6. Project Library
A library of projects by course, difficulty and career direction. Projects are the best way to turn knowledge into evidence of ability.
How it's organized​
- By course: DSA · Databases · Operating Systems · Networks · AI · Web · IoT.
- By level: Beginner · Intermediate · Advanced.
- By career direction: AI · Software · Data · Security · Systems (see Choose Your Direction).
Template for describing a project​
Each project in the library should include:
- Goal — what you build, what problem it solves.
- Prerequisite knowledge — required courses/skills.
- Deliverables — what you hand in (code, demo, report).
- Tools — language, framework, infrastructure.
- Suggested repo structure — folder layout.
- Completion criteria — when it counts as "done".
Examples by difficulty​
| Level | Example projects |
|---|---|
| Beginner | CLI to-do app; calculator; re-implement basic data structures |
| Intermediate | Web app with database; data dashboard; simple chatbot |
| Advanced | Mini compiler; data pipeline + warehouse; vision/robot project |
Examples by course​
- DSA: maze solver (BFS/DFS/A*), sorting-algorithm benchmark with timing.
- Databases: a library/booking system with a normalized schema + reporting queries.
- Operating Systems: a mini shell, a process scheduler.
- Networks: a network monitor, a client–server chat over sockets.
- AI: image classifier, OCR, chatbot, vision robot.
- Web: a SaaS mini-product with auth + CI/CD.
- IoT: see Courses → IoT (temperature/humidity sensing, dashboard, OTA).
Repo structure templates and how to document: How to document a project.