Data Structures & Algorithms, A Story
Every structure and algorithm derived from a real problem — following the GeeksforGeeks curriculum, told as a story, with code in Python, TypeScript, and Java.
Most DSA courses hand you a definition and a diagram, then wonder why none of it sticks. This track is different. Every lesson starts with a problem you can feel — an app that's crawling, a task eating your whole afternoon — and then we discover the data structure or algorithm together, the same way it was really invented.
By the time we name the thing ("this is a hash table", "this is binary search"), you already understand why it has to exist. The name becomes a label for something you own.
Follows the GeeksforGeeks DSA curriculum
The topics and ordering mirror the GeeksforGeeks DSA Tutorial, so you can use this as a story-driven companion to it. Code samples come in Python, TypeScript, and Java.
The roadmap
1 · Fundamentals
Two programs solve the same task — one finishes before you blink, the other never finishes at all. The difference is data structures and algorithms.
2 · Recursion & Math
A problem that contains smaller copies of itself can be solved by a function that calls itself — once it clicks, whole classes of problems get simpler.
3 · Arrays & Strings
You need to store a million readings and grab any one instantly — arrays give you that superpower, with one big catch.
4 · Searching
Looking for one item by checking each in turn is the most natural thing in the world — and the baseline every faster search must beat.
5 · Sorting
Sorting feels like busywork until you notice it unlocks binary search, deduplication, and half the algorithms you’ll ever write.
6 · Bit Manipulation
Underneath every number is a row of switches — learn AND, OR, XOR, and shifts, and some problems become one-liners.
7 · Hashing
How do you turn "a name" into "a shelf number" so you always know where to look? That mapping is a hash function.
8 · Two Pointers
Checking every pair takes forever — but two pointers walking a sorted array collapse many problems from O(n²) to O(n).
9 · Sliding Window
Finding the best stretch of consecutive items shouldn’t mean re-checking every window from scratch — slide instead of restart.
10 · Prefix Sum
You keep being asked for the sum of a range, over and over — precompute once and every query becomes a subtraction.
11 · Backtracking
To solve a puzzle you try a move, and if it dead-ends you undo it and try another — brute force with a brain.
12 · Linked List
Inserting at the front of a giant array means shifting everything — what if each item just pointed to the next instead?
13 · Stack
The undo button, the back button, matching brackets — they all share one rule: last in, first out.
14 · Queue
A print line, a support queue — first come, first served — is a data structure with its own rules.
15 · Deque
Sometimes you need to add and remove from both ends — a double-ended queue is the best of both worlds.
16 · Binary Tree
File systems, org charts, family trees all branch — the binary tree is the simplest way to capture that shape.
17 · Binary Search Tree
What if a tree kept itself sorted, so every lookup could throw away half the remaining nodes? That’s a BST.
18 · Heap
You constantly need the single most urgent task out of thousands, and it keeps changing — a heap hands it over instantly.
19 · Graph
Cities linked by roads, people by friendships, tasks by dependencies — before we explore a network, we must store it.
20 · Greedy
Sometimes grabbing the best option at every step gives the best overall answer — and sometimes it fails. Knowing which is the game.
21 · Dynamic Programming
Computing the 50th Fibonacci naively makes billions of repeated calls — DP is the art of never solving a subproblem twice.
22 · Number Theory
A handful of number facts — divisibility, GCD, primes — power a surprising range of algorithms.
23 · Trie
Autocomplete has to find every word starting with "str" out of millions — a trie makes prefix search almost free.
24 · String Matching
Finding "cat" inside a huge document by re-checking every position wastes work — smarter algorithms never look twice.
25 · Range Queries
You need the sum (or min, or max) of any range in a constantly-changing array — a segment tree answers and updates in log time.
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