our Rezi AI Resume Summary Generator. Just enter your job title and skills, and our tool will quickly create a tailored resume for you.

What Makes a Data Scientist Resume Different

In short: focus on your technical skills and back them up with quantifiable results. 

The barrier to entry is high for data scientists, with employers looking for mid-level or senior candidates to solve their data challenges. But what if you’re confident you have all the right skills and knowledge to succeed? 

Instead of being defined by your experience level, impress future recruiters with your quantifiable achievements, technical skills, and drive. The key is to spotlight your strengths and align them with what the employer wants. 

Quantify your achievements and impact

Your mathematical skills are top-notch, so flex them in your resume. Quantifying your achievements shows the real impact of your work and adds more depth to your experiences. Numbers are a language everyone understands — even non-technical recruiters will be impressed by a solid percentage increase. 

What this means for you: 

• Use metrics to highlight key performance indicators (KPIs) like revenue increases or cost reductions. If the job description describes specific KPIs, try to align these with quantifiable impacts from your professional background. 
• Anyone could say they “improved efficiency” — what did you do exactly? Be specific about your results, for example: “Optimized a machine learning model that reduced processing time by 25%, saving the company $50,000 annually”.

Focus on your data science hard skills

While communication and organization are solid skills in any job, it won’t check those all-important technical boxes. Employers have problems to solve and questions to answer — they need to see you have the right technical abilities to find solutions. 

What this means for you: 

• Weave your hard skills into your work experiences by highlighting how you’ve used them to achieve tangible results, like building models or analyzing data sets. Add specific tools and programs you’re proficient in to get past ATS scanners. 
• Recruiters can spot an amateur a mile off if they list every skill they’ve ever dabbled in (don’t do this). Be selective and only list skills you’re genuinely proficient in and comfortable discussing during an interview. 

Balance technical expertise with straightforward language

Writing a data scientist resume is a balancing act between showing off your expertise and using language that a non-technical recruiter can grasp. The last thing you want is for them to zone out while reading about all your impressive programming abilities. 

What this means for you: 

• Explain complex duties in simple terms and spell out technical acronyms. For example, instead of “utilized NLP algorithms,” say “used natural language processing techniques to improve customer feedback analysis.”
• Focus on the results your hard skills achieved, like “reduced processing time by 30%”. Anyone can recognize increased percentages as a win without getting lost in technical details.

Prove your ability to work in a team

Data science is a team effort — if your collaboration skills aren’t sharp, you could hold everyone back. So, should you list your interpersonal skills on your resume? Not exactly. Technical skills get dibs on your resume skills section, so you’ll need to find other ways to prove you’re a team player. 

What this means for you: 

• Mention projects where you worked closely with others, especially cross-functional teams. For example, “Collaborated with marketing and engineering teams to develop a predictive model that increased customer retention.”
• Showcase communication skills by describing cases where you led projects, presented data findings to non-technical stakeholders or collaborated with data analysts. Be specific and let your people skills speak for themselves. 

Show continuous growth and education 

Data science is constantly evolving, with new trends emerging all the time. No matter how skilled or experienced you are, it won’t matter if you can’t keep up. Showcasing your ongoing growth and education highlights your dedication to staying relevant in this fast-paced field.

What this means for you: 

• Include any recent data science courses, certifications, or online learning programs you’ve completed. Mention specific skills or tools you’ve mastered that are relevant to the job.
• List any data-related conferences or hackathons you’ve participated in. This shows your active engagement with the data science community and your drive to innovate and collaborate.

Bonus Resources for Data Scientists

Getting hands-on experience is key to overcoming the challenges surrounding the data science skill gap, but there are other ways to impress recruiters. Online courses can give you the foundational skills and specific technical know-how that employers are looking for. 

Adding courses to your resume also shows you’re eager to learn and keep updated with the latest technology and trends in data science. This can give you an edge over other candidates with the same education and skill set 

Check out these learning platforms for courses and certifications to give your resume some extra flare. 

Coursera

Coursera is an online learning platform offering a range of courses from top universities and companies. You’ll find plenty of data science courses, covering everything from basics to advanced topics. You can earn certificates and even complete specializations to boost your data science resume and skills.

Here are some popular data science courses: 

• Deep Learning Specialization
• Python for Everybody Specialization
• IBM Data Science Professional Certificate
• Databases and SQL for Data Science with Python

Harvard University

If you want the Harvard stamp of approval without the mammoth tuition fees, you’ve come to the right place. Harvard offers data science courses for free, with an extra charge if you want a certification. While it’s not a degree, having “Harvard” on your resume can add a touch of prestige and show your commitment to learning.

