entry level data scientist resume reddit

15 Entry Level Data Scientist Resume Examples for 2024

Landing your first job as an entry-level data scientist means presenting a resume that shows your skills clearly. This article guides you through crafting a resume that hiring managers will notice. Find examples and tips to highlight your data analysis, programming, and statistical skills. Learn how to organize your qualifications, projects, and education to start your data science career.

• 01 Nov 2024 - 1 new resume template (Data Science Engineer - Manufacturing Sector) added
• 01 Oct 2024 - 1 new section (Showcase leadership experience) added
• 27 Aug 2024 - 1 new resume template (Junior Data Scientist - Aerospace Industry) added

  Next update scheduled for 13 Nov 2024

Here's what we see in standout entry-level data scientist resumes.

Show Impact With Numbers : The best resumes show clear impact with accuracy improvements , time savings , cost reductions , and efficiency gains . These metrics help you show the value you can bring to the role.

Match Skills To The Job Description : Include skills you have that match the job description. Highlight tools or techniques such as Python , R , SQL , Machine Learning , and Data Visualization .

Current Industry Trends : Stay updated with trends such as automated data cleaning . Show that you're ready to adapt and apply these in your role.

use this maybe to have a resume upload button widget 1: yellow Here's a short quick tip / warning for people to include. If your symptoms get worse or do not improve after 1 day, go to a lower altitude if you can. Try to go around 300 to 1,000 metres lower.

widget 2: red / but not serious Here's a short quick tip / warning for people to include. If your symptoms get worse or do not improve after 1 day, go to a lower altitude if you can. Try to go around 300 to 1,000 metres lower.

helpful blue / but not serious Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include.

Entry Level Data Scientist Resume Sample

Find out how good your resume is.

ummm here it is

Get feedback on your resume

Want to know if your resume stands out for entry-level data scientist roles? Our AI-powered tool simulates how hiring managers evaluate resumes. It checks for key skills, readability, and industry-specific criteria that recruiters look for when screening candidates.

Upload your resume now for a free, unbiased assessment. You'll get a score and clear feedback on how to improve your chances of landing interviews in the data science field.

Education placement on resume

As an incoming data scientist, it's crucial to leverage your educational experience. Place your educational background at the beginning of your resume. This showcases your strong theoretical knowledge base, particularly important for entry-level positions where you may lack extensive professional experience.

Include relevant coursework that might differentiate you, such as advanced statistics, machine learning, or data visualization. Also, highlight any thesis or significant projects related to data science. This will provide a practical context for your theoretical knowledge.

Displaying quantitative skills

An entry-level data scientist role requires a strong mathematical and statistical foundation. Ensure to include quantitative accomplishments in your resume. Did you manage to improve a process or identify a key insight during a school project? Quantify these achievements. It shows you can not only handle data, but also draw valuable conclusions.

Data scientists often require proficiency with specific tools such as Python, R, and SQL, or data visualization platforms like PowerBI or Tableau. Showcase your familiarity or proficiency with these tools prominently to attract the attention of hiring managers.

Data Science Associate Resume Sample

Keeping your resume concise.

For entry-level data scientist positions, it's most effective to keep your resume to one page. It presents a clear and focused snapshot of your abilities, indicating your aptitude for the meticulous, precision driven role of a data scientist. It also shows respect for hiring managers’ time, who often sift through dozens, if not hundreds, of resumes.

If you find your resume stretching beyond one page, review your content ruthlessly. Prioritize recent and relevant educational and work experience, and cut older or less relevant information. It's about showcasing quality, not quantity, of your experience.

Entry Level Data Scientist with Data Visualization Specialization Resume Sample

Showcasing problem-solving skills.

Problem-solving is at the heart of being a data scientist. Illustrate your knack for problem-solving by sharing instances where you've identified complex problems, dissected them, and formulated effective solutions. Even scenarios from academic projects or hackathons can illuminate your abilities.

Additionally, communication is key in data science, as findings often need to be translated to non-technical teams. Highlight any experience or activities where you had to present complicated information in a clear, understandable way. This shows you not only understand data, but can also help others understand it as well.

