Data Structures & Algorithms for Beginners

Understanding how Random Access Memory works and why it matters for data structures. Learn about memory addresses, bytes, and how computers store data.

Learn about fixed-size arrays, their memory layout, and basic operations. Understand how arrays store elements contiguously in memory.

Understanding resizable arrays, amortized analysis, and how languages implement them. Learn about array doubling and the ArrayList/Vector pattern.

Learn the stack data structure, LIFO principle, and common applications like function call stacks, undo operations, and expression evaluation.

Introduction to linked lists, node structure, and basic operations. Learn why linked lists exist and when to use them over arrays.

Learn about doubly linked lists with forward and backward traversal. Understand the trade-offs between singly and doubly linked lists.

Understanding the queue data structure and FIFO principle. Learn implementations using arrays and linked lists.

Understanding recursive thinking, base cases, and the call stack. Learn to think recursively and avoid common pitfalls.

Classic recursive problems: factorial calculation and Fibonacci sequence. Learn memoization to optimize recursive solutions.

A simple sorting algorithm that builds the sorted array one element at a time. Great for small datasets and nearly sorted arrays.

Divide and conquer sorting algorithm with O(n log n) complexity. Learn the merge operation and recursive decomposition.

Efficient in-place sorting using partitioning and pivot selection. Understand average vs worst case performance.

Distribution sort that works in linear time for uniformly distributed data. Learn when bucket sort outperforms comparison sorts.

Efficient searching in sorted arrays using divide and conquer. Master the classic binary search algorithm.

Finding ranges, boundaries, first/last occurrences, and searching in rotated arrays. Advanced binary search applications.

Introduction to tree data structure, terminology, and properties. Learn about nodes, edges, height, depth, and tree traversal.

BST property, searching, and why BSTs are useful. Understand the ordering invariant that makes BSTs efficient.

Implementing insertion and deletion in binary search trees. Handle all deletion cases including nodes with two children.

DFS traversal: preorder, inorder, and postorder traversals. Understand recursive and iterative implementations.

Level-order traversal using queues. Learn when BFS is preferred over DFS for tree problems.

Using BSTs to implement Set and Map data structures. Understand TreeSet and TreeMap implementations.

Understanding the backtracking paradigm and decision trees. Learn the template for solving backtracking problems.

Using backtracking to find paths through mazes. Apply the backtracking template to a visual problem.

Understanding the heap data structure and its properties. Learn about complete binary trees and the heap invariant.

Implementing insertion and extraction in heaps. Learn sift-up and sift-down operations for maintaining the heap property.

Building a heap from an array in linear time. Understand why heapify is O(n), not O(n log n).

Understanding hash tables and their O(1) average time complexity. Learn the fundamentals of hash-based data structures.

Designing hash functions and handling collisions. Learn chaining and open addressing strategies.

Graph terminology, representations, and types. Learn vertices, edges, directed vs undirected, weighted graphs.

Depth-first search on 2D grids and matrices. Solve island counting, flood fill, and connected components.

Breadth-first search on matrices for shortest path problems. Learn when BFS guarantees optimal paths.

Graph representation using adjacency lists. Compare with adjacency matrix and understand space-time tradeoffs.

Introduction to dynamic programming with 1D problems. Learn overlapping subproblems, optimal substructure, and memoization.

Solving problems with 2D state spaces. Grid paths, edit distance, and longest common subsequence.

Understanding bitwise operators AND, OR, XOR, NOT, shifts. Learn how computers represent numbers in binary.

Practical bit manipulation techniques for problem solving. Check bits, count bits, power of two, and more.