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Data Structures and Algorithms

What is Data Structures and Algorithms?

Data Structures and Algorithms is the study of how to organise data in memory and the step-by-step procedures that operate on it. A data structure decides what an operation costs; an algorithm decides how many operations you need. Together they determine whether a program finishes in milliseconds or never finishes at all.

Most interview preparation is a list of problems to grind through. That approach does not transfer, because the next problem is never quite the one you memorised. This guide is organised around the structures and the patterns instead — the reusable machinery that lets you attack a problem you have never seen.

Complexity analysis

The measurement tools everything else depends on. If you cannot say what a piece of code costs, every later decision is guesswork.

  • Big-O, Big-Theta and Big-Omega
  • Time complexity of loops and recursion
  • Space complexity and auxiliary space
  • Amortised analysis
  • Best, average and worst case

Articles on complexity analysis

Arrays, strings and hashing

The structures behind the majority of interview questions, and the ones most engineers think they already understand.

  • Dynamic arrays and amortised growth
  • Two pointers
  • Sliding window, fixed and variable
  • Prefix sums and difference arrays
  • Hash maps, collisions and load factor
  • Hash sets and frequency counting

Articles on arrays, strings and hashing

Linked structures

Pointer manipulation, and the problems that only it can solve cleanly.

  • Singly, doubly and circular linked lists
  • Fast and slow pointers
  • Cycle detection
  • Stacks and monotonic stacks
  • Queues, deques and circular buffers
  • LRU cache implementation

Trees and heaps

Hierarchical data and priority-based access.

  • Binary tree traversals, recursive and iterative
  • Binary search trees and their degenerate case
  • Balanced trees: AVL and red-black
  • Heaps and priority queues
  • Top-K and streaming median
  • Tries and prefix search
  • Segment trees and Fenwick trees

Searching and sorting

Binary search is far more general than most engineers realise, and that generality is what interviews test.

  • Binary search on a sorted array
  • Binary search on the answer space
  • Merge sort, quick sort and heap sort compared
  • Quickselect and order statistics
  • Counting sort and radix sort
  • Stability and custom comparators

Recursion and backtracking

Problems that define themselves in terms of smaller versions of themselves.

  • The call stack and recursion depth
  • Subsets, permutations and combinations
  • Constraint satisfaction and N-Queens
  • Pruning the search space
  • Converting recursion to iteration

Graphs

The structure that models more real problems than any other.

  • Adjacency list vs adjacency matrix
  • BFS and DFS, and when each is correct
  • Topological sort and cycle detection
  • Union-Find with path compression
  • Dijkstra, Bellman-Ford and Floyd-Warshall
  • Minimum spanning trees

Articles on graphs

Dynamic programming

Taught as a method rather than a bag of tricks: define the state, find the transition, decide the evaluation order.

  • Memoisation vs tabulation
  • Identifying overlapping subproblems
  • 1D and 2D state spaces
  • Knapsack family
  • Longest common subsequence family
  • DP on trees and bitmask DP
  • Space optimisation

Learn all of this in one structured course

A structured, pattern-first course that takes you from the fundamentals of complexity analysis to advanced graph and dynamic programming problems. You learn the reasoning that generalises, not a list of memorised solutions.

View the DSA course

Data Structures and Algorithms: frequently asked questions

In what order should I learn data structures and algorithms?
Start with complexity analysis, then arrays, strings and hashing, then linked structures, then trees and heaps. Cover searching and sorting next, then recursion and backtracking, then graphs, and finish with dynamic programming. Each stage assumes the previous one, and skipping ahead to dynamic programming without solid recursion is the most common reason people get stuck.
How long does it take to get good at DSA?
With consistent practice of roughly eight hours a week, most working engineers reach interview readiness in three to four months. The variable is not intelligence but whether you practise retrieval — solving problems from scratch — rather than re-reading solutions.
Which programming language should I use?
Use whichever language you are fastest in, provided it has a decent standard library of collections. Python, Java, C++, Go and JavaScript are all accepted in interviews. The language matters far less than fluency with its built-in data structures.
Do I need to memorise algorithms?
No. You need to be able to derive them. Memorising Dijkstra's implementation is far less useful than understanding that it is BFS with a priority queue, because the second lets you reconstruct it and adapt it.

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