Computer Science · DSA Lab

Understand the pattern before you write the code.

Programming and DSA taught as concepts first — then visualised, coded and practised with real problems.

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two_sum.py

def two_sum(nums, target):
    seen = {}
    for i, n in enumerate(nums):
        need = target - n
        if need in seen:
            return [seen[need], i]
        seen[n] = i

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Learn the concepts

Guides that explain each data structure, with code in Python, JavaScript, Java and C++.

All guides

Big-O and Complexity

How to describe how fast code grows as its input grows, and why O(n log n) beats O(n²) long before your computer runs out of patience.

12 min read

Arrays and Dynamic Arrays

Why reading arr[i] is instant, why inserting at the front is slow, and how lists that grow (Python list, ArrayList, vector) work underneath.

14 min read

Prefix Sums and Difference Arrays

Precompute cumulative information for constant-time range queries, then reverse the idea to apply many range updates efficiently.

14 min read

Linked Lists

What a linked list is, how nodes and pointers fit together, and how to build one yourself in Python, JavaScript, Java and C++.

15 min read

Stacks

Last in, first out. How a stack works, how to build one on top of an array, and the problems it solves, from matching brackets to undo.

11 min read

Queues and Deques

First in, first out. How a queue works, why a circular buffer makes both ends O(1), and where queues show up, from schedulers to breadth-first search.

13 min read