Data Analyst Projects for a Portfolio: 8 Ideas With Data

Last updated: 21 September 2026 · By the Asuraa Team

What makes good data analyst projects?

Good data analyst projects start from a real question, use a real dataset, and end with a conclusion someone could act on. Coursera's guide to data analytics projects, updated 1 November 2025, says an end-to-end project combines collecting data, cleaning it, analysing it and interpreting the result, which shows employers how the skills fit together.

For a fresher, two or three finished projects are a sensible target, which is our editorial advice. Each one should show a different step of the job, so a recruiter sees range and not repetition.

What are eight data analyst project ideas?

The table lists eight ideas and the data source for each. The ideas are ours, and the data sources are pages we opened or examples that Coursera's guide names.

If you want your first three, pick 1 or 3 for cleaning, 4 for SQL, and 5 for visualisation. Ideas 2 and 6 suit an exploratory write-up, and ideas 7 and 8 are stretch projects once the basics are solid.

Where can you find free datasets?

Start with three free sources and check the licence or terms on each dataset before you use it.

• World Bank data. The India country page describes "free and open access to global development data," with more than a thousand indicators covering social, economic, environment and institutional topics, and downloads in CSV, XML and Excel.
• Open Government Data platform India. The National Portal's page on the platform says it features open government data, datasets and APIs, and that the National Informatics Centre maintains the portal under MeitY. The datasets themselves are published at data.gov.in, so check each dataset's format and terms before you plan a project around it.
• Kaggle Datasets. Kaggle's dataset page describes hundreds of thousands of open datasets to browse and download. Read each dataset's own licence and description.

Coursera's guide also lists CDC Wonder, Data.gov, Data.world and Reddit's r/datasets for cleaning practice, and NYC Open Data among exploratory sources.

How do you choose which project to build?

Choose by asking four questions in order. This is our own framework.

1. Does the question interest you? You will spend hours on it, so pick a topic you can explain.
2. Can you finish it in two to three weekends? A small finished project beats a large abandoned one.
3. Does it add a skill the others lack? Cover cleaning, SQL and a dashboard between your projects.
4. Can a stranger understand the result? If you cannot state the finding in one sentence, narrow the question.

If you are unsure where to begin, our data analyst roadmap for India shows when in your learning plan each project fits.

What does a finished project look like? (an illustration)

The walkthrough below is invented to show the shape of a project, using the kinds of indicators the World Bank's India page lists, such as GDP and internet usage. It does not report real findings.

1. Question. Has internet usage in India grown faster than GDP since the year you start?
2. Data. Download the two indicators as CSV or Excel files from the World Bank page.
3. Clean. Reshape the year columns into rows, fix missing years and log every change.
4. Analyse. Calculate year-on-year change for both indicators and compare them.
5. Visualise. Draw one line chart with both series, and a short table of the biggest gaps.
6. Conclude. Write three sentences on what the comparison shows and what it cannot show.

The technical work is modest. What makes the project useful is the clear question, the visible cleaning log and the honest limits.

How should you present a project?

Give each project a page a recruiter can read in two minutes. Coursera suggests free visualisation tools such as Tableau Public, Datawrapper and RAWGraphs for sharing charts.

For each project, write a short readme with these parts.

• The question, in one sentence.
• The data source, with a link and the date you downloaded it.
• The method, naming the tools and the main steps.
• The result, with one chart and a plain-language conclusion.
• The limits, covering what the data cannot tell you.

Then place the best projects at the top of your resume. Our guides on how to list projects on a resume and on the data analyst resume for freshers show the wording.

Is scraping data for a project allowed?

It depends on the website. Coursera's guide advises that whenever you scrape data from the internet, you should respect and follow each website's terms of service.

Read the terms before you collect anything, prefer downloadable datasets where they exist, and never publish personal data. If a site forbids scraping, choose another source.

What do most guides on data analyst projects get wrong?

Many guides give a list of ideas and stop there. These are the gaps.

• They recommend the same famous datasets. Recruiters see the same projects repeatedly, so a question of your own stands out.
• They stop at the chart. A project needs a question, a method and a conclusion, not only a visual.
• They ignore India. A dataset on your own country or city is easier to explain and more relevant to local employers.
• They skip the write-up. If the readme is missing, few people will open the code.

FAQ

What projects should a data analyst fresher build?

Build two or three projects that show different steps of the job: a data-cleaning project, an SQL analysis on a business dataset, and a dashboard. Use real, free datasets and write a short readme with the question, method and result for each one.

Where can I find datasets for data analyst projects?

Free options include World Bank data, which offers CSV and Excel downloads, India's Open Government Data platform, and Kaggle Datasets. Coursera also lists sources such as Data.gov and Data.world. Read the licence or terms on each dataset before you use it.

How many projects do I need in my data analyst portfolio?

There is no fixed number in the sources we read. We suggest two or three finished, well-explained projects for a fresher, which is our editorial view. Quality and clarity matter more than count, so improve the write-ups before you add more projects.

What are good SQL projects for a data analyst?

Pick a retail, orders or customer dataset and answer five business questions with joins, grouping and filtering, such as which customers ordered more than once. Save the queries in a file, explain each answer in a sentence, and share the file.

Do I need Power BI or Tableau in my projects?

One dashboard project helps, because many analyst postings name Power BI or Tableau. Coursera lists Tableau Public as a free tool for sharing visualisations. Choose the tool that appears in the postings you want, and explain the decision the dashboard supports.

How do I put data analyst projects on my resume?

Give each project a title, the tools used, and one or two lines on the question and the result. Link to the write-up or repository. Keep the strongest two or three near the top, especially if you have no work experience.

Final thoughts

The best portfolios are small and clear: a real question, a real dataset, visible cleaning and an honest conclusion. Finish two or three, write them up well, and put them where recruiters will see them.

To see how a project reads on your resume against a real posting, try the AI resume reviewer on asuraa.in. For a wider view of proof-of-work, read our guide on how to build a portfolio for jobs.