Step 2
Data Structures
How you store data decides how fast your program runs. The common ground of interviews and every later course.
Know these firstIntroduction to Programming
- Arrays and linked lists
- Stacks and queues
- Hash tables
- Trees and BSTs
- Heaps
- Graphs
- Big-O
Why it matters
You can solve the same problem with a list or a hash table; one takes a second, the other an hour. Database indexes are trees, the OS scheduler uses queues, map apps are graphs. This course is the shared language of interviews and every course that follows.
What to know
- Arrays and linked lists: where insertion, deletion and access are cheap.
- Stacks and queues; examples like undo and a print queue.
- Hash tables: how collisions are resolved and why lookups are O(1) on average.
- Trees, binary search trees, the idea of balanced trees (AVL, red-black).
- Heaps and priority queues.
- Graph representations: adjacency matrix and adjacency list.
- Complexity analysis with Big-O.
Practice and projects
- Implement every structure from scratch at least once; use the library only after that.
- Watch the steps on visualization sites such as VisuAlgo.
- Start with LeetCode Easy problems; solve 5-10 for each structure.
Common mistakes
- Memorizing structures without studying which one fits which problem.
- Treating Big-O as a formula; learn to estimate it by looking at the loops in your code.
- Coding pointer-based structures (linked lists, trees) without drawing them on paper first.