A data analyst resume in India is one page with four things on it: a two-line summary, a skills block that names the tools exactly as job ads name them, two or three projects written as outcomes rather than activities, and your education. If you have no work experience, projects sit where experience would go and the document still works. Everything else is optional.
This page is the document itself. What to learn is covered elsewhere on this blog, and how to build the projects that fill the middle of the page is covered in our guide to the data analyst portfolio. Here we deal with the page a recruiter opens.
What the page has to survive
Two readers, in order. The first is usually software. Most large Indian employers and every big job board run applications through an applicant tracking system that matches your document against the words in the posting, which is why the tools have to appear in plain text and in the posting’s own vocabulary. Write Power BI, not “PowerBi”. Write SQL, not “databases”.
The second reader is a human going through a stack quickly, looking for a small number of things: which tools you have actually used, whether the projects on the page are yours, and whether the basics on the page are real. Everything you put on the page competes for that attention. A photograph, a skills bar chart showing SQL at 80 percent, a hobbies line and a declaration sentence all take room a project line would use better.
One page if you have under five years of experience. Save it as a PDF and name the file with your own name rather than “resume final v3”.
Section by section
The summary
Two sentences at the top, no heading needed beyond your name. Say what you are, the tools you work in, and the kind of problem you want to be handed. Skip the adjectives entirely.
A fresher’s summary is honest about where the evidence comes from: “Commerce graduate who moved into analytics, working in SQL, Excel and Power BI. Three completed projects on public Indian datasets, including a pricing analysis of 50,000 Bengaluru restaurant listings.” That says more than “detail-oriented data enthusiast passionate about turning data into insights”, which says nothing and appears on thousands of other documents.
The skills block
This is the section the automated screen reads hardest. Group it so a human can read it too, and do not list anything you would not want to be tested on live.
| Group | What to list | What to leave out |
|---|---|---|
| Query | SQL, and the database you used it on, such as MySQL or PostgreSQL | “Databases” as a word on its own |
| Spreadsheets | Excel, with pivot tables, lookups and Power Query if you use them | “MS Office” |
| Visualisation | Power BI or Tableau, whichever you actually built in | Both, if you have only opened one |
| Programming | Python, with pandas and one plotting library | A language you studied one semester of |
| Statistics | The methods you can explain, such as hypothesis testing or regression | “Machine learning” on a fresher analyst resume |
The last row is where freshers lose credibility fastest. A first analyst job in India is hired on querying, cleaning and explaining, so a resume that leads with deep learning reads as someone applying for a different job. Our breakdown of the skills a data analyst actually needs is what Indian job ads screen against.
Projects, with numbers
The heart of the document for anyone under two years in. Two projects done well, three at most. Each one gets a title, one line of context, and two or three bullets that end in a result.
The rewrite that matters is from activity to outcome. “Performed exploratory data analysis on restaurant data using Python” is an activity. “Found that three Bengaluru localities delivered the highest rating per rupee, from 50,000 listings after cleaning a cost column that arrived as text” is a result, and it survives a follow-up question because there is something in it to ask about.
Numbers make a bullet checkable, so put them in wherever they are true: rows of data, how many hours a manual process took before you rebuilt it, the size of the gap you found. If a project has no number in it anywhere, that usually means it did not reach a conclusion.
Education
One line for the degree, one for the institution, one for the year. Put it below the projects if you graduated more than a year ago, and above them only if you are still studying or the degree is genuinely the strongest thing on the page. Indian applicant tracking systems frequently filter on graduation status, so the line has to be there and has to be unambiguous, but it rarely earns you the interview by itself.
Certificates go here as a single line, not as their own section with logos. Name the certificate and the year. Our read on which data analytics certifications are worth it in India covers what they do and do not buy you.
Work experience, if you have any
Non-data work still belongs on the page, written for what it proves. A support executive who built the team’s weekly report in Excel has been doing analyst work without the title, and that bullet is worth more than the job title above it. Lead with what you produced and who used it.
If you are serving a notice period, put the length on the page. Indian recruiters ask in the first call anyway, and a resume that answers it moves faster.
