Technical Fundamentals & Core Skills Topics
Core technical concepts including algorithms, data structures, statistics, cryptography, and hardware-software integration. Covers foundational knowledge required for technical roles and advanced technical depth.
Sorting and Searching Algorithms
Comparison and non-comparison sorting (quicksort with its pivot and partition choices and hybrid cutoffs, mergesort, heapsort as a sort, insertion/selection/bubble, counting/radix/bucket), their stability, in-place behavior, stack depth and lower bounds, and choosing a sort from input constraints, including near-sorted data, library sorts (Timsort, introsort, language defaults), comparator design and expensive comparisons or copies. Also selection (quickselect, median-of-medians, nth_element), external merge sorting of data larger than memory (run generation and merge passes), and binary search with its variants: first/last occurrence, lower/upper bound, rotated arrays with or without duplicates, peak and mountain arrays, sorted matrices, unbounded search, loop-invariant correctness and off-by-one pitfalls, and binary search on an answer space (discrete or continuous, including k-th smallest by value). Includes divide-and-conquer reasoning for sorts (merge step, merging sorted arrays, median of two sorted arrays, inversion counting). Excludes heap data-structure mechanics and heap-based top-k and k-way merging, trees and tries, general graph search, distributed data-processing systems, approximate nearest-neighbor and streaming-sketch algorithms, and abstract Big-O comparison.
Algorithmic Problem-Solving and Data Structure Selection
The higher-order meta-skill of attacking an unfamiliar problem: recognizing problem archetypes and mapping them to known techniques, decomposing under constraints, and choosing, composing, or designing the right data structures to meet specified operation costs (LRU cache, min-stack, ordered maps, disjoint-set/union-find). Covers reasoning about trade-offs between competing structures and approaches, working through medium-to-hard problems methodically, handling problem variations, and communicating an approach before coding. The connective-tissue topic that ties the individual structure and algorithm topics together, rather than any single structure or algorithm.
Linked Lists, Stacks, and Queues
Pointer-based linear structures and the classic problems built on them: singly, doubly and circular linked lists (reversal whole, partial and in groups of k, cycle detection and cycle start with fast/slow pointers, intersection, middle and nth-from-end, merging two or k sorted lists, sorting a list, reordering, palindrome check, rotation, partitioning, duplicate removal, deep copy with random pointers, flattening child lists, sentinel nodes), implementing lists in C, C++ and Rust with attention to pointer handling, ownership and cleanup, array versus linked-list trade-offs including cache locality, stacks, queues and deques (array, linked and circular-buffer implementations, ring-buffer full versus empty, queue from two stacks, min stack, round-robin scheduling), monotonic stacks and queues (next greater element, largest rectangle in a histogram, sliding-window maximum), stack-based expression and bracket evaluation (balanced brackets, Reverse Polish Notation, infix to postfix, calculators), and structure-level builds such as an LRU cache from a hash map plus doubly linked list, undo/redo history, and variants like persistent or unrolled lists. Foundational for coding interviews and for understanding how higher-level structures are built. Concurrent and lock-free queues, memory allocators, general graph traversal, heaps and cache-eviction system design are covered elsewhere.
Time and Space Complexity Analysis
Reasoning about algorithmic efficiency: Big-O/Theta/Omega notation, amortized analysis, recurrence solving, and the time-versus-space trade-off. Covers deriving bounds from code, comparing candidate approaches, and communicating complexity clearly under interview pressure. The analytical layer applied across every algorithm topic.
Heaps and Priority Queues
Binary heaps and priority queues as data structures: min-heap and max-heap, array layout, sift-up and sift-down, push/pop/peek costs, build-heap (heapify) in O(n), and indexed heaps with decrease-key versus lazy deletion of stale entries. Covers the problems where a heap is the intended tool: top-K and kth largest or smallest selection (including over a one-pass stream and under inserts and deletes), sorting an array whose elements are at most k places out of order with a window heap, the two-heap pattern for running and sliding-window medians (invariants, rebalancing, lazy deletion, testing, and extensions to percentiles, weighted medians, custom orderings and merging structures), k-way merge of sorted lists, arrays, files or streams with bounded memory, scheduling by priority with cancellation and reprioritisation, stable priority queues with tie-breaking, and implementing a heap or priority queue with a language library such as heapq or PriorityQueue (including max-heap behaviour from a min-heap). Boundary: graph shortest-path algorithms, approximate quantile sketches, production streaming-system design, and heapsort and quickselect as sorting and selection algorithms are covered elsewhere.
