Data Structures Intro

A **data structure** is a way to store data so you can **use it efficiently**. If you choose the right structure: - your code becomes faster - your logic becomes simpler - your programs scale better If you choose the wrong structure: - you get slow apps - messy code - hard bugs ## What you will learn in this playlist We will go step by step from beginner to advanced: - Lists, tuples, sets, dictionaries - Stacks, queues, heaps - Trees, graphs (intro level) - How to pick the right structure for the job - Time complexity (in simple words) ## A simple example Imagine you store student names. - If you use a **list**, searching a name can be slow for large data. - If you use a **set**, checking if a name exists is fast. That is the power of picking the right structure. ## Core idea: operations you do most When you choose a data structure, ask: - Do I add items often? - Do I search often? - Do I need ordering? - Do I need uniqueness? - Do I need key-value lookup? ## Graph: how to choose quickly ```mermaid flowchart TD A[Need to store many items] --> B{Need order?} B -->|Yes| C[List / Tuple] B -->|No| D{Need uniqueness?} D -->|Yes| E[Set] D -->|No| F{Need key->value?} F -->|Yes| G[Dict] F -->|No| H[List] ``` ## Key terms (student-friendly) - **Ordered**: items keep position (like list) - **Unordered**: no fixed position (like set) - **Mutable**: you can change it (list, dict) - **Immutable**: you cannot change it (tuple) - **Key-Value**: look up using keys (dict) ## Remember - Data structure choice decides speed and simplicity - Python gives powerful built-in containers - We'll build your concepts so you don’t need to read elsewhere