How to Study Data Structures and Algorithms: A Practical 8-Week Plan
Most people who "study DSA" do one of two things: they read theory until their eyes glaze over, or they grind random practice problems and forget the patterns a week later. Neither builds the skill interviews and exams actually test, which is recognising which structure or technique a problem needs and implementing it without help.
This guide is a plan that balances the two. It assumes you can already write basic code in one language and have about an hour a day.
The three things you are really learning
- What each structure is good and bad at. An array gives constant-time access by index but slow inserts in the middle. A hash table gives fast average lookups but no ordering. A heap always hands you the smallest (or largest) item quickly. If you know the costs, you can reason your way to a solution.
- A small set of recurring techniques. Two pointers, sliding window, binary search, recursion and backtracking, BFS and DFS, dynamic programming and greedy choices cover a very large share of the problems you will meet.
- Complexity analysis. You should be able to say, for anything you write, how its time and memory grow with input size, in Big-O terms.
Before you start: pick one language and stick to it
Choose the language you are most fluent in. Python is popular for interviews because it is short to write, but Java, C++, JavaScript, Go or Kotlin are all fine. Switching languages mid-plan doubles the work.
The 8-week plan
| Week | Topics | Practice goal |
|---|---|---|
| 1 | Big-O, arrays, strings, dynamic arrays | Reverse, rotate, de-duplicate; explain the cost of each |
| 2 | Hash tables, two pointers, sliding window | Pair-sum, longest substring without repeats |
| 3 | Linked lists, stacks, queues, deques | Reverse a list, detect a cycle, balanced brackets |
| 4 | Recursion, sorting (merge, quick, heap), binary search | Implement each sort once from memory |
| 5 | Trees: binary, BST, traversals; heaps | Level-order traversal, validate a BST, top-k items |
| 6 | Graphs: representations, BFS, DFS, topological sort | Number of islands, course-schedule ordering |
| 7 | Shortest paths and spanning trees: Dijkstra, Bellman-Ford, Kruskal, Prim; tries | Shortest path on a weighted grid, prefix search |
| 8 | Dynamic programming and review | Climbing stairs, coin change, longest common subsequence |
Advanced structures such as AVL and red-black trees, segment trees and Fenwick trees are worth understanding conceptually, but most interview loops rarely ask you to implement them from scratch. Learn them after the core is solid.
A daily routine that works
- 10 minutes: read or watch one concept. Keep it short.
- 10 minutes: visualise it. Step through the algorithm on paper or with an animation. Watching a merge sort split and merge, or Dijkstra relax edges one at a time, makes the invariant obvious in a way prose rarely does.
- 30 minutes: implement it without looking at a solution, then run it on a few inputs, including edge cases (empty input, one element, duplicates).
- 10 minutes: write down the time and space complexity and one sentence on when you would use it.
The writing step matters more than it seems. Those one-line notes become your revision sheet in week 8.
How to practise problems without wasting time
- Time-box each attempt to 25–30 minutes. If you are stuck, read only the first hint, not the full solution.
- After you solve or read a solution, re-implement it the next day from scratch. If you can't, you didn't learn it.
- Tag each problem by technique (for example "sliding window"). After a few weeks you will see the same handful of tags again and again, and that is the point.
- Mix difficulties. Mostly easy and medium; one hard problem a week is plenty early on.
Common mistakes
- Skipping complexity. A correct but quadratic solution often fails the real question.
- Memorising code instead of invariants. Know why binary search keeps
low <= high, not just the code. - Never running your code. Reading a solution feels like understanding. Running and breaking it is understanding.
- Studying only on a laptop. Short reviews on your phone during a commute keep topics fresh between sessions.
Tools that help
You need three things: a way to read concise explanations, a way to see algorithms run, and a place to execute code. A whiteboard and any online judge cover a lot of it.
Study on your phone: DSA Master - Data & Algorithms (free, by the author of this guide) covers 50+ data structures and algorithms with step-by-step animated visualisations, code examples in several languages with time and space complexity, and a code playground that runs Python, JavaScript, Java, C++, Kotlin, Dart and Go. It also has daily challenges, bookmarks and notes, and works offline.
How you know you're ready
You are in good shape when you can, for a new problem:
- Restate it and ask about edge cases.
- Name a brute-force approach and its complexity.
- Spot the technique that improves it.
- Write working code and test it out loud.
That is the whole interview loop in four steps, and it is also how you learn this material for good.