Data Structures & Algorithms

1 Time and Space Complexity, Explained Simply Why we measure algorithms by counting operations instead of stopwatch seconds, and what O(1), O(log n), O(n) and O(n squared) actually mean. 9 min 2 Arrays: How They Sit in Memory and Why That Matters What an array really is at the memory level, why indexing costs nothing, why inserting in the middle is expensive, and the traversal patterns you will reuse everywhere. 10 min 3 Searching: Linear Search and Binary Search How linear search works, how binary search halves the problem each step, exactly when binary search is allowed, and the off-by-one traps that break it. 9 min 4 Sorting Basics: Bubble, Selection and Insertion Sort How the three simple sorting algorithms actually work, which one is least bad in which situation, what stability means, and why all three are O(n squared). 10 min 5 Linked Lists: Nodes, Pointers and Trade-offs What a node really is, why linked lists exist at all, how the basic singly linked list operations work, and an honest comparison with arrays. 10 min 6 Stacks and Queues: LIFO and FIFO The two simplest restricted data structures, why limiting what you can do is useful, how to build both on top of arrays, and where they show up in real software. 9 min 7 Hashing: How Dictionaries Find Things Instantly The idea behind hash tables, why lookup is close to O(1), what collisions are and how they are handled, and when hashing beats sorting. 10 min 8 Recursion: Functions That Call Themselves Base cases and recursive cases, what the call stack is doing underneath, factorial and Fibonacci, why naive Fibonacci is so slow, and when to prefer a loop. 10 min