Blog›Data Analytics Series›Chapter 26 SERIES · CHAPTER 26Career
Data Analyst Portfolio Guide India 2026 — Projects, GitHub & Dashboards That Get Hired
A practical guide to building a portfolio that Indian hiring managers actually shortlist: 7 project ideas with Indian datasets, a GitHub structure template, free hosting options for dashboards, and a list of signals that hiring managers look for (and red flags they avoid).
7 Project IdeasIndian DatasetsGitHub TemplateFree HostingHiring Signals
Why a Portfolio Matters More in 2026 Than a Certification
In 2023–2024, a Google Data Analytics Certificate or a Power BI certification significantly differentiated candidates in India. By 2026, these certifications are table stakes — nearly every applicant in the 0–3 year experience bracket holds one. What differentiates shortlisted candidates now is demonstrated analytical thinking on real (or realistic) problems.
A portfolio project that asks a clear business question, documents the full analytical process, and ends with a quantified recommendation is worth more than three certificates in 2026. This guide covers how to build exactly that.
7 Portfolio Project Ideas with Indian Context
Each project includes the business story, recommended dataset, tools, and what skills it demonstrates.
#1BeginnerRetail / D2C
E-Commerce Sales Dashboard
Analyse sales, return rate, and customer retention from a CSV of orders. Build an interactive Power BI or Tableau dashboard with city-level maps, category filters, and MoM trend charts.
DATASET
Brazilian Olist dataset (Kaggle) or generate synthetic Indian e-commerce data
TOOLS
Python (pandas), Power BI or Tableau, Excel
THE BUSINESS STORY TO TELL
GMV grew 22% but profit margin fell — identify the return rate spike in Electronics and recommend a product review policy.
pandas EDAdata cleaningdashboard designbusiness storytelling
#2BeginnerSports / EDA
IPL Performance Analytics
Analyse IPL match data to find batting strike rate trends, best powerplay bowlers, and venue-specific win patterns. Visualise with Seaborn and present key findings.
DATASET
IPL dataset on Kaggle (2008–2024)
TOOLS
Python (pandas, Seaborn, Matplotlib), Jupyter Notebook
THE BUSINESS STORY TO TELL
Which player type (opener, finisher, spinner) has had the most impact on match outcomes since T20 rule changes? Present 3 actionable recommendations for a franchise.
pandas groupbyaggregationSeaborn chartsEDA narrative
#3IntermediateMacro / Government Data
Indian CPI Inflation Trend Analysis
Track CPI inflation by category (Food & Beverages, Fuel, Housing, Clothing) over 5 years using MOSPI data. Decompose seasonality and compare urban vs rural trends.
DATASET
MOSPI CPI data from data.gov.in / RBI DBIE
TOOLS
Python (pandas, statsmodels seasonal_decompose, Prophet), Matplotlib
THE BUSINESS STORY TO TELL
Food inflation consistently outpaced headline CPI in Q1 of each year — quantify the Rabi harvest cycle impact and compare to policy rate changes from RBI.
time-series analysisseasonal decompositiongovernment datamacro context
#4IntermediateML / Fintech
Customer Churn Prediction for a Telecom
Build a logistic regression + decision tree model to predict which mobile subscribers will churn next month. Compare model accuracy, plot ROC curves, and translate to business ROI.
DATASET
Telecom Churn dataset (Kaggle) or synthesise Indian telco data (Jio/Airtel context)
TOOLS
Python (pandas, sklearn, matplotlib), Jupyter Notebook
THE BUSINESS STORY TO TELL
Proactively retaining the top 20% at-risk customers with a ₹100 recharge voucher saves ₹12L per quarter vs ₹45L cost of acquiring equivalent new customers.
feature engineeringlogistic regressionmodel evaluationbusiness ROI translation
#5IntermediateCRM / Marketing
RFM Customer Segmentation with SQL + Python
Compute Recency, Frequency, Monetary scores per customer using SQL on a transactional database, then cluster with K-means in Python. Design targeted campaign recommendations per segment.
DATASET
Any orders table — generate synthetic 100k order dataset with Python Faker
TOOLS
SQL (PostgreSQL or BigQuery), Python (pandas, sklearn K-means), Power BI
THE BUSINESS STORY TO TELL
"Champions" segment (RFM 5-5-5) accounts for 38% of revenue from 12% of customers — a birthday voucher campaign for this segment costs ₹2L but prevents ₹18L in potential churn.
SQL joins + aggregationPython K-meansbusiness segmentationdashboard storytelling
#6AdvancedProduct Analytics
A/B Test Analysis — Checkout Flow Redesign
Simulate an A/B test on checkout conversion rate. Conduct statistical significance testing (z-test, chi-square), check for SRM, segment by device and city, and write an executive summary with recommendation.