Marketing Associate to Entry Level Data Scientist Resume Sample

Beat the resume screeners.

When you apply for an entry level data scientist role, your resume may first be read by a computer program known as an Applicant Tracking System (ATS). It's important to format your resume in a way that this system can read it well. This means avoiding images or graphics that contain important text and using standard section headings like 'Work Experience' and 'Education'.

Here are some key things to keep in mind to help your resume get past the ATS:

• Use relevant keywords from the job description. For example, include skills like 'data mining' or 'machine learning' if they match your abilities.
• Make sure your layout is simple. Use clear headings and bullet points to list your skills and experiences.

These steps will help ensure that your resume is ATS-friendly and that it will be seen by a hiring manager for the entry level data scientist positions you are applying for.

Senior Data Analyst Resume Sample

Shape your resume for the job.

You want to show you fit the data scientist role you're after. Think about what skills and experiences are most important for someone who works with data. Match your resume to these needs. This tells me, as a hiring manager, that you understand the job and are ready to step in.

• Put your most relevant data projects first. Use terms like data analysis , machine learning , and data visualization .
• Show how you solve problems. Mention a time you used statistical methods or predictive models to make decisions.
• If your past work was not in data science, show matching skills. For example, if you used Excel for data tracking, that’s useful.

Junior Data Scientist - HealthTech Industry Resume Sample

Avoid data overload.

When applying for entry level data scientist roles, you might feel the need to include every project you have worked on, but this can lead to too much information. Focus on the most relevant projects. Show how these projects gave you skills that will help you in this job. For example, if you developed a model to predict customer behavior, mention it and explain how it polished your analytical abilities.

Be careful with technical terms and acronyms. Not everyone reading your resume will understand complex data science language. Instead, use simple words to describe your skills and experiences. For instance, say 'wrote code to collect data' instead of using a programming term that some might not know. Remember that clear communication is as important as your technical skills.

Machine Learning Engineer - FinTech Industry Resume Sample

Use strong action verbs.

As a guide for your entry level data scientist resume, you should choose verbs that show your skills in analyzing and handling data. Using the right verbs can help you make a strong impression. Remember, each word on your resume counts, so pick verbs that are clear and direct.

Here is a list of action verbs you can include on your resume to describe your abilities and contributions:

• To display your analytical skills, use analyzed , calculated , evaluated , investigated , modeled .
• Showcase your technical expertise with programmed , computed , visualized , extracted , implemented .
• Highlight your problem-solving abilities by using solved , optimized , rectified , reconciled , debugged .
• To demonstrate collaborative work, include verbs like collaborated , contributed , coordinated , liaised , partnered .
• When discussing projects or research, verbs such as designed , developed , formulated , tested , assessed are effective.

Want inspiration for other action verbs you can use? Check out synonyms to commonly used action verbs like Using , Make , Updated , Worked on , Supervised .

Data Insights Analyst - Retail Industry Resume Sample

Showcase achievements, not tasks.

When you are crafting your resume as an entry level data scientist, remember that it’s more compelling to emphasize your achievements rather than listing your daily responsibilities. Make sure you convey the value you have added in your experiences. This tells employers what you might bring to their team.

Consider the following shifts from responsibilities to accomplishments:

• Instead of 'Wrote code for data analysis', you could say 'Developed and optimized data analysis code, reducing data processing time by 20%'.
• Rather than 'Assisted in data collection', express this as 'Collaborated in a data collection initiative that increased the dataset accuracy by 15%, leading to more reliable insights'.

These changes help you stand out by providing measurable results that you have achieved. Numbers and outcomes talk louder than tasks, giving a clearer picture of your capability.

Quantitative Analyst - Energy Sector Resume Sample

Essential technical skills list.