The fresher template, to copy
Every line below is one line in the document. Replace the capitals with your own words and delete anything you cannot fill honestly.
- YOUR NAME, in the largest type on the page
- CITY, PHONE NUMBER, EMAIL, LINKEDIN URL, GITHUB OR PORTFOLIO URL, all on one line under the name
- SUMMARY
- Sentence one: what you are, what you studied, the tools you work in.
- Sentence two: what you have built, with one specific detail from it.
- SKILLS
- SQL: WHAT YOU CAN DO, for example joins, window functions, query tuning, on MYSQL OR POSTGRESQL
- Excel: pivot tables, lookups, WHATEVER ELSE YOU USE
- Power BI or Tableau: WHAT YOU HAVE BUILT IN IT
- Python: pandas, WHICHEVER LIBRARIES YOU ACTUALLY USE
- Statistics: THE METHODS YOU CAN EXPLAIN UNDER QUESTIONING
- PROJECTS
- PROJECT ONE TITLE, with the tools in brackets
- One line saying what question the project answered and what data it used, with the source named.
- Result bullet: what you found, with a number in it.
- Method bullet: the hardest thing you had to fix in the data, in one line.
- Link: the repository or the published dashboard.
- PROJECT TWO TITLE, with the tools in brackets
- One line of context, one result bullet, one method bullet, one link.
- EXPERIENCE OR INTERNSHIPS
- ROLE, ORGANISATION, MONTH YEAR to MONTH YEAR
- One bullet on what you produced, one on who used it.
- EDUCATION
- DEGREE, INSTITUTION, YEAR OF GRADUATION
- CERTIFICATIONS
- CERTIFICATE NAME, ISSUING BODY, YEAR
Copy those lines into a blank document in the order given and keep the whole thing in a single column. That is the entire format. Single-column layouts are also what the automated screens read most reliably, so a template with sidebars, boxes and two columns of text is working against you before a human sees it.
Three sample resumes
These are written out in full so you can see the difference between them. The tools are similar in all three. What changes is what the middle of the page is made of.
Sample one: a fresher with no work experience
Riya Menon. Bengaluru. Phone and email on one line, followed by her LinkedIn and GitHub links.
Summary. B.Com graduate, 2026, working in SQL, Excel and Power BI. Three completed projects on public Indian datasets, including a pricing study of 50,000 Bengaluru restaurant listings and an air quality comparison across two cities.
Skills. SQL: joins, aggregation, window functions, on MySQL. Excel: pivot tables, lookups, Power Query. Power BI: published dashboards with slicers and calculated measures. Python: pandas, Matplotlib. Statistics: descriptive statistics, hypothesis testing.
Projects. Restaurant pricing in Bengaluru, using Python and Power BI. Asked which localities deliver the best rating for money, using a public listings dataset of about 50,000 restaurants. Found three localities where mid-price restaurants rate consistently above the city average, after cleaning a cost field that arrived as text with commas. Dashboard published and linked.
Air quality across Delhi and Bengaluru, using Python. Compared a year of hourly pollution readings from one monitoring station in each city. Built an hour-of-day profile showing the two cities peak at different times, and excluded four weeks of missing readings rather than interpolating them, with the reason documented.
Education. B.Com, a Bengaluru university, 2026.
Certifications. One named certificate, with the issuing body and year.
Sample two: a career switcher
Arjun Rao. Pune. Contact line, LinkedIn, GitHub.
Summary. Customer support team lead with four years in a logistics firm, moving into analytics. Built and owned the team’s weekly performance reporting in Excel and SQL before retraining in Power BI and Python. Available on a 60-day notice period.
Skills. SQL: joins, subqueries, window functions. Excel: pivot tables, Power Query, VBA for recurring reports. Power BI: two published dashboards. Python: pandas for cleaning and analysis.
Experience. Team Lead, Customer Support, a Pune logistics company, 2022 to 2026. Rebuilt the weekly service report in SQL and Power BI, cutting it from roughly four hours of manual work to under twenty minutes, and it is still the version the operations head reviews. Investigated a recurring delivery-delay complaint and traced it to two pin codes with a single vendor, which changed how those routes were allocated.
