Entry-level data analyst resume example

By the Rezumed team · Updated October 31, 2026

An entry-level data analyst resume should show that you can take a messy question, find the data, and turn it into a clear answer someone can use. Every bullet should hint at all three: the question, what you did with the data, and what you found.

What hiring teams look for

For job facts we used the Bureau of Labor Statistics profile for data scientists, the closest occupation in its handbook. It says they "use analytical tools and techniques to extract meaningful insights from data". Their listed duties include collecting, categorizing and analyzing data, presenting findings with data visualization software, and making recommendations to stakeholders. The typical entry-level education is a bachelor's degree.

For an entry-level analyst, that becomes a simple test: can you pull data, clean it, and explain it to someone who isn't technical? BLS lists conveying results to "technical and nontechnical audiences" as a key skill.

Sample resume

How to write each section

Projects: question, data, finding

A strong analysis bullet reads like a tiny case study. Start with the verb, name the data and the tool, and end with what you found or who used it. If a class project used a public dataset, say so. It tells the reader the work is real and checkable.

Experience: name who used your work

A summary that a manager actually read is worth more than a big query count. Name the audience: "the lending team", "a faculty paper", "the class". It shows the communication half of the job.

The numbers in these bullets are things a student would know: the size of the dataset, how many stations, how many responses. Don't estimate business impact you never measured.

Education

Statistics, econometrics, database and visualization courses are the ones to list. A minor in statistics or computer science is worth naming on the degree line.

Skills to list

Hiring teams and their software search for exact tool names. If the posting says "Power BI" and you have used Power BI, write Power BI. If you have only used Tableau, list Tableau and don't add Power BI to match. The University of Maryland career center also suggests mirroring the job description's keywords.

Tools

• SQL, with the database you used (PostgreSQL, MySQL, SQL Server)
• Excel: pivot tables, lookups, charts
• Python (pandas, matplotlib) or R
• A BI tool: Tableau, Power BI or Looker
• Google Sheets, if the job uses it

Abilities

• Cleaning and joining messy data
• Descriptive statistics and regression
• Writing a short summary of findings
• Presenting results to people who don't work with data

Common mistakes

• Tools with no analysis. A skills line full of tools, with bullets that only repeat them, says you installed software. Show one question you answered with each important tool.
• Percentages with no base. "Improved efficiency by 30%" raises the question: of what, measured how? If you didn't measure it, describe the change in words.
• Overselling machine learning. One tutorial isn't machine learning experience. List a model only if you built and evaluated it yourself and can explain it.
• Dashboards with no audience. Say who looked at it and what they used it for.

FAQ

Do I need Python, or are SQL and Excel enough?

It depends on the posting, so read it first. List what it asks for that you have really used, and show at least one project for each language it names.

Should I link a portfolio?

Yes, if it has one or two finished projects with a short write-up: the question, the data, the method and the finding. A folder of notebooks with no explanation helps much less.

Is an economics or business degree OK for analyst jobs?

BLS says data scientists need extensive study in math and statistics, with computer science courses also important. If your degree is in another field, list the statistics and data courses you took and lead with your projects.

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Sources

• U.S. Bureau of Labor Statistics, Occupational Outlook Handbook: Data Scientists
• University of Maryland Career Center: Resume how-to guide