Learn and practice

Topics

Follow a learning path through each part of the curriculum, from arrays and hash tables to graphs and dynamic programming.

Learn array invariants, in-place work, prefix/suffix products, and matrix traversal through a curated sequence of lessons and exercises.

Practice reasoning about input size, loops, recursion, and auxiliary memory before comparing the efficiency of interview solutions.

Use hash maps and sets to count frequencies, track previously seen values, and trade additional memory for faster lookups.

Practice text traversal, character counts, palindromes, and anagrams while making the character model and input constraints explicit.

Define base cases, trace recursive calls, and understand return values and shared state through progressively harder exercises.

Learn binary-search invariants, sorted-input reasoning, and sorting tradeoffs with targeted interview problems.

Trace pointer updates, preserve reachability, and practice reversing lists and joining chains without losing nodes.

Choose last-in-first-out or first-in-first-out processing for bracket matching, traversal, and state tracking.

Practice recursive contracts, traversal order, binary-search-tree bounds, and ancestor relationships with a structured learning path.

Represent relationships, track visited nodes, choose BFS or DFS, and solve graph and grid interview problems.

Connect subproblems, memoization, tabulation, and state design with worked interview exercises.

Practice promises, closures, array methods, event emitters, debounce, and throttle with guides and coding lessons.