TOOLKIT

Core data structures and algorithmic techniques you'll use across every week. Each topic is referenced directly from the problem walkthroughs - come here when you need to learn or revise a concept, then mark it complete.

Core concepts and data structures for solving problems

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Recursion
A function that calls itself to break a problem into smaller pieces. The foundation of tree traversal and divide-and-conquer.
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Dynamic Programming
Solve overlapping subproblems once and cache the results. Top-down memoisation turns slow recursion into efficient solutions.
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Bit Masking
Use integer bits as compact sets. Check membership, union, and intersection with single bitwise operations.
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Using Dictionaries
Key-value storage with instant lookups. Map keys to values, store relationships, enable O(1) access.
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Using Sets
Unique element storage with instant membership checks. No duplicates, O(1) add/remove/lookup.
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Using Stacks
Last-in-first-out structure. Push to add, pop to remove. Enables reversal and nested processing.
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Using Trees
Recursive node structure with left and right children. Traverse with DFS or BFS. Solve by breaking into subtrees.
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Searching and Sorting
Merge, mergesort, heapsort, binary search. Sort by custom keys, merge intervals, and select from weighted distributions.
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Techniques and patterns for solving common problems

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Counting Frequencies
Count how many times each element appears. Detect duplicates, find most common, compare distributions.
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Reversing Order
Walk two pointers from opposite ends. Swap elements, check palindromes, partition in-place.
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Matching Brackets
Validate nested structures using a stack. Push openers, pop and match closers, check balance.
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