Algorithms & Data Structures

Algorithms and data structures are the bedrock of computer science — and the single biggest lever on whether your code scales. The same problem can be solved in a microsecond or grind to a halt depending on which structure stores the data and which algorithm processes it. This is why these topics dominate both real engineering and technical interviews: they're how you reason about efficiency before you ever run the code.

This hub covers the essentials: the core data structures, how to analyze cost with Big-O, the classic algorithm techniques (sorting, searching, recursion, dynamic programming), and graph algorithms.

TL;DR

• A data structure organizes data; an algorithm processes it — choose both to fit the problem.
• Big-O describes how cost scales; avoiding O(n²)/O(2^n) blowups is the main game.
• A right data structure (hash table, heap, balanced tree) often matters more than clever code.
• Master the techniques: sorting, searching, recursion, dynamic programming, graph traversal.

The Cost Hierarchy

Everything here connects back to one question: how does the work grow with input size?

Knowing where your algorithm sits — and how to move it left — is the core skill.

Featured Topics

Core Data Structures

• Core Data Structures — Arrays, stacks, queues, heaps, graphs
• Hash Tables — O(1) average key-value lookup
• Trees — Binary search trees, balancing, traversal
• Linked Lists — Nodes, pointers, and when to use them

Algorithm Techniques

• Sorting Algorithms — Quicksort, mergesort, and tradeoffs
• Searching Algorithms — Linear, binary, and hash-based
• Recursion — Base cases, the call stack, backtracking
• Dynamic Programming — Overlapping subproblems and memoization

Complexity Analysis

• Big-O Notation — Time and space complexity, best/average/worst case

Graph Algorithms

• Graph Algorithms — BFS, DFS, shortest paths, topological sort

How It Fits Together

The data structure you choose determines which algorithms are cheap:

Common Questions

Do I really need this if I just use built-in libraries?

Yes. You rarely implement a hash table or sort, but you constantly choose between them — and the wrong choice turns an O(n) task into O(n²). Knowing that a set gives O(1) membership, a heap gives cheap top-k, or a balanced tree supports range queries is what lets you write code that scales. The libraries handle the implementation; you still have to pick the right tool. See Big-O Notation.

How do I get better at algorithm problems for interviews?

Learn the core toolkit first — data structures, Big-O, recursion, sorting/searching, graph traversal, and dynamic programming — then practice recognizing which applies. Most problems are variations on these patterns. Focus on recognizing the pattern (is this a graph? a DP? a two-pointer scan?) rather than memorizing solutions. See Career & Interviews.

What's the most common performance mistake?

The hidden quadratic: a linear-looking loop that does an O(n) operation inside it (a list membership check, a re-sort, a nested scan), making the whole thing O(n²). It works fine on small test data and falls over in production. The fix is almost always a better data structure — build a hash set once for O(1) lookups instead of scanning a list repeatedly. See Performance Optimization.

Which matters more, the algorithm or the data structure?

Usually the data structure. The same algorithm becomes fast or slow depending on whether its operations are O(1), O(log n), or O(n). Picking a hash table, heap, or balanced tree that makes your hot-path operation cheap typically yields a bigger win than micro-optimizing the algorithm. Choose the structure to fit your access pattern first; the efficient algorithm often follows naturally.

Related Hubs

• Programming Languages — Implementing these concepts
• Career & Interviews — Where these problems show up
• Performance & Optimization — Applying complexity analysis