Data Scientist Projects: 8 Ideas With Open Data Sources
Eight data scientist projects for freshers, where to find open datasets (including Indian government data), and how to finish and present them.
Last updated: 21 September 2026 · By the Asuraa Team
What makes good data scientist projects for a fresher?
Good data scientist projects start with a question you chose, use real data, and end with a result you can explain in two minutes. StrataScratch's guide to data science projects for beginners recommends quality over quantity: three to five projects at most, matched to the skills and tools the target role asks for.
It also says a portfolio made only of Kaggle competitions is weaker than one that shows you defining a problem yourself, and that end-to-end work signals more than isolated notebooks. Our data scientist skills guide shows which skills to aim each project at.
Where can you find open datasets for data scientist projects?
Start with three sources we opened: NDAP for Indian government data, the UCI Machine Learning Repository, and Kaggle. Each suits a different kind of project.
| Source | What it is | Good for |
|---|---|---|
| NDAP | A platform that says it lets users "search, merge, visualize, and download" Indian government datasets; its listed areas include agriculture, economy, health, education, finance and infrastructure | India-specific analysis and visualisation projects |
| UCI Machine Learning Repository | A collection of datasets for machine learning, hosted by the University of California, Irvine; its page showed 689 datasets when we opened it and lets you filter by task, size and area | Classification, regression and clustering practice with well-known data |
| Kaggle Datasets | Describes itself as a place to browse and download hundreds of thousands of open datasets | Wide variety, plus notebooks from other people to compare with |
We could not open the data.gov.in portal in this session, so we do not describe it here. Whichever source you use, read the dataset's licence or terms of use before you publish your project, and note the source and access date in your README.
Which data scientist projects should you build?
Pick projects that cover different skills instead of five versions of the same model. The table below is our own selection, with the categories drawn from the types StrataScratch lists (beginner, end to end, AI-powered, industry-specific).
| Project idea | Question it answers | Skills it shows |
|---|---|---|
| Sales or demand analysis | Which products and months drive sales, and what is the trend? | Cleaning, time series, visualisation |
| Customer churn prediction | Which customers are likely to leave, and why? | Classification, class imbalance, business framing |
| Customer segmentation | Which groups of customers behave alike? | RFM analysis, K-Means clustering, communication |
| Delivery or duration prediction | How long will an order or trip take? | Regression, feature engineering |
| Price prediction with a small web app | What should a house or laptop cost, given its features? | Modelling, deployment (for example Flask or Streamlit) |
| Fraud detection | Which transactions look suspicious? | Imbalanced data, precision and recall |
| Indian public-data analysis | How do prices, enrolment or health indicators vary by state and year? | Merging tables, exploratory analysis, clear charts |
| Document question answering | Can a model answer questions from a set of documents? | Retrieval-augmented generation, LLM evaluation |
For an Indian angle, a project built on NDAP data, such as agricultural commodity prices or school statistics, is easier to explain to an Indian recruiter than another house-price model. If you are early in your journey, follow the stages in the data scientist roadmap and keep projects small until each stage is done.
How do you finish a data science project properly?
Follow the same seven steps every time, so each project reads as complete work and not as a half-finished notebook. This checklist is our own suggestion.
- Write the question first. One sentence that a non-technical person could understand.
- Record the data source. Name the source, the access date and the licence.
- Clean and explore. Show what was missing or odd and how you handled it.
- Set a baseline. Start with a simple rule or model so you know what "better" means.
- Evaluate fairly. Keep test data separate and choose a metric that fits the problem.
- Write the result. State what you found, what it could be used for and where it may fail.
- Publish a README. Include the question, data source, method, result and how to run the code.
The metric choice matters most in projects like fraud detection, where accuracy on a rare event can mislead. Our guide to data scientist interview questions covers how interviewers probe exactly this.
What does a worked example look like?
Here is an illustration with invented details, so treat it as a template and not as a real result. Say you choose a project on how commodity prices vary across Indian states, using a dataset from NDAP.
Your question is "which states show the widest price swings across the year?" You record the dataset name, access date and terms, clean the tables, and merge them by state and month. You then chart the swings, write three findings in plain language, and list the limits, such as missing months or inconsistent units.
That is a complete project without any machine learning, and it shows cleaning, analysis and communication. If you wanted a modelling angle, you could add a simple forecast with a baseline and a held-out test period.
How do you present projects on a resume and GitHub?
Give each project two to three bullets: what you built, with which tools, and what the result was. Keep numbers you can defend, such as dataset size or an evaluation metric on held-out data.
Link the repository from your resume and make the README the first thing a visitor sees. Our data scientist resume for freshers guide shows where projects sit on the page. To check how your project bullets read against a job description, try the AI resume reviewer on asuraa.in.
What do most guides on data scientist projects get wrong?
Most guides hand you a long list and stop. These are the usual gaps.
- They list ideas without data sources. An idea with no dataset is a homework problem, not a project.
- They favour the same famous datasets. The same few datasets appear in many portfolios, so a question of your own is likely to stand out.
- They stop at the model. StrataScratch stresses end-to-end work, and a model with no explanation or README shows only half the skill.
- They ignore licences and honesty. Publish only data you may share, and never claim results you did not reproduce.
FAQ
How many data science projects do I need for a resume?
StrataScratch recommends three to five projects at most, chosen to match the skills and tools the target role asks for. Two or three strong, finished projects are usually better than many small ones. Make sure at least one is end to end, from question to written result.
What are good data science projects for freshers?
Good choices include customer churn prediction, customer segmentation, sales analysis, price prediction and an analysis of Indian public data. StrataScratch groups beginner projects like these with end-to-end and industry-specific ones. Pick ideas that match the role you want and that you can explain fully.
Where can I find datasets for data science projects in India?
NDAP offers downloadable Indian government data across areas such as agriculture, health, education and finance. The UCI Machine Learning Repository and Kaggle offer general datasets. Check each dataset's licence or terms, and record the source and access date in your project README.
Are Kaggle projects enough for a data science portfolio?
Not on their own. StrataScratch says Kaggle work is useful for learning but should be supplemented with end-to-end projects where you define the problem yourself. Combine one or two Kaggle exercises with a project built on data you sourced, cleaned and explained.
Do I need machine learning in every data science project?
No, because a well-framed analysis with careful cleaning, clear charts and written findings can show strong skills. Add machine learning when the question calls for prediction, and compare it against a simple baseline. A complex model with no reason behind it can look worse than a clean analysis.
How do I show data science projects on my resume?
Give each project two or three bullets covering what you built, the tools you used and a result you can defend, such as dataset size or a metric on held-out data. Add a repository link with a clear README so a recruiter can check the work quickly.
Final thoughts
Data scientist projects work when they are finished, honest and explained. Choose a few that cover different skills, use data whose source you can name, and write the result like a short report.
Once you have a project or two, ask someone in the field to review them, and see how to find a data science mentor in India.
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