AlgoInsight

Data Structures & Algorithms

DSA Mastery - Interview Prep, End to End

A 34-module DSA course for engineers prepping for serious technical interviews.

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DSA Mastery - Interview Prep, End to End

A complete 34-module data structures and algorithms course for engineers preparing for serious technical interviews. The course is structured the way patterns actually appear in interviews — not the way textbooks order them — and grouped into sub-modules so you can drill into a specific pattern (Two Pointers — Opposite Direction, DP — 0/1 Knapsack, Binary Search on Answer) instead of skimming a flat topic list.

You start with the interview process itself (how rounds work, how to communicate, how to time-manage), build the mathematical vocabulary for Big O, and pick a language for the rest of the course. Then you work through the core data structures (sorting, arrays, strings, hash tables) and the pattern-based sections that most problems collapse into (two pointers, sliding window, prefix sum, kadane's, matrix).

From there, the course moves into linked structures (linked lists, stacks, queues), recursion and backtracking, binary search, and the tree family (binary tree, BST, tries, heaps, intervals). The deeper second half covers design problems, greedy algorithms, graphs (76 topics — every graph algorithm you'll need), and dynamic programming (91 topics, taxonomised by class). The advanced module closes with senior-level material — KMP, suffix arrays, segment trees with lazy propagation, line sweep.

The course also ships with three companion modules:

The topics that mirror our free practice problems stay free as previews — sample the course voice and depth before deciding. The full course is a one-off purchase. Code samples use Java as the primary language.

What you will learn

Prerequisites

Course outline

Module 1: Welcome

2 topics. Course orientation.

Module 2: Coding Interview Strategy

5 topics. Meta-content about the interview process itself, not algorithms.

Module 3: Big O Notation

5 topics. The mathematical vocabulary for talking about algorithm efficiency.

Module 4: Programming Fundamentals

6 topics. Language-specific setup for learners coming from different stacks.

Module 5: Sorting Algorithms

10 topics. The foundational comparison and non-comparison sorts.

Module 6: Arrays

19 topics. The most-used data structure in interviews, grouped by problem pattern.

Module 7: Strings

18 topics. String manipulation, the second-most common interview surface.

Module 8: Bit Manipulation

13 topics. Working with binary representation directly.

Module 9: Hash Tables

22 topics. The single most powerful data structure for interview problems.

Module 10: Two Pointers

17 topics. A core technique that turns O(n²) brute force into O(n) solutions.

Module 11: Prefix Sum

13 topics. Precomputed cumulative sums for range-query problems.

Module 12: Sliding Window

22 topics. The technique for problems involving contiguous subarrays or substrings.

Module 13: Kadane's Algorithm

8 topics. The classic technique for maximum-subarray problems.

Module 14: Matrix

9 topics. 2D-grid problems.

Module 15: Linked List

28 topics. Pointer-based linear data structures.

Module 16: Stacks

37 topics. The LIFO data structure and