20 Best Data Analytics Projects for Portfolio & Resume (2026)

Build a standout data analytics portfolio with 20 hands-on projects — from beginner Excel dashboards to advanced Python ML pipelines. Each project is designed to impress recruiters and fill gaps in your resume.

20 ProjectsExcel · SQL · Python · Power BI · TableauUpdated 2026

BeginnerIntermediateAdvanced

Beginner Projects (No Coding Required)

Perfect if you are just starting out. These projects use Excel, Power BI, and Tableau — no programming needed.

Sales Dashboard in Excel

Beginner

Tools: Excel, Pivot Tables, Charts

What you'll build

A monthly sales performance tracker

Skills learned

Pivot tables, conditional formatting

Why recruiters love it

Shows real business thinking

HR Attrition Analysis

Beginner

Tools: Excel + Google Sheets

What you'll build

Analyze employee turnover data

Skills learned

VLOOKUP, charts, conditional formatting

Why recruiters love it

HR analytics is in demand

Covid-19 Data Visualization

Beginner

Tools: Power BI

What you'll build

An interactive dashboard from public data

Skills learned

DAX basics, slicers, maps

Why recruiters love it

Shows initiative with public datasets

E-commerce KPI Dashboard

Beginner

Tools: Power BI / Tableau

What you'll build

Track revenue, conversion, returns

Skills learned

Dashboard design, DAX

Why recruiters love it

Common interview project

Student Performance Analysis

Beginner

Tools: Excel

What you'll build

Analyze grades, identify patterns

Skills learned

Pivot tables, statistical charts

Why recruiters love it

Easy to explain in interviews

Superstore Sales Analysis

Beginner

Tools: Tableau Public

What you'll build

Classic beginner project with retail data

Skills learned

Tableau charts, filters, story

Why recruiters love it

Tableau Public hosts it for free

Intermediate Projects (SQL & Python)

Level up with SQL queries and Python scripts. These projects are the most commonly asked in data analyst interviews.

Customer Segmentation using SQL

Intermediate

Tools: SQL + Python

What you'll build

Group customers by purchase behavior

Skills learned

SQL JOINs, Python clusters

Why recruiters love it

Very common interview question

Financial Stock Analysis

Intermediate

Tools: Python + Matplotlib

What you'll build

Analyze stock price trends

Skills learned

Pandas, yfinance, visualizations

Why recruiters love it

Shows finance domain knowledge

Movie Recommendation Analysis

Intermediate

Tools: Python + SQL

What you'll build

Analyze viewing patterns

Skills learned

Pandas, SQL, groupby

Why recruiters love it

Netflix-style project

Delivery Time Prediction

Intermediate

Tools: Python

What you'll build

Predict order delivery time

Skills learned

Regression, feature engineering

Why recruiters love it

Logistics sector popular

Marketing Campaign Analysis

Intermediate

Tools: SQL + Power BI

What you'll build

Measure campaign ROI

Skills learned

SQL aggregations, BI dashboards

Why recruiters love it

Marketing analytics

Retail Inventory Optimization

Intermediate

Tools: SQL + Excel

What you'll build

Identify overstock/understock

Skills learned

SQL window functions, Excel models

Why recruiters love it

Operations domain

Advanced Projects (Production-Ready)

Stand out for senior and specialist roles with end-to-end pipelines, ML models, and live dashboards.

End-to-End Data Pipeline

Advanced

Tools: Python + SQL + Power BI

What you'll build

Build ETL pipeline + dashboard

Skills learned

Full analytics stack

Why recruiters love it

Senior roles expect this

Customer Churn Prediction

Advanced

Tools: Python + ML

What you'll build

Predict which customers will leave

Skills learned

Logistic regression, feature importance

Why recruiters love it

Cross-functional skill

Real-time Sales Dashboard

Advanced

Tools: Power BI with live data

What you'll build

Auto-refreshing KPI dashboard

Skills learned

DirectQuery, scheduled refresh

Why recruiters love it

Shows production skills

A/B Test Analysis

Advanced

Tools: Python + Statistics

What you'll build

Analyze experiment results

Skills learned

Hypothesis testing, p-values

Why recruiters love it

Product analytics companies love this

How to Present Projects in Interviews

Use the STAR method — Situation, Task, Action, Result — for every project you discuss.

1. Set the business context first
Start with the real-world problem — "The marketing team needed to understand why CAC spiked in Q3." This shows business thinking before technical skill.
2. Name the tools and why you chose them
Interviewers test decision-making. "I used SQL for aggregation and Power BI for the dashboard because the stakeholder needed daily self-serve access."
3. Walk through your analysis steps
Describe data cleaning, exploration, and the key insight you found. One surprising finding makes the story memorable.
4. Quantify the result
Always end with a number: "Reduced reporting time from 3 hours to 15 minutes" or "Identified ₹4L in overstock that was written off annually."
5. Keep your GitHub README updated
Recruiters open links during calls. A README with a problem statement, screenshot, and setup instructions shows professionalism that 90% of candidates skip.

How to Host Your Data Analytics Portfolio

💻

GitHub

Host SQL scripts, Python notebooks, and Excel files. Add a profile README listing your projects with links. Free forever.

Tip: Pin your best 6 repos to your profile.

📊

Tableau Public

Publish interactive Tableau dashboards for free. Shareable link works in resumes and LinkedIn. No download needed for recruiters.

Tip: Add a project description with the business context.

⚡

Power BI Service

Free Microsoft account lets you publish reports and share read-only links. Ideal for Power BI projects in your portfolio.

Tip: Create a dedicated workspace called "Portfolio".

✍️

Personal Blog / Notion

Write a case study for each project — problem, approach, result. Notion is free and looks professional. Medium is good for reach.

Tip: Even one well-written case study beats five undocumented repos.

Frequently Asked Questions

What data analytics projects should I do?

Start with beginner-friendly projects like a Sales Dashboard in Excel or a Covid-19 Visualization in Power BI. Once comfortable, move to intermediate SQL and Python projects. Focus on domains you enjoy — finance, HR, e-commerce, or marketing — because you will explain these in interviews.

How to make a data analytics portfolio?

Pick 3–5 projects covering different tools (Excel/SQL/Python/Power BI). Host them on GitHub with a clear README, Tableau Public for dashboards, or Power BI Service for reports. Write a 2–3 sentence "story" for each project explaining the business problem, your approach, and the outcome.

Which tool should I use for data analytics projects?

For visualization: Tableau or Power BI. For data manipulation and analysis: SQL and Python (Pandas). For quick ad-hoc analysis: Excel. Most job descriptions require at least SQL + one BI tool + Python, so aim to have at least one project for each.

How many projects should I have on my data analytics resume?

3 to 5 strong, well-documented projects are ideal for a resume. Quality matters far more than quantity. Each project should clearly state the business question, tools used, methodology, and a quantified result (e.g., "reduced churn prediction time by 40%").

Ready to Build All 20 Projects?

Our Data Analytics course walks you through every project step-by-step — with mentorship, live sessions, and a portfolio review before you apply.

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