Data Structures - lesson 8 | HackIndia Skills

Key point

The repo is proof

Your final project is not a folder of random solutions. It is proof that you can choose a data structure, explain the cost, write working code, and test it.

A recruiter or peer should understand your thinking in two minutes without running everything.

Your to-do

Create the structure

Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.

Create a public GitHub repo named dsa-interview-kit or a similar clear name.
Keep one folder per topic: arrays, maps, stacks, queues, linked-lists, trees, heaps, graphs.
Add code files, matching test files, and a short note for each topic.
Use one language if possible, like Python, Java, C++, or JavaScript.
Commit often with messages that say what changed.

0 of 5 done

Example

Simple repo layout

Use names that make review easy. Do not make someone hunt through screenshots or zip files.

dsa-interview-kit/
  README.md
  arrays/
    two_sum.py
    test_two_sum.py
    notes.md
  stacks/
    valid_parentheses.py
    test_valid_parentheses.py
    notes.md
  heaps/
    top_k_frequent.py
    test_top_k_frequent.py
    notes.md
  graphs/
    bfs_shortest_path.py
    test_bfs_shortest_path.py
    notes.md
  run_tests.md

What README must say

Your README is the front door. Keep it short and useful.

Include what the repo covers, how to run tests, and how each note compares choices by time, space, and trade-offs.

Example

README starter

Copy this, then edit it for your language and files.

# DSA Interview Kit

A small interview prep repo with working code, tests, and notes for common data structure choices.

## Topics
- Arrays, strings, maps, sets
- Stacks, queues, linked lists
- Trees and BSTs
- Heaps
- Graphs

## How to run tests
Python:
python -m pytest

JavaScript:
npm test

## What each note explains
- Problem pattern
- Data structure used
- Time complexity
- Space complexity
- Why this choice is better than at least one alternative

Tip

Use AI carefully

You can ask ChatGPT, Gemini, or Claude to review your README and notes. Ask for missing edge cases, unclear cost claims, and weak explanations.

Do not paste AI output blindly. Check every complexity claim, run every test, and remove fake links or invented problem names.

Common mistake

Avoid weak proof

A repo with only solved code is not enough for this track. The skill is choosing the right data structure, not only passing sample input.

Also avoid private repos, broken test commands, huge copied files, and notes that say only 'used map because fast'.