DATASET
Synthesise using Python — 50k users, 2 variants, realistic Indian device mix (60% Android, 25% iOS, 15% desktop)
TOOLS
Python (scipy, pandas, matplotlib), Jupyter Notebook, Word/Slides for deck
THE BUSINESS STORY TO TELL
Overall test showed +1.4pp lift (significant), but mobile showed +2.8pp vs desktop showed -0.3pp — recommend mobile rollout only while redesigning desktop flow separately.
statistical hypothesis testingsegment analysisbusiness communicationdecision framing
#7AdvancedData Engineering + Analytics
End-to-End Sales Analytics Pipeline
Build a Python ETL pipeline: extract from a CSV (simulating a source system), clean and transform (IST timezone, FY quarter, deduplication), load to a local SQLite or free BigQuery sandbox, then build a live dashboard on top.
DATASET
Synthetic daily sales data (use Python Faker + random to generate)
TOOLS
Python (pandas, SQLite or BigQuery), Apache Airflow (basic DAG), Power BI or Looker Studio
THE BUSINESS STORY TO TELL
Daily revenue dropped 18% on October 3rd — pipeline identified it as a data ingestion gap (Shiprocket API timeout) not a real sales drop. Fixed with a 26-hour lookback window.
ETL pipelinedata qualityAirflow basicsfull-stack analytics
GitHub Repository Structure Template
Your GitHub profile is the first thing a hiring manager checks after your resume. Keep it clean, well-documented, and easy to navigate. Use this structure for every project:
your-portfolio-repo/
├── README.md ← Your portfolio homepage: who you are, skills, links to all projects
├── 01-ecommerce-dashboard/
│ ├── README.md ← Project story: question → approach → findings → recommendation
│ ├── data/
│ │ └── orders_sample.csv ← NEVER commit full raw data; sample only or link to source
│ ├── notebooks/
│ │ └── analysis.ipynb ← Clean, commented notebook; run cells top to bottom
│ ├── sql/
│ │ └── queries.sql ← Standalone SQL file for key queries
│ └── dashboard/
│ └── screenshot.png ← Dashboard screenshot + link to live dashboard
├── 02-churn-prediction/
│ ├── README.md
│ ├── notebooks/
│ └── models/
│ └── churn_model.pkl ← Serialised model for reference
└── 03-ab-test-analysis/
├── README.md
└── notebooks/README is everything
Write it as if the reader has 60 seconds. Start with the business question and the headline finding.
Pin your best 3 repos
GitHub lets you pin 6 repos on your profile. Pin your 3 best projects + your portfolio README repo.
Never commit raw data
Commit a 100–500 row sample only. For large datasets, add instructions in README for how to download from the source.
Profile README.md
Create a repo named exactly your GitHub username. The README.md becomes your profile page. List skills, tools, and links to live dashboards.
Free Dashboard Hosting Options
A live dashboard link is far more impressive than a screenshot. All of these options are free for portfolio use:
| Tool | Cost | Best for |
|---|
| Google Looker Studio | Free | Connect to Google Sheets or BigQuery sandbox. Publish as a public link. Best for beginners — no account needed to view. |
| Power BI Service (free tier) | Free (publish to public) | Publish reports to Power BI Service and share a public link. Limit: 1GB storage on free tier. |
| Tableau Public | Free | Unlimited public dashboards. Well-recognised by Indian hiring managers. Visual quality is high. |
| Streamlit Cloud | Free (3 apps) | Deploy Python Streamlit apps — great for showing ML models or interactive Python analysis. Connects to GitHub. |
| GitHub Pages | Free | Host static HTML/CSS portfolio site from your GitHub repo. Useful for a custom portfolio page with links to all projects. |
| Jupyter nbviewer | Free | Render any public GitHub notebook as a static HTML page with a shareable URL. No deployment needed. |
What Indian Hiring Managers Look For (and Avoid)
Based on feedback from analytics hiring managers at Indian product companies, D2C brands, and consulting firms in 2026:
✅Business question is stated first
Signals analyst thinking, not just technical execution. "I wanted to understand why return rate spiked" is better than "I analysed returns data".
✅Data cleaning steps are documented
Real analysts spend 60–70% of time cleaning. If your project skips this, it looks like a tutorial completion, not real work.
✅SQL and Python both appear
Most Indian analyst roles expect both. If your portfolio only has Excel, you will be screened out for mid-to-senior roles.
✅Findings are quantified
"Return rate was high in Electronics" is weak. "Electronics return rate was 22% vs 8% sitewide, costing ₹14L/month" is strong.