Projects. Mandi price spread, using SQL. Tracked the daily gap between the highest and lowest reported price for one commodity across ten markets for a season, and found the spread widens at harvest. Repository linked.
Education. B.A. Economics, 2021.
The switcher’s advantage is on the page and most switchers hide it. Four years of doing reporting inside a non-analyst job is experience, and writing it as an analyst bullet is the whole trick. Our guide on moving into data analytics from a non-technical degree goes through the rest of that move.
Sample three: two years into the job
Neha Sharma. Gurugram. Contact line, LinkedIn.
Summary. Data analyst with two years in a financial services team, owning credit portfolio reporting. Works in SQL and Power BI daily, with Python for anything the warehouse cannot do.
Skills. SQL: window functions, query tuning, data modelling on a warehouse of about 40 tables. Power BI: row-level security, calculated measures, refresh scheduling. Python: pandas, scikit-learn for basic regression. Excel: advanced.
Experience. Data Analyst, a financial services firm, 2024 to now. Owns the monthly credit portfolio pack that the risk committee reviews. Rebuilt three overlapping dashboards into one after finding two of them used different definitions of the same metric, which is the reason the numbers disagreed for a year. Reduced the refresh time on the main report from forty minutes to under five by rewriting the underlying queries. Works with the collections team directly to translate their questions into something the data can answer.
Education. B.Tech, 2023. One certificate line.
At two years the projects section usually disappears, because the job is the evidence. Notice what replaced it: ownership of something named, a problem nobody had asked her to fix, and a line about talking to a business team.
Mistakes that cost interviews
Listing a tool you cannot be tested on. The SQL round is where most Indian analyst candidates are eliminated, and putting window functions on the page and then freezing on one is worse than not listing it. Our set of data analyst interview questions is a fair test of what you can genuinely claim.
Describing the method instead of the finding. Half the resumes in any pile say “performed data cleaning and visualisation”. None of them say what came out of it.
Sending one document to every posting. You do not need a rewrite each time. You do need the skills line to carry the words that particular ad uses, because those are the words being matched.
A project nobody can open. If the repository is private or the dashboard link is dead, the project is not on the page as far as the reviewer is concerned.
Padding to two pages. A fresher with a second page is repeating themselves, and it reads that way.
Frequently asked questions
What should a data analyst resume look like for a fresher?
One page, single column, in this order: name and contact line, a two-sentence summary, a skills block naming SQL, Excel, a BI tool and Python, then two or three projects written as outcomes with links, then education and certificates. Projects occupy the space that work experience would take, and they are what the interview will be built around. Save it as a PDF named after yourself.
How do I write a data analyst resume with no experience?
Treat finished projects as the experience section. Each one needs a question, a named public dataset, what you found with a number in it, and a working link to the repository or dashboard. Any non-data job you have held still belongs on the page if you can write a bullet about something you produced that other people used, which is true of far more jobs than people assume.
How many projects should be on a data analyst resume?
Two or three, and no more. Reviewers stop reading after the first two, so a fourth project takes space from the ones being read. Pick projects that use different skills, so one leans on SQL, one on cleaning messy data, and one ends in a dashboard, and make sure every link on the page opens.
Should I put my photo, age and marital status on an Indian data analyst resume?
No. None of the three helps an analyst application and all of them take room that a project line would use better. The same goes for the declaration sentence and the signature at the bottom, which are conventions from a different kind of document. Keep your notice period on the page if you are serving one, because recruiters ask that in the first call.
Does a certification help a data analyst resume?
It helps you get past the first screen and does very little after that. A certificate tells a reviewer you completed a course, and a project tells them what you can do, which is why the projects section is longer than the certificates line on every sample above. Put the certificate on one line with its issuing body and year, then spend your effort on the work.
What should a data analyst put in the resume summary?
Two sentences: what you are and what you work in, then one specific thing you have built, with a detail in it. The specific detail is doing all the work, because it is the only part a reviewer cannot skim past. Avoid the adjectives that appear on every other document, and never open with the phrase “passionate about data”.
Build projects worth putting on the page
The resume is downstream of the work. If the middle of your page is thin, it is because there is nothing finished behind it, and no amount of formatting fixes that.
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