Here are some free courses worth checking out: 

• Statistics and R
• Machine Learning and AI with Python
• Introduction to Data Science with Python
• Digital Humanities in Practice: From Research Questions to Results
• Principles, Statistical and Computational Tools for Reproducible Data Science

Udemy

Udemy offers courses on a range of topics, including many in data science. You’ll find everything from beginner to advanced courses on topics, such as machine learning, data analysis, and data visualization using Python and R Programming. 

• Statistics for Data Science and Business Analysis
• 100 Days of Code: The Complete Python Pro Bootcamp
• Data Science: Deep Learning and Neural Networks in Python
• The Data Science Course: Complete Data Science Bootcamp 

Summary

Here’s a rundown of the steps you should follow when creating a data scientist resume:

• Include a strong summary of your key skills, achievements, and what you bring to the table to catch the recruiter’s attention. 
• Focus on positive results and accomplishments throughout your work experience section. Use numbers and metrics to demonstrate the real impact of your work, like increasing efficiency or improving model accuracy.
• List your degrees, relevant coursework, certifications, and any academic honors or societies in your education section. Stick to post-secondary degrees that are relevant to the position. 
• Include a section for projects where you describe your hands-on experience, the tools you used, and positive results, so recruiters can see your skills in action. 
• Mention any recent courses, certifications, conferences, or hackathons to show you’re staying up-to-date and are passionate about your professional growth. 
• Focus on your technical skills with programming languages, tools, and technologies you’re proficient with, and make sure they align with the abilities listed in the job listing. 
• Customize your resume for each job by aligning your skills and experiences with the job description. Check out the company’s goals and values, so how your own ambitions are a good match. 
• Keep it concise and try sticking to one page, especially if you’re early in your career. Recruiters often spend less than ten seconds on each resume, so focus on what really matters.
• Make sure your resume is easy to read with clear headings, bullet points, and consistent formatting.
• Don’t let a typo ruin your chances. Double-check everything to ensure your resume is polished and professional.

FAQ

How to write a data science student resume? 

When crafting a data science student resume, lead with your education and focus on showing your potential. Highlight relevant coursework, projects, and internships that demonstrate your skills and hands-on experience. 

Include a resume objective that shows your passion for data science and your eagerness to contribute. Employers know you’re just starting out, so use this to your advantage by showing your willingness to learn and grow.

What to include on a data scientist’s CV? 

In most parts of the world, a Curriculum Vitae (CV) is the same as a resume in the US. However, in the US, a CV goes beyond a simple career summary. It’s an in-depth document that outlines your entire academic and professional journey. CVs are commonly used for academic, research, or other roles that require a thorough account of your professional and academic background.

Your data scientist CV should include detailed sections on your work experience, emphasizing your role in projects and how your contributions led to measurable outcomes. Highlight your academic background, including degrees, publications, and research experience. Add a section for technical skills, certifications, and any presentations to demonstrate your expertise.

Learn more: CV (Curriculum Vitae) vs. Resume: The Difference Explained

Where to list my skills in the data science resume?

Your skills should have a dedicated section on your data science resume, typically placed in a margin at the side or below your education and projects section. Use bullet points to list technical skills like programming languages (Python, R), tools (TensorFlow, Hadoop), and any specialized libraries (NumPy, Pandas). 

You should also weave these skills into your job descriptions to show how you’ve applied them in real-world scenarios. This dual approach ensures recruiters can quickly see your expertise while understanding how you’ve used your skills.

What should be the resume headline for a data scientist?

Your resume headline is a snapshot of who you are as a data scientist. It should be concise yet descriptive, giving a clear picture of your expertise. For example, “Data Scientist Specializing in Machine Learning and Predictive Analytics” quickly tells the recruiter what you bring to the table. 

If you’re a student or new to the field, you might go with something like “Aspiring Data Scientist with Foundations in Python and Data Analysis.” The goal is to grab attention and make them want to read more.

What’s the difference between a data analyst and a data scientist? 

Both data analysts and data scientists work with data, but their roles differ in scope and complexity. A data analyst focuses on interpreting existing data to provide actionable insights. 

On the other hand, a data scientist builds models and often works with data to predict future trends or solve complex problems. They require strong programming skills and a deep understanding of machine learning. While data analysts describe the present, data scientists predict the future.