Hash Maps

Hash maps (also called dictionaries or hash tables) are perhaps the most versatile data structure in coding interviews. They provide O(1) access to values by key, making them essential for counting, grouping, caching, and building indexes. When you need to associate data or track relationships, hash maps are your first choice.

The real power of hash maps lies in the patterns they enable: frequency counting to find duplicates or most common elements, complement finding for two-sum problems, grouping for anagrams or organizing data, and caching for memoization. This unit covers these patterns systematically.

What You'll Learn

• Map Basics: How hash maps work, fundamental operations (get, put, remove), time and space complexity, and choosing between maps and other structures. You'll understand when to use maps vs arrays vs sets.

• Map Patterns: The essential patterns including frequency counting, complement/pair finding, prefix sums, grouping/categorization, and using maps as indexes or caches. These patterns solve hundreds of interview problems.

• Map Problems: Classic problems that showcase map patterns, including two-sum and variations, subarray sum equals k, group anagrams, longest consecutive sequence, and character/substring problems.

Why It Matters

Hash maps are the workhorse of interview problems. They appear in array problems, string problems, and as optimization tools for dynamic programming and graph algorithms. Mastering map patterns—especially frequency counting and complement finding—is essential for interview success.