If you're stepping into the data science field, your resume needs to show your technical prowess. Focus on the skills that will make you stand out as an entry-level data scientist. Here's a list of skills you might include, depending on the job you want:

• Python or R for data analysis and modeling
• SQL for database management
• Machine Learning techniques
• Data Visualization tools like Tableau or PowerBI
• Big Data platforms such as Hadoop or Spark
• Statistical analysis abilities
• Data wrangling skills with Pandas or NumPy
• Git for version control

You don't need to list every skill, just those that match the data science role you're aiming for. For example, if the job focuses on data visualization, emphasize your experience with Tableau or PowerBI . Place these skills in a dedicated section on your resume to help it pass Applicant Tracking Systems (ATS), which employers use to filter candidates.

Remember, show how you've used these skills in real projects or during your education. This helps employers see your practical experience, which is valuable even at the entry level.

Data Science Consultant - Healthcare Sector Resume Sample

Quantify your impact with numbers.

When you apply for a data science role, you must show the value you can bring. Use numbers to make your impact clear. Numbers help hiring managers see your potential quickly and easily.

Think about your past work or projects. Look for ways you have used data to create value. Here are some ideas:

• How you improved a process - maybe you made a model that increased efficiency by 20% .
• If you worked on a project, did it help make decisions faster? Maybe you cut down the time needed to analyze data by 30% .

Remember, even if you're not sure about exact numbers, you can estimate. Think about how your work changed things. Did it make a system run faster or help save money? For instance, if you created a predictive model, estimate how much it could increase accuracy or reduce errors. Consider metrics like:

• Accuracy increase by 15%
• Error reduction by 25%
• Cost savings of $10,000
• Time savings of 5 hours per week
• Boost in data processing speed by 40%
• Reduction in customer support issues by 50%
• Upgraded 3 major data systems
• Conducted over 100 hours of data analysis leading to actionable insights

Data Science Manager - E-commerce Sector Resume Sample

Showcase leadership experience.

When you're applying for a data scientist role at the entry level, it's important to show any leadership skills or career growth. This can make your resume stand out. Here's how you can do it:

• Include any projects where you led a team, even if it was for a class or a club. For example, 'Led a team of four in a data analysis project for a marketing class, resulting in a 15% increase in campaign effectiveness.'
• Point out any promotion you received, even in part-time or unrelated jobs. For example, 'Promoted from sales associate to team leader within one year due to strong performance and leadership skills.'

Think about times when you had more responsibility than before. This could be in school, work, or volunteer activities. It shows your ability to grow and take on challenges. For instance:

• 'Tasked with coordinating data collection efforts in a group volunteer project, improving data accuracy by 20%.'
• 'Selected as a peer mentor in university statistics courses, guiding new students through complex data concepts.'

Junior Data Scientist - Aerospace Industry Resume Sample

Show leadership and growth.

When you're applying for data science roles, it's helpful to show that you can lead and grow in a work setting. You might think you don't have this experience, but there are many ways to demonstrate it.

Think about times when you had more responsibility than before. This could be a project where you were the lead, or when you taught other people how to do something. Here are some ideas:

• Include any projects where you took the lead on data analysis or presented findings to a team.
• Mention if you trained others on data tools like Python or R.

Also, remember to list any promotions or titles that show growth. Even small steps up are worth mentioning. For example:

• From data intern to assistant data analyst
• Lead on a project after just a few months on the job

Data Science Engineer - Manufacturing Sector Resume Sample

Targeting small companies.

When applying for positions at small companies or startups, such as DataRobot or H2O.ai, highlight your versatility and ability to learn quickly. These companies value candidates who can wear multiple hats.

Include phrases like 'Comfortable working in fast-paced environments' and 'Experience with diverse data projects.' Mention any experience with startup culture or small team collaborations.

