ATS RESUME CHECKER

ATS Resume Checker for Entry-Level Data Analyst

Entry-level data analyst postings usually screen for a core toolset (SQL, Excel, a BI tool) and for evidence that you turned data into a decision. This checker scores your resume against that profile and lists the keywords it could not find. It runs in your browser; your file is never uploaded.

Target job title Your resume fileUpload a resumeDrop a PDF, DOCX or TXT file here, or click to browse (max 10 MB)The file is read in your browser and is never uploaded to our servers. Resume text (or paste it here) Job description (optional, recommended)

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Your score and detailed report will appear here.

What this checker looks for in a Entry-Level Data Analyst resume

With no job description pasted, the checker compares your resume against the keyword profile below. Paste a real posting into the job description box for a tighter, posting-specific match. Keywords are matched as whole words or phrases, so use the wording employers use, and only list skills you can discuss in an interview.

Core keywords (expected in most postings for this role)sqlexceldata analysisdata visualizationdashboardreportpythonstatistics

Strong differentiatorstableaupower bidata cleaningpivot tablea/b testingstakeholderkpietl

Nice to have, depending on the employerrlookergoogle analyticsbigquerygitvlookup

The core and differentiator lists are our editorial summary of commonly listed skills for this role, not scraped data. Employers differ; the posting you apply to always wins.

How the score reads

The checker scores experience, keywords, impact, education, structure and format together. On its own scale, 85 or higher is labeled Excellent, 70 to 84 Good, 50 to 69 Fair and below 50 Needs work. This is our own rubric, not an industry standard, and real applicant tracking systems differ. See how the score is calculated.

Stronger bullets for this role

Start with an action verb such as Analyzed, Built, Automated, Queried, Visualized, Reduced, then add the tool, the scale and the result. Examples below are illustrations of the pattern; replace the numbers with your own true figures.

Weak: Responsible for making weekly reports for the sales team.

Stronger (example): Built a weekly SQL and Excel report for a 12-person sales team, replacing a manual process that took about 3 hours.

Weak: Worked with data and made charts.

Stronger (example): Cleaned a 40,000-row survey dataset in Python and built a Tableau dashboard that showed the three drivers of churn.

Weak: Helped with analysis for projects.

Stronger (example): Analyzed order data for a capstone project and recommended a change that cut the modeled delivery delay by 15%.

Format advice for Entry-Level Data Analyst applications

List tools in a Skills section using the exact names postings use (SQL, Excel, Tableau, Power BI, Python).
Put one or two projects under Experience or Projects if you lack a job title; give each a dataset size, tool and outcome.
Link a portfolio or GitHub only if it holds finished, readable work.
Keep it to one page; recruiters scan for tools and numbers first.

Common reasons these resumes score low

Tools named only in a skills list, never shown in a bullet.
No numbers: dataset size, time saved, accuracy or revenue affected.
Course names instead of outcomes ("Completed data analytics course").
Charts or skill bars made as images that the parser cannot read.

Questions about this role

Do I need Python for an entry-level data analyst job?

Many postings list SQL and Excel as essential and Python or R as a plus. Check the postings you are targeting and mirror what they ask for, but only claim tools you can use.

How do I show data skills without a job?

Use a project: state the question, the data source and size, the tool, and the result. The checker counts quantified bullets whether they come from a job or a project.

Related guides

Educational estimate, not a guarantee of interviews or hiring outcomes. Examples are illustrative.