About These Exercises
These exercises have you implement and apply data structures to solve algorithmic problems with the right performance.
These practice problems put data structures to work. You will implement and use arrays, linked lists, stacks, and queues, then apply hash tables with collision handling to build fast lookups. Tree exercises cover binary search tree insertion and traversal, heap based priority queues, and tries for prefix problems, while balanced trees such as AVL and red black appear where guarantees matter. Graph problems use adjacency lists to model relationships and support traversal.
Each exercise asks you to choose a structure that gives the operations you need cheaply, implement it correctly, and analyze the Big O cost. The goal is turning structural knowledge into working, efficient code. Knowing data structures in theory is not enough, since real problems reward the ability to reach for the right one and implement it under time pressure.
Hash tables make caches and lookups fast, heaps drive scheduling and shortest path algorithms, and graphs model networks and dependencies. Coding interviews at technology companies center on exactly this kind of applied structural reasoning, and everyday engineering depends on it whenever data grows large enough that naive approaches stall.
Practicing implementation builds the fluency to write code that is both correct and efficient, which is what distinguishes strong engineers from those who only recognize structures on paper. To prepare, code each structure from scratch and test it on edge cases like empty inputs, duplicates, and large sizes. Practice narrating the time and space complexity of your solution as you write it, since interviewers expect that analysis.
When stuck, ask which operations dominate and which structure makes them cheapest. A strong score indicates that you can move from a problem statement to a correct, efficient implementation and justify your choice with complexity reasoning. That applied skill is precisely what coding interviews reward and what real projects require when performance and scalability genuinely affect the user experience.
What You Will Practice
Implementing Structures
Build linked lists, stacks, queues, and hash tables from scratch, handling edge cases and collisions correctly under test.
Trees and Heaps
Insert and traverse binary search trees, maintain heap based priority queues, and use tries for efficient prefix and dictionary problems.
Graph Problems
Model relationships with adjacency lists and apply traversal to connectivity, pathfinding, and dependency ordering tasks.
Complexity Analysis
State Big O time and space costs for your solutions and reason about worst case behavior as inputs grow large.