•   Research Scientist Resume Examples
•   Senior Data Scientist Resume Examples
•   Environmental Scientist Resume Examples
•   NLP Data Scientist Resume Examples
•   Data Scientist Resume Examples
•   Healthcare Recruiter Resume Examples
•   Illustrator Resume Examples
•   Entry-Level Cybersecurity Resume Examples

Quick links

Entry level data scientist, resume sample #1, resume sample #2, resume sample #3, data science associate, resume sample #4, entry level data scientist with data visualization specialization, resume sample #5, marketing associate to entry level data scientist, resume sample #6, senior data analyst, resume sample #7, junior data scientist - healthtech industry, resume sample #8, machine learning engineer - fintech industry, resume sample #9, data insights analyst - retail industry, resume sample #10, quantitative analyst - energy sector, resume sample #11, data science consultant - healthcare sector, resume sample #12, data science manager - e-commerce sector, resume sample #13, junior data scientist - aerospace industry, resume sample #14, data science engineer - manufacturing sector, resume sample #15, additional resources, questions get in touch.

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.

Similar Posts

Is Chemistry A Life Science?

Whether chemistry is considered a life science or a physical science is a topic of debate among scientists. At its core, chemistry is the study of matter and its transformations. However, the applications of chemistry have implications across many scientific fields, including the life sciences. If you’re short on time, here’s the quick answer: Chemistry…

Pure Science Vs Applied Science: A Comprehensive Comparison

Science plays a vital role in society, but not all science is created equal. Understanding the difference between pure and applied science is key. In short, pure science seeks knowledge for its own sake, while applied science uses knowledge to solve real-world problems. But there’s more nuance than that simple definition. In this comprehensive guide,…

Is Exercise Science A Stem Major?

Exercise science is a popular field of study for students interested in health, fitness, and the human body. But with its blend of science and practical coursework, there is often debate around whether exercise science qualifies as a STEM major. If you’re short on time, here’s a quick answer: Exercise science is generally considered a…

Is Physics Math Or Science? Examining The Relationship

Physics relies heavily on mathematical concepts and tools, leading many to wonder – is physics more of a math discipline or a natural science? This article will examine the intrinsic links between math and physics and discuss how physics straddles the line between mathematical theory and experimental science. If you’re short on time, here’s a…

Bachelor Of Technology Vs Bachelor Of Science: How To Choose

When exploring undergraduate programs in technical fields like engineering, computer science or agriculture, you’ll often have to decide between pursuing a Bachelor of Technology (BTech) or a Bachelor of Science (BSc). Both degrees cover scientific and mathematical concepts but have some key differences in focus and curriculum. If you’re short on time, here’s a quick…

Should I Study Computer Science? Analyzing The Pros And Cons

With career opportunities in high-paying fields like software engineering and data science, it’s understandable why many students consider studying computer science. But is it ultimately the right choice? This field offers immense potential but also has downsides to weigh. In this in-depth guide, we’ll assess the key benefits and drawbacks of studying computer science to…

Build my resume

• Build a better resume in minutes
• Resume examples
• 2,000+ examples that work in 2024
• Resume templates
• Free templates for all levels
• Cover letters
• Cover letter generator
• It's like magic, we promise
• Cover letter examples
• Free downloads in Word & Docs

5 Entry-Level Data Analyst Resume Examples for 2024

• Entry-Level Data Analyst

Best for candidates with 3+ years of experience

With your job experience and a stunning resume layout, recruiters will be ready to give your application the official stamp of approval.

Resume Builder

Like this template? Customize this resume and make it your own with the help of our Al-powered suggestions, accent colors, and modern fonts.

Standout Entry-Level Data Analyst Resume

Modern entry-level data analyst resume, elegant entry-level data analyst resume, professional entry-level data analyst resume.

• Skills & Work Experience

You’re looking to break into data analysis and yet, most jobs you see require some analysis experience. What came first, the work experience or the job requiring work experience?

No worries though, there are other ways to showcase that you’d be an excellent entry-level data analyst hire for any company smart enough to hire you.

The five entry-level data analyst resume examples below have worked to help fellow analysts break into the world of data in 2024, and these resume tips are a great place for you to get started on your job search.

Entry-Level Data Analyst Resume

or download as PDF

Why this resume works

• Relevant courses like Linear Algebra and Statistics are a welcome addition, but what’s really going to spice things up is an internship experience. If you have any, highlight how you’ve used mathematical knowledge and programming skills to clean data and indirectly improve metrics like ROI.