Version Control and Developer Tooling
The everyday toolchain of software work: version control with Git (branching, merging, rebasing, conflict resolution, using git bisect to find a regression), command-line and shell proficiency for day-to-day navigation, log inspection, and troubleshooting, IDE and editor workflows, build systems and package/dependency management (npm, Maven, pip, Gradle, CocoaPods, and embedded/cross-compilation toolchains), and the growing practice of AI-assisted coding: using, reviewing, and verifying AI-generated code and tests. Deliberately generic across languages and stacks; language- and domain-specific frameworks live in their own categories. This topic covers a developer's individual command of these tools, not: writing durable shell automation and glue scripts (Shell Scripting and Automation owns that), producing, versioning, and publishing build artifacts or container images (Build Automation and Artifact Management owns that), release cadence and change governance (Release Management and Change Control owns that), or diagnosing a live production incident end to end (Performance Troubleshooting and Incident Response and the Observability topics own that).
Hashing and Hash Tables
How hash tables and hash-based structures work internally, and how to reason about their performance and correctness. Covers hash function properties (determinism, uniform distribution, speed, avalanche effect), cryptographic versus non-cryptographic hash choices, collision resolution (separate chaining, open addressing: linear probing, quadratic probing, double hashing, Robin Hood hashing, cuckoo hashing), load factor and amortized-cost resizing, and what makes an object hashable (the __hash__/__eq__ contract, immutability, custom composite keys). Covers hash-map-backed cache design (LRU and LFU eviction, TTL) and thread-safe concurrent hash maps (lock striping, CAS-based updates, safe concurrent resizing). Also covers hash-based structures beyond arrays and strings: consistent hashing for distributed routing and sharding, hash joins, hash-flooding and algorithmic-complexity security attacks and their mitigations, and probabilistic membership/cardinality structures such as Bloom filters, Cuckoo filters, Count-Min Sketch, and HyperLogLog. Excludes using a hash map purely as an optimization trick inside an array or string problem (two-sum, group anagrams, longest substring without repeating characters); that pattern belongs to Arrays, Strings, and Hashing. This topic is about the hash table itself: how it is built, how it fails under skewed or adversarial input, and how it scales.
Code Review and Working with Existing Codebases
Reviewing others' code and navigating unfamiliar systems: giving and receiving actionable review feedback, spotting correctness and design issues, and reading and understanding large or legacy codebases before changing them. Covers collaborative coding norms, incremental change in shared repositories, and verifying changes against existing behavior. The team-facing side of day-to-day engineering.
Graphs and Graph Algorithms
Graph representations (adjacency list and adjacency matrix) and the traversal algorithms applied to general, non-tree structures: BFS, DFS, topological sort (Kahn's algorithm and DFS-based), shortest paths (Dijkstra, Bellman-Ford, A*), minimum spanning trees, cycle detection, connected components, and union-find. Covers modeling a problem as a graph even when the underlying data is not obviously graph-shaped, such as state-space search, an implicit graph over strings or grid cells (for example Word Ladder), or a task-dependency graph, and implementing these traversals with a hash map or hash set as the storage vehicle (adjacency map, visited set, memoization table), not the subject being tested. The graded skill is traversal, ordering, connectivity, or shortest-path reasoning over nodes and edges. This topic does not own: traversal, reconstruction, or serialization of a single-rooted binary tree (preorder, inorder, postorder, or level-order implementation, rebuilding a tree from traversal arrays, lowest common ancestor, binary search tree validation), which belongs to binary trees and binary search trees even though a tree is technically a graph; hash table internals such as hash function design, collision resolution, and load factor and resizing, which belong to hashing and hash tables; and deriving or comparing algorithmic complexity across graph algorithms without implementing them, such as comparing the time complexity of BFS, DFS, Dijkstra, and A*, which belongs to time and space complexity analysis. One of the highest-signal areas in senior coding interviews.