Data Structures and Algorithms Overview

What are Data Structures and Algorithms

Data structures are ways of organizing and storing data. Arrays, linked lists, stacks, queues, trees, graphs, hash tables — each has its own characteristics suited for different scenarios.

Algorithms are step-by-step procedures for solving problems. Sorting, searching, traversal, dynamic programming, greedy algorithms — each is a general solution for a class of problems.

Why Learn Data Structures and Algorithms

Programming fundamentals. DS&A are the foundation of all programming tasks. Without them, you might use a list where a hash table is needed, with orders of magnitude worse performance.

Interview core requirement. Algorithm problems are mandatory in tech company interviews. Solving hundreds of LeetCode problems is practically standard.

Develop computational learning. Learning algorithms isn't just about problem-solving techniques — it's about decomposing problems, abstract modeling, and analyzing complexity.

Understand AI and systems. Search engines use graph algorithms, databases use B+ trees, compilers use syntax trees, recommendation systems use graphs and matrices.

Core Knowledge Modules

Basic Data Structures

• Arrays vs. linked lists: contiguous storage vs. pointer linking
• Stacks vs. queues: LIFO vs. FIFO
• Hash tables: key-value mapping, collision handling, O(1) average lookup
• Trees: binary trees, BST, AVL, red-black trees
• Graphs: adjacency matrix vs. list, directed vs. undirected

Basic Algorithms

• Sorting: bubble, selection, insertion, merge, quicksort, heap sort
• Search: linear, binary, BFS, DFS
• Recursion: recursive thinking, recursion to iteration

Advanced Topics

• Dynamic programming: optimal substructure, overlapping subproblems, state transitions
• Greedy algorithms: conditions for local-to-global optimality
• Divide and conquer: break large problems into smaller ones
• Graph algorithms: shortest path (Dijkstra, Floyd), MST (Prim, Kruskal), topological sort
• Advanced data structures: heaps, union-find, segment trees, Trie

Complexity Analysis

• Time complexity: O(1), O(log n), O(n), O(n log n), O(n^2)
• Space complexity: how much extra memory an algorithm needs
• Best/worst/average case: performance under different inputs

How to Learn

Learn data structures first, then algorithms. Data structures are the carrier of algorithms. Without understanding linked lists, it's hard to understand linked list sorting.

Solve problems, but think while solving. After each problem: what data structure was used? What algorithm? What's the time complexity?

Solve by topic, not randomly. Finish array and string problems first, then linked lists, then trees, then dynamic programming.

Draw diagrams. Linked list pointer changes, tree rotations, graph traversals — diagrams are far more effective than text alone.

Resources in This Module