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 sorts (quicksort, mergesort, heapsort, counting/radix), their stability and complexity, and binary search with its many variants. Covers divide-and-conquer reasoning, searching in rotated or implicit spaces, and choosing an algorithm from input constraints. A staple of both fundamentals screens and optimization discussions.
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: singly and doubly linked lists, stacks, queues, and deques. Covers pointer manipulation, cycle detection, reversal, and using LIFO/FIFO ordering to model traversal, undo, and scheduling problems. Foundational for both interview problems and understanding how higher-level structures are built.
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 for maintaining ordered access to the smallest or largest elements. Covers heapify, top-K selection, streaming medians via two-heap patterns, and merge-of-sorted-streams problems. Appears whenever a problem needs efficient repeated access to extremes without full sorting.
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.
Trees and Binary Search Trees
Hierarchical structures: binary trees, binary search trees, balanced trees, and tries. Covers traversal orders (in/pre/post-order, level-order), insertion and deletion invariants, and using tree properties to achieve logarithmic search. A core mid-difficulty interview area and the basis for many indexing and lookup systems.
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.
Recursion and Backtracking
Recursive decomposition, base/recursive-case design, and backtracking search over combinatorial spaces (permutations, subsets, constraint satisfaction, N-queens style problems). Covers recursion-tree reasoning, pruning, and converting recursion to iteration. The conceptual bridge into dynamic programming and search.