Guide 💼 The data analyst portfolio that gets you hired — 5 free-data projects (2026)

Hard truth first: recruiters don't read certificates, they skim proof of work. Five solid projects beat fifteen course badges, every single time. Here's exactly which 5 to build, in order, with data that costs ₹0.

🧱 The rules before you start

• Every project gets a public GitHub repo with a README that explains the question, the data, and what you found — the README is the portfolio, the code is the appendix
  
• Every project answers a business question, not a tutorial prompt. "Which products drive repeat orders?" hits. "Titanic survival prediction" does not.
  
• One project = one weekend, two max. Shipped beats perfect.
  

1️⃣ SQL deep-dive — the interview skill
Take a real transactional dataset (retail orders, food delivery, Olist Brazilian e-commerce — all free on Kaggle). Load it into PostgreSQL or SQLite and answer 10 business questions with pure SQL: top repeat customers, monthly retention cohorts, revenue by category over time.
Proves: you can query — the #1 tested skill in analyst interviews. Put the 10 questions + queries in the README.

2️⃣ The messy-data cleanup
Grab a genuinely dirty public dataset (government open data portals are gold for this — missing values, mixed formats, spelling chaos). Document every cleaning decision in a notebook: what you dropped, what you fixed, and why. Our free datasets field guide shows where to find these and how to clean them step by step with pandas.
Proves: you survive real-world data — 80% of the actual job.

3️⃣ The dashboard a manager would actually open
Pick one domain (sales, IPL stats, your own expenses — anything) and build ONE clean dashboard in Power BI (free) or Looker Studio (free). Rules: max 5 charts, one clear headline insight at the top, filters that work.
Proves: you can communicate, not just calculate. Screenshot it into the README for the non-clickers.

4️⃣ Exploratory analysis with a point of view
Take a dataset about something you genuinely like — movies, cricket, stocks, gaming — and do a full EDA that ends with 3 surprising findings written in plain English. Not "correlation is 0.7" but "sequels earn 40% less on average, except in horror."
Proves: curiosity + storytelling. This is the project that gets remembered in interviews.

5️⃣ The end-to-end capstone
One project that chains it all: raw data → clean → SQL/analysis → dashboard → one-page written summary with a recommendation ("if I were the business, I'd do X"). Host the summary as a blog-style post and link everything.
Proves: you can own the whole pipeline — this is the "hire them" signal.

🚫 Mistakes that get portfolios binned

• Titanic / Iris / MNIST clones — recruiters have seen ten thousand; it signals "followed a tutorial"
  
• Code with no README, or a README with no findings
  
• Ten half-finished repos instead of five finished ones
  
• Charts with no takeaway sentence under them — always write the "so what"
  

📅 The 4-week plan

1. Week 1 → project 2 (cleanup) + project 1 (SQL)
   
2. Week 2 → project 3 (dashboard)
   
3. Week 3 → project 4 (EDA story) — post the 3 findings on LinkedIn too, recruiters lurk there
   
4. Week 4 → capstone + polish every README like it's a product page
   

TL;DR — 5 projects, free data, public repos, READMEs with real findings: SQL → cleanup → dashboard → story → capstone. That's the whole formula.

Working on a portfolio right now? Drop your project idea below — happy to tell you which of the 5 slots it fills best. 👇