DSA & CS — The Full Map

Data Structures and Algorithms are the backbone of software engineering. Every system, framework, and application you build relies on these fundamentals underneath.

Why it matters:
• Interviews at top companies test this almost exclusively
• Understanding complexity makes you write better production code
• Choosing the right data structure often solves the problem entirely

The mental model:
• Data Structures → how to organize and store data
• Algorithms       → how to process and manipulate that data
• Complexity       → how fast and how much memory does it take

Big-O Notation (always worst case unless stated):
O(1) < O(log n) < O(n) < O(n log n) < O(n²) < O(2ⁿ) < O(n!)

The golden rule: before coding, ask yourself:
1. What data structure fits this problem's access pattern?
2. What's the time/space tradeoff?
3. Can I reduce it to a known problem?

1# Python is the standard for DSA interviews
2# No installs needed — Python stdlib has everything
3
4# Key built-ins you'll use constantly
5from collections import defaultdict, Counter, deque
6from heapq import heappush, heappop, heapify
7import bisect       # binary search on sorted lists
8import functools    # @cache, @lru_cache
9import itertools    # combinations, permutations
10
11# Quick complexity check habit
12# Before writing any solution, ask:
13# Time: O(?), Space: O(?)