DSA Tutorials
Data structures, algorithms, and problem-solving patterns for coding interviews. Each tutorial includes code examples, complexity analysis, and common mistakes.
Arrays and Strings
Time complexity, two-pointer technique, common operations.
6 min read →
Hash Maps
Lookup tables, frequency counting, anagram patterns.
6 min read →
Two Pointers
Opposite ends, same direction, fast/slow pointer.
6 min read →
Sliding Window
Fixed and variable window, max/min subarray.
6 min read →
Stack and Queue
LIFO/FIFO, monotonic stack, next greater element.
6 min read →
Linked Lists
Singly/doubly linked lists, cycle detection, fast-slow pointers.
6 min read →
Binary Search
Sorted arrays, search space reduction, rotated arrays.
7 min read →
Trees and Graphs
Binary trees, BST, traversals, graph representations.
7 min read →
BFS vs DFS
Graph traversal, shortest path, cycle detection.
6 min read →
Heaps
Min/max heap, heapify, k-smallest, priority queues.
8 min read →
Sorting Algorithms
Quick sort, merge sort, time complexity comparison.
6 min read →
Intervals
Merge, insert, overlap, meeting rooms problems.
7 min read →
Greedy Algorithms
Activity selection, intervals & scheduling, when greedy works.
7 min read →
What this round tests
Coding interviews are not memory tests. Nobody asks you to recite the definition of a B-tree, and nobody expects you to produce a LeetCode hard from memory. What interviewers actually score is whether you can take an unfamiliar problem, pick a sensible data structure, and argue your way to a solution while saying your reasoning out loud. The same problem can be answered in five different valid ways, and what separates a hire from a reject is usually the clarity of the process rather than the final code.
That is why this section is organized by pattern instead of by problem. Each tutorial covers one data structure or technique, shows the problems it unlocks, and explains when the pattern does not apply. Once you recognize that a new prompt is a sliding-window problem, the implementation becomes routine. Once you misrecognize it, you will spend twenty minutes writing code that was never going to run in time.
Every tutorial here is free and written for interview conditions rather than for a data structures course. You will find complexity analysis stated explicitly, the common wrong approaches called out before you make them, and worked examples that show the reasoning an interviewer wants to hear. InterviewSkool pairs the theory with a practice problem set and an AI mock interviewer, so you can move from recognizing a pattern to executing it under time pressure.
Practice
Read a tutorial, then try it out loud with Alex, our AI interviewer.
Start a mock interview →Frequently asked questions
What is DSA and why do tech interviews test it?
DSA means data structures and algorithms: the ways you store data and the step-by-step procedures you use to solve problems. Tech interviews test it because it is a fast proxy for how you approach an unfamiliar problem. Interviewers rarely ask a question you have seen before, so they are scoring your process: choosing a data structure, reasoning about time and space complexity, and narrowing the search space without brute force.
Which data structures should I learn first for coding interviews?
Start with arrays, hash maps, stacks, and queues, then add linked lists, trees, and heaps. Graphs and tries come after that. This order matters because the later structures are built from the earlier ones, so every problem that uses a graph still assumes you are comfortable with a hash map and a queue. Most interview questions are variations on these core structures rather than genuinely new ones.
How many DSA problems should I solve before an interview?
Aim for 200 to 300 quality problems rather than a thousand random ones. The goal is pattern coverage, not volume: work through 10 to 15 problems per pattern until you can recognize the pattern without hints, then move on. Solving the same problem twice a week later is more valuable than solving five unfamiliar ones, because interview questions are recycled and rephrased constantly.
When should I use dynamic programming?
Reach for dynamic programming when a problem has overlapping subproblems and an optimal substructure, which in practice means overlapping inputs and a choice of decisions whose order does not matter. The tell is usually a naive solution that is far too slow: if a recursion tree has repeated states or the same subarray is recomputed, memoize it. If no state repeats, you almost certainly do not need DP.
How do I analyze time complexity in an interview?
Count how many times the code touches the data, not how many lines you wrote. A single loop over n items is O(n), a nested loop where the inner range depends on the outer is O(n squared), and a loop that halves its range each pass is O(log n). Say the complexity out loud as you design the solution, because interviewers are grading the reasoning, not just the final answer.