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Data Structures

Learn invariants, complexity, memory layout, edge cases, and implementation tradeoffs across related structures.

10 Concepts71 Articles21h 16m

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Learn invariants, complexity, memory layout, edge cases, and implementation tradeoffs across related structures.

How this topic helps

Algorithms
System Design
Java
Langchain

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Article 1The Ultimate Data Structures Cheat SheetTLDR: Data structures are tools. Picking the right one depends on what operation you do most: lookup, insert, delete, ordered traversal, top-k, prefix search, or graph navigation. Start from operation15 minArticle 2Tries (Prefix Trees): The Data Structure Behind AutocompleteTLDR: A Trie stores strings character by character in a tree, so every string sharing a common prefix shares those nodes. Insert and search are O(L) where L is the word length. Tries beat HashMaps on 17 minArticle 3Exploring Backtracking Techniques in Data StructuresTLDR: Backtracking is "Recursion with Undo." You try a path, explore it deeply, and if it fails, you undo your last decision and try the next option. It explores the full search space but prunes inval13 minArticle 4Big O Notation Explained: Time Complexity, Space Complexity, and Why They Matter in InterviewsTLDR: Big O notation describes how an algorithm's resource usage grows as input size grows โ€” not how fast it runs on your laptop. Learn to identify the 7 complexity classes (O(1) through O(n!)), deriv34 minArticle 5Python Data Structures: Lists, Dicts, Sets, and TuplesTLDR: Python's four built-in collections are not interchangeable โ€” their internals are fundamentally different. list is a dynamic array: fast at the end, slow for membership. dict is a hash table: O(126 minArticle 6DFS โ€” Depth-First Search: Go Deep Before Going WideTLDR: DFS explores a graph by diving as deep as possible along each path before backtracking, using a call stack (recursion) or an explicit stack. It is the go-to algorithm for cycle detection, path f15 min

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