Learn by building
Project Ideas
These are briefs, not tutorials. Each one states the problem, what counts as finished, the order to build in and where to go when you get stuck — and leaves the code to you. That is the difference between a project you can explain in an interview and one you followed along with.
Data & AI (5)
Exploratory Data Analysis
Take a messy public dataset from raw file to a defensible conclusion.
Movie Recommendation Engine
Collaborative and content-based recommendations, evaluated honestly.
Sentiment Analysis API
A trained classifier served behind a real, monitored HTTP endpoint.
RAG Document Q&A
Ask questions of your own documents, with citations and honest failure.
Image Classifier
Fine-tune a pretrained vision model on your own small dataset.
Web Development (5)
Personal Portfolio Website
The site that presents everything else you build.
URL Shortener
A short link service with redirects, analytics and rate limiting.
Real-Time Chat App
Rooms, presence and live messages over WebSockets.
Kanban Task Board
Drag-and-drop task management with persistence and optimistic updates.
E-Commerce Storefront
Catalogue, cart, checkout and orders — the full commercial flow.
Python (4)
Expense Tracker CLI
A command-line tool that records spending and reports where the money went.
Price Tracker Web Scraper
Watch a product page and alert you when the price drops.
Automated File Organizer
Sorts a messy folder by type, date or rule — safely.
Static Blog Generator
Turn a folder of Markdown into a fast, deployable website.
Databases (2)
Library Management System
A properly normalised schema with loans, returns and constraints that hold.
SQL Analytics Dashboard
Turn a transactional database into answers a business would act on.
DevOps (2)
Looking for the course capstones?
Those are shorter, course-linked coding challenges with starter code — one per AI & ML course, rather than multi-week briefs. They live in their own section.
Frequently Asked Questions
How is this different from a tutorial?
A tutorial gives you the code. These give you the brief — the problem, what counts as finished, and the order to build in — and leave the code to you. That difference is the entire point: following along teaches almost nothing, and reviewers can tell which one you did.
Which project should I pick?
The one in the area you want to work in, at the level just above where you are comfortable. One finished project you can explain in depth beats three abandoned ones, so choose something whose "done" list you believe you can complete.
Are the time estimates realistic?
They are ranges, and a first attempt will usually run past them. The range is there to signal scope — an evening versus a fortnight — not to time you. Taking longer is normal and is not a signal about your ability.
What makes a project worth putting on a CV?
That it is finished, deployed and explainable. Handled edge cases, a readable README and a live URL do more than an ambitious half-built system, because a reviewer can actually verify the first and cannot assess the second.