Data Analyst Projects for Your Resume: 8 Ideas and How to Present Them

Last updated: 13 October 2026 · By the Asuraa Team

Quick answer: The best data analyst projects for a resume answer a business question and end in a recommendation. Eight options with public data: Olist delivery delays (about 100,000 orders), telecom churn, IBM HR attrition, UCI Bank Marketing targeting (45,211 records), hotel cancellations, Play Store gaps, rental price drivers and a website conversion funnel (12,330 sessions). Present two or three, each with two bullets: action, dataset size, tool, method, finding.

Key takeaways

• Data analyst resume projects work best when they answer a business question and end in a recommendation a manager could act on.
• The UCI Bank Marketing dataset has 45,211 records with 16 features and a yes/no target for term-deposit subscription.
• The UCI Online Shoppers Purchasing Intention dataset has 12,330 sessions, of which 1,908 ended in a purchase, making it suited to funnel analysis.
• The Olist Brazilian e-commerce dataset on Kaggle covers about 100,000 orders with product, customer and review information across multiple tables.
• A strong project bullet follows one formula: action verb, dataset size, tool, method and the specific finding or recommendation.

Which data analyst projects look best on a resume?

The projects that look best on a data analyst resume mirror real business questions: why customers leave, why orders arrive late, which campaign converts, why employees quit. Each of the eight ideas below uses a public dataset, produces a recommendation a manager could act on, and fits in one or two resume bullets.

Two or three of these, finished properly, are enough. Pick the ones closest to the industry you are applying to: churn for telecom and SaaS, attrition for HR analytics roles, conversion for e-commerce and fintech.

What does each project involve, and how do you write it up?

Every project below has a goal, four to five steps, what it proves, and an example resume bullet. The bullets use brackets where your own numbers go; never fill them with figures you did not actually find.

1. E-commerce delivery delays

Goal: find which product categories and seller regions cause late deliveries and lower review scores. Dataset: Olist, about 100,000 orders with product, customer and review information.

1. Load the orders, items, sellers and reviews tables into PostgreSQL or SQLite.
2. Calculate delivery delay as actual minus estimated delivery date.
3. Join delay to review score and compare average ratings for late versus on-time orders.
4. Rank seller states and categories by late-delivery share.
5. Recommend where to tighten seller service levels.

Shows: joining several tables and defining a KPI clearly. Example resume bullet (example only): "Joined [n] Olist tables in SQL to show late orders scored [x] stars lower on average; identified [y] seller regions driving most delays."

2. Telecom churn drivers

Goal: explain which customer groups churn most. Dataset: Telco Customer Churn.

1. Convert total charges to numeric and handle blanks.
2. Compare churn rates by contract type, tenure band and payment method.
3. Build a simple segment table showing size and churn rate together.
4. Propose one retention offer aimed at the largest high-churn segment.

Shows: segmentation and turning rates into a recommendation. Example resume bullet (example only): "Segmented telecom customers by contract and tenure; found month-to-month users churned at [x] times the rate of annual users and proposed a [y]-month discount test."

3. Employee attrition analysis

Goal: identify factors linked with attrition for an HR leadership summary. Dataset: IBM HR Analytics Employee Attrition & Performance.

1. Calculate overall attrition rate and rates by department and job role.
2. Cross-tab attrition against overtime, distance from home and years since promotion.
3. Visualise the three strongest differences.
4. Write a one-page memo with caveats about correlation versus cause.

Shows: HR metrics and careful interpretation. Example resume bullet (example only): "Analysed attrition across [n] employees; showed staff working overtime left at [x]% versus [y]%, presented in a one-page HR memo."

4. Bank campaign targeting

Goal: decide which customers a bank should call in its next term-deposit campaign. Dataset: UCI Bank Marketing, 45,211 records with 16 features and a yes/no subscription target.

1. Compute conversion rate overall and by job, age band and previous contact outcome.
2. Calculate lift for each segment versus the average.
3. Check how conversion changes with number of calls in the campaign.
4. Recommend a call list and a cap on repeat calls.

Shows: conversion analysis and cost-aware thinking. Example resume bullet (example only): "Analysed 45,211 bank campaign contacts; found [segment] converted at [x] times average and recommended capping calls at [n] per customer."

5. Hotel cancellation patterns

Goal: explain when and why bookings are cancelled. Dataset: Hotel Booking Demand.

1. Create lead-time bands (0-7 days, 8-30, 31-90, 90+).
2. Compare cancellation rates by band, hotel type, deposit type and month.
3. Plot monthly bookings against cancellations.
4. Suggest a deposit or overbooking policy.

Shows: seasonality and feature banding. Example resume bullet (example only): "Showed bookings made over [n] days ahead were cancelled at [x]%; proposed a deposit rule for long-lead bookings."

6. App market gaps

Goal: find app categories with strong demand but weak ratings. Dataset: Google Play Store Apps, data on about 10,000 apps.

1. Clean installs, size and price strings into numbers and remove duplicates.
2. Compute median installs and rating per category.
3. Plot categories on a demand-versus-quality chart.
4. Pick two categories that look under-served.

Shows: cleaning messy strings and benchmarking. Example resume bullet (example only): "Cleaned [n] Play Store listings and benchmarked [m] categories; flagged [category] for high installs but below-median ratings."