Related resume examples

• Senior data analyst
• Data analytics manager
• Revenue reporting data analyst
• SQL data analyst
• Data analyst

What Matters Most: Your Skills & Work Experience

When you’re looking for your first role in data analysis, any experience counts. Whether it’s an internship or a solo project, put it on your resume! Focus on what you did and the tools you used to do it.

Since you don’t yet have much real-world data analysis experience, your skills section will play a big role in determining whether you get an interview. Recruiters want to see your technical skills here. Vague words like “teamwork” don’t tell much.

9 popular entry-level data analyst skills

• Excel/ Google Sheets
• SQL (any flavor)
• Linear Regression
• Experimentation
• A/B Testing
• Data Cleanup
• Data Visualization

When it comes to your technical skills, if you can answer this question, you should include it on your entry-level data analyst resume: Would I be comfortable being asked interview questions about that tool/ topic?

Sample entry-level data analyst work experience bullet points

Now I know what you’re thinking: “I’m looking to break into data, I don’t have much relevant experience.” Yes, you do!

How did you learn the analysis skills you have? What projects did you work on? Talk about those!

When talking about experience (through internships) or your projects, you want to convince a recruiter your analysis can have impact. So, your bullet points should similarly focus on impact.

We’re math people, so here’s the formula:

[ action you took ] + [ context/skills you used ] + [ outcome of action ]

Here’s a few examples for inspiration:

• [ Built a random forest model ] in [ scikit-learn ] that combined disparate sources into one projection that [ outperformed the mean absolute error of the next best projection by 14% ]
• [ Built data visualizations ] using [ SQL and Tableau ] for business KPIs that [ reduced manual reporting work by 10 hours weekly ]
• [ Identified strategic marketing opportunity ] for client [ through detailed analysis with intern team ], making recommendations that [ saved client over $10K in yearly campaign costs ]
• [ Received, cleaned, and prepped data from client ] using [ Python, SQL, and Excel ] to help data scientists build marketing mix models that [ resulted in a lift in ROI of 8 basis points ]

If you’re in a time-crunch or are just looking for a quick reference, this section is for you! We’ve helped thousands of entry-level data analysts land jobs and here are the most common questions and quick-wins to help you.

Top 5 Tips for Your Entry-Level Data Analyst Resume

• That teen summer job you spent serving ice cream? By our estimation, you demonstrated reliability, organization, and collaboration. The point is, when you’re looking for an entry-level role, recruiters don’t expect years of relevant experience.
• Since most applicants for entry-level data analyst roles don’t have much experience, a great way to differentiate yourself is to customize your resume and use a cover letter maker for each company you apply to. More work, yes, but a higher success rate.
• A career summary is just that, a summary. As an entry-level data analyst you likely don’t have an extensive career to summarize. Instead, you may want to add a career objective detailing what you’re looking for in a data analyst role and what you’ll contribute.
• Employers need some evidence that you can go from idea to analysis in a project. This is the value of your projects section as an entry-level data analyst. These can be things you’ve done for class or personally, but you should have something relevant to data!
• When it comes to your technical skills, it’s much better to demonstrate a strong command of a few skills than have a laundry list of skills on your resume. Nothing is a bigger red flag to recruiters then when they see five-plus programming languages for entry-level candidates.

Frequently Asked Questions

• Ideally, you can make your entry-level data resume take up one nice, full page. But, it’s better to have a shorter resume than to add a lot of filler content. Remember, recruiters know you’re applying to an entry-level role, they don’t expect an extensive working background. The goal of your resume is to convince them you can do the job, not that you necessarily have done the job.
• Projects, projects, projects. As an entry-level data analyst projects are the best (and sometimes only) way to demonstrate your competency. These projects can be anything. Just ask a question, think of data you can use to answer that question, and do a short write-up of your analysis (any public forum you can link to for this is good).
• A bit of a leading question, I admit. But yes, of course, include any relevant courses you took in school. For our co-founder, including his game theory class led to some interesting conversations in interviews. Similarly, while they’re not as important later in your career, certifications can demonstrate you have the capacity and willingness to learn data analysis.