✅Recommendations are included
Analysts who only describe data, but do not recommend action, are less valuable than those who close the loop.
✅Dashboard is live and accessible
A live dashboard link (Looker Studio, Power BI published, Tableau Public) shows you can actually deliver a working product.
❌Generic Iris / Titanic / Boston Housing datasets
These are tutorial datasets. Interviewers see hundreds of them. Using Indian public data or generating domain-relevant synthetic data immediately differentiates you.
❌Notebook cells are out of order or have errors
Sends a signal that the project was rushed or not reviewed. Always restart kernel and run all before publishing.
❌README just says "This project analyses data"
The README is your cover letter for the project. Write the business context, your key finding, and a link to the live dashboard.
❌Too many projects, all shallow
10 dashboards with no analysis behind them is weaker than 3 end-to-end projects that show the full analytical process.
4-Week Portfolio Build Plan
·Pick 3 project ideas from the list above
·Download datasets and do initial explore
·Write the business question in 1 sentence for each
·Data cleaning + EDA (Jupyter notebook)
·SQL queries for aggregation
·Draft findings and recommendation
Week 3
Build Projects 2 & 3
·Project 2: dashboard or ML model
·Project 3: end-to-end pipeline or A/B test
·Write README for each with business story
·Clean all notebooks (restart + run all)
·Host dashboards live (Tableau Public / Looker Studio)
·Push to GitHub, update LinkedIn with portfolio link
The Data Analytics Series
Frequently Asked Questions
How many projects should a data analyst portfolio have in India 2026?
3 to 5 high-quality projects is the ideal range for a data analyst portfolio in India in 2026. Most hiring managers report spending 8–12 minutes on a portfolio during initial screening — enough to open 3 projects and check depth. More than 5 projects is rarely beneficial; it creates noise and signals that you included everything rather than curating your best work. Each project should tell a complete story: the business question, your approach (SQL/Python/Power BI), what you found, and what action you recommended. A common mistake is building 10 shallow projects (just a chart or a dashboard screenshot) instead of 3 deep ones. For freshers with no work experience, 3 projects is a strong target. For professionals switching into analytics from another domain, 2 domain-specific projects plus 1 general analytics project tends to be more compelling than 5 generic ones.
What are the best free Indian public datasets for data analyst portfolio projects in 2026?
The best Indian public datasets for analyst portfolio projects in 2026 are: (1) data.gov.in — India's official government open data portal with datasets on agriculture, education, health, transport, and economy. High credibility; interviewers recognize the source. (2) NITI Aayog datasets — SDG India Index, state development metrics. Good for dashboards. (3) RBI DBIE (Database on Indian Economy) — monetary policy, banking, inflation, interest rates. Best for fintech-focused portfolios. (4) MOSPI (National Statistical Office) — CPI inflation, GDP, employment surveys. (5) SEBI Bhavcopy — NSE/BSE daily stock price data, useful for finance and time-series projects. (6) Zomato dataset (Kaggle) — restaurant data with Indian cities; popular for EDA and clustering projects. (7) IPL match data (Kaggle) — well-structured sports analytics data familiar to Indian recruiters. (8) India road accident data (MoRTH) — useful for geospatial and trend analysis. For e-commerce analytics specifically, look for the Brazilian Olist dataset on Kaggle — while not Indian, it closely mirrors Indian D2C order patterns (COD, multi-seller, logistics) and is accepted as a strong portfolio project.
Do hiring managers in India actually look at GitHub and portfolio links in 2026?
Yes, but differently depending on the company. At product companies (Swiggy, Zomato, CRED, Razorpay, PhonePe, Meesho, Groww) and analytics-first firms, portfolio links are checked — engineers and analytics managers review them before shortlisting for technical rounds. At traditional BFSI companies, consulting firms, or companies that rely on structured ATS screening, the resume itself is more important and the portfolio may be checked only if you get to a technical round. However, even in those cases, a clean portfolio link gives interviewers talking points. The correct approach in 2026: always include a portfolio link in your resume and LinkedIn profile, but do not assume it will be checked. Every project in the portfolio must also be summarisable in a 2-minute spoken explanation, because interviewers will often say "walk me through one of your projects" rather than having read it. Ensure your GitHub README is a polished version of that 2-minute explanation.
EVIKA ACADEMY · NOIDA SECTOR 51
Build Your Portfolio with Mentor Guidance
Our Data Analytics placement programme includes 3 guided portfolio projects using Indian datasets, GitHub and dashboard setup support, and 1:1 mock interviews with working data professionals — so you apply with a portfolio that gets callbacks.
Book Free Demo Class →