7. Rental price drivers

Goal: explain what drives rent across cities. Dataset: House Rent Prediction Dataset, with more than 4,700 listings.

1. Remove extreme outliers and compute rent per square foot.
2. Compare by city, furnishing status and number of bedrooms.
3. Check whether tenant preference changes asking rent.
4. Present a simple table a relocating fresher could use.

Shows: outlier handling and fair comparisons. Example resume bullet (example only): "Analysed 4,700+ rental listings; showed furnished flats in [city] commanded a [x]% premium per sq ft."

8. Website conversion funnel

Goal: find which session behaviours lead to a purchase. Dataset: UCI Online Shoppers Purchasing Intention, 12,330 sessions of which 1,908 ended in a purchase.

1. Compute conversion rate by month, visitor type and traffic type.
2. Compare product-page time and bounce rate for buyers and non-buyers.
3. Check whether sessions near special days convert differently.
4. Recommend two site or marketing changes.

Shows: product-analytics thinking. Example resume bullet (example only): "Analysed 12,330 web sessions; found returning visitors converted at [x]% versus [y]% for new visitors."

How should you present data analyst projects on a resume?

Put projects in their own section below education, two bullets each, with a GitHub or dashboard link next to the title. Use this formula for each bullet: action verb, dataset size, tool, method, finding or recommendation.

1. Title the project by the business question, such as "Telecom churn drivers", not "Kaggle project".
2. Name the tools in the bullet (SQL, Python, Power BI) so ATS keyword matches work.
3. Lead with the finding, then the method.
4. Link a repository with a README that states the question, data source and three findings.
5. Order projects by relevance to the job you are applying for.

The how to list projects on a resume guide has more layouts, and the data analyst resume for freshers post shows a full sample. To test whether your bullets match a specific listing, paste both into the Asuraa resume reviewer.

What do most guides on data analyst projects get wrong?

Most guides stop at the analysis, but a resume project is judged on the recommendation. "Visualised churn data" tells a recruiter nothing; "Recommended a discount for month-to-month users in the highest-churn segment" shows you think like an analyst.

Many guides also push predictive models into analyst projects. For analyst roles, a clean SQL query, a correct metric definition and a clear chart usually matter more than a classifier. Keep modelling for data science applications, covered in data scientist projects.

Finally, guides rarely mention honesty about the data. The Olist data comes from a Brazilian marketplace and several other popular files are not Indian either; say so in your README and explain what would change in an Indian context.

How do these projects fit into a wider portfolio?

Treat these as the core of a portfolio that also shows Excel and a dashboard tool. The projects and portfolio guide for India covers the full mix, and how to build a portfolio for jobs explains hosting and layout. Asuraa's October 2026 job market report found SQL named in 20.1% of fresher listings, which is why at least one project should be SQL-first.

Related guides

• Projects and Portfolio Guide for Freshers: Data, Software and AI
• Data Analytics Projects for Final Year Students: 10 Dataset Ideas
• SQL Projects for a Data Portfolio: 6 Projects With Free Datasets
• Python Projects for Freshers: 8 Resume-Ready Ideas by Level

FAQ

How many projects should a data analyst fresher put on a resume?

Two or three finished projects are enough. Each should get two bullets that name the dataset size, the tools and a specific finding, plus a link to a GitHub repository or published dashboard. Order them by relevance to the job: a churn project first for a telecom or SaaS role, a conversion funnel first for e-commerce.

What is a good data analyst project for beginners?

Telecom churn and hotel cancellation analysis are good starting points because the questions are clear and the files are small. You compare rates across segments, chart the biggest differences and write one recommendation. Once comfortable, move to the multi-table Olist dataset, which tests SQL joins across orders, items, sellers and reviews.

How do I write a data analyst project as a resume bullet?

Use action verb, dataset size, tool, method and finding. An example only: Analysed 45,211 bank campaign contacts in SQL; found [segment] converted at [x] times the average and recommended a call cap. Replace bracketed parts with your real results, and keep each bullet to one or two lines so recruiters can scan it.

Are Kaggle datasets fine for data analyst resume projects?

Yes, provided you add your own question, analysis and recommendation and credit the source. Recruiters see many copies of the same notebooks, so the framing matters more than the file. Note where the data comes from, for example a Brazilian marketplace for Olist, and say what might differ in India.

Should data analyst projects include machine learning?

Not necessarily. Analyst roles usually test SQL, spreadsheets, metric definitions and clear charts. A project with a correct conversion rate, a fair segment comparison and a well-argued recommendation is stronger for an analyst application than a classifier. Keep predictive modelling for data science roles, where it is expected.

Where should projects go on a fresher data analyst resume?

Place a Projects section directly below Education when you have little work experience. Give each project a business-style title, a link to the repository or dashboard, and two bullets. If you have an internship, list it first and keep the projects section underneath, trimmed to the two most relevant projects.

Final thoughts

Choose two or three projects that match the industry you want, finish each with a clear recommendation, and write bullets that lead with what you found. That is what separates an analyst resume from a list of tools. If you want feedback on which projects fit your target role, a mentor session on Asuraa mentors can help you prioritise.