Data Structures & Algorithms

The mental models and habits that underpin everything else: how to read a problem, reason about correctness, and pick a language.

How to measure and communicate the cost of an algorithm in time and space — the language every interviewer speaks.

The most-tested data structures. Master contiguous memory, indexing, and the manipulation idioms that appear in nearly every interview.

Constant-time lookup is the single biggest optimization lever in interviews. Understand how hashing works and when it breaks.

Two of the highest-yield patterns for linear-time array and string solutions. Recognizing them instantly separates strong candidates.

Pointer-based linear structures that teach reference manipulation — and host a family of classic interview tricks.

Restricted-access linear structures that model order and recursion. The basis for monotonic-stack and BFS techniques.

The thinking style behind trees, graphs, and combinatorics. Master the call stack, base cases, and systematic search.

Ordering data underpins binary search, greedy, and interval problems. Know the classics cold and when to apply each.

Binary search is the most under-recognized pattern in interviews. Learn the template and the art of searching on the answer.

Hierarchical structures that dominate the mid-to-hard interview range. Traversals and BST invariants are non-negotiable knowledge.

The go-to structure for top-k, scheduling, and streaming problems. Know heap mechanics and the patterns they unlock.

The richest topic in senior interviews. Master representations, traversals, and the algorithm zoo built on top of them.

Specialized structures that show up in hard problems and system-flavored questions. Strong differentiators at senior levels.

Locally optimal choices that sometimes yield global optimums. Learn to spot when greedy works — and prove it.

The make-or-break topic for hard interviews. Build intuition for states, transitions, and the canonical DP families.

Low-level tricks and number theory that produce elegant O(1) solutions and occasionally make-or-break a problem.

Frequently tested standalone topics: interval reasoning and translating requirements into clean data-structure-backed designs.

Pattern matching and string-specific algorithms that surface in harder rounds and at companies with heavy text workloads.

The synthesis layer: recognizing which pattern a problem hides, and the communication and testing habits that win offers.