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.
Linked Lists and Trees
Dynamic and pointer based data structures including linked lists and tree structures commonly tested in interviews. For linked lists cover node based representation, traversal, insertion at head and tail, deletion, searching, reversing a list, detecting cycles, and tradeoffs versus array based lists. For trees cover basic concepts such as binary trees and binary search trees, tree node representation, insertion and deletion in search trees, recursion patterns, and traversal algorithms including depth first search with in order pre order and post order variants and breadth first search. Also include problem solving patterns such as recursion and iterative stack or queue based approaches, analysis of time and space complexity in plain terms, and common interview tasks such as lowest common ancestor, tree balancing awareness, and converting between representations. Practice includes implementing algorithms, writing traversal routines, and reasoning about correctness and performance.
Designing Data Structures
Focuses on designing and implementing common data structures and simple caches such as a least recently used cache, a least frequently used cache, or a linked hash map. Candidates should explain the public interface they would expose, the time and space complexity of each operation, memory overhead, and trade offs between different eviction and lookup strategies. The assessment includes reasoning about using language standard libraries versus custom implementations, concurrency and thread safety considerations, and approaches to testing and validating correctness under edge cases.
Data Structures and Complexity
Comprehensive coverage of fundamental data structures, their operations, implementation trade offs, and algorithmic uses. Candidates should know arrays and strings including dynamic array amortized behavior and memory layout differences, linked lists, stacks, queues, hash tables and collision handling, sets, trees including binary search trees and balanced trees, tries, heaps as priority queues, and graph representations such as adjacency lists and adjacency matrices. Understand typical operations and costs for access, insertion, deletion, lookup, and traversal and be able to analyze asymptotic time and auxiliary space complexity using Big O notation including constant, logarithmic, linear, linearithmic, quadratic, and exponential classes as well as average case, worst case, and amortized behaviors. Be able to read code or pseudocode and derive time and space complexity, identify performance bottlenecks, and propose alternative data structures or algorithmic approaches to improve performance. Know common algorithmic patterns that interact with these structures such as traversal strategies, searching and sorting, two pointer and sliding window techniques, divide and conquer, recursion, dynamic programming, greedy methods, and priority processing, and when to combine structures for efficiency for example using a heap with a hash map for index tracking. Implementation focused skills include writing or partially implementing core operations, discussing language specific considerations such as contiguous versus non contiguous memory and pointer or manual memory management when applicable, and explaining space time trade offs and cache or memory behavior. Interview expectations vary by level from selecting and implementing appropriate structures for routine problems at junior levels to optimizing naive solutions, designing custom structures for constraints, and reasoning about amortized, average case, and concurrency implications at senior levels.
Binary Trees and Binary Search Trees
Focuses on tree data structures, specifically binary trees and binary search trees. Candidates should understand node relationships, common traversals including in order, pre order, post order, and level order, and be able to implement traversals both recursively and iteratively. Cover binary search tree properties and operations including search, insertion, deletion, validation of binary search tree property, and finding the lowest common ancestor. Include problems on tree paths, height and balance calculations, serialization and deserialization, checking and restoring balance at a high level, and use cases in system design. Emphasize complexity analysis, recursion versus iterative solutions using stacks or queues, and handling edge cases such as duplicate keys and degenerate trees.
Graph Algorithms and Traversal
Covers fundamental representations, traversal techniques, and classical algorithms for graph structured data. Candidates should understand graph representations such as adjacency list and adjacency matrix and the tradeoffs in time and space for each. Core traversal skills include implementing and reasoning about breadth first search and depth first search for reachability, traversal order, and unweighted shortest path discovery, as well as tree traversal variants and their relationship to graph traversals. Algorithmic topics include cycle detection, topological sorting for directed acyclic graphs, connected components and strongly connected components, and shortest path and pathfinding algorithms for weighted graphs including Dijkstra algorithm and Bellman Ford algorithm with discussion of negative weights and appropriate use cases. Candidates should be able to analyze time and space complexity, choose appropriate auxiliary data structures such as queues, stacks, priority queues, and union find, handle directed versus undirected and weighted versus unweighted graphs, discuss implementation details and trade offs, and explain practical applications such as dependency resolution, scheduling, pathfinding, connectivity queries, and roles of graph algorithms in system design and data processing.
Trees and Graphs
Comprehensive knowledge of tree and graph data structures and algorithms commonly tested in coding interviews. Candidates should understand representations such as adjacency list and adjacency matrix and when to use each, and tree representations including n ary trees and binary search trees. Expect to implement and reason about traversals including depth first search and breadth first search, tree traversals such as pre order in order and post order, and level order traversal. Cover algorithms including topological sorting for directed acyclic graphs, cycle detection, connected components, shortest path algorithms such as breadth first search for unweighted graphs, Dijkstra for nonnegative weights, and Bellman Ford for graphs with negative edges, and minimum spanning tree algorithms such as Kruskal and Prim. Include disjoint set union find for connectivity and for use with Kruskal, lowest common ancestor techniques and implementations, tree dynamic programming problems, serialization and deserialization, reconstruction from traversals, balancing and validation checks for binary search trees and balanced tree concepts, diameter and path sum problems, and common interview patterns such as path finding dependency resolution and structural transformation. Emphasize implementation details and common pitfalls including correct use of visited tracking recursion depth edge cases and disconnected components, and practice articulating time and space complexity tradeoffs and algorithm selection under different constraints.
Problem Decomposition
Break complex problems into smaller, manageable subproblems and solution components. Demonstrate how to identify the root problem, extract core patterns, choose appropriate approaches for each subproblem, sequence work, and integrate partial solutions into a coherent whole. For technical roles this includes recognizing algorithmic patterns, scaling considerations, edge cases, and trade offs. For non technical transformation work it includes logical framing, hypothesis driven decomposition, and measurable success criteria for each subcomponent.
Stacks and Queues
Implementation and usage of stack and queue data structures. Problems involving balanced parentheses, expression evaluation, queue operations, or sliding window maximums. Understanding when to use each structure and their time/space trade-offs.
Recursion and Dynamic Programming
Covers recursive problem solving and dynamic programming as core algorithmic techniques. For recursion, understand how functions call themselves, base and recursive cases, the call stack, common patterns such as tree and graph traversals, backtracking, permutations, and detecting and avoiding infinite recursion. For dynamic programming, understand when to apply optimization via memoization and bottom up approaches, recognize optimal substructure and overlapping subproblems, convert naive recursive solutions into memoized or tabulated solutions, and analyze time and space complexity tradeoffs. Familiarity with classic examples such as Fibonacci, longest common subsequence, knapsack, coin change, and path counting is expected. At more senior levels, be able to discuss performance considerations, space optimization, and how DP principles can map onto real systems such as caching strategies, state management, and optimization of workflows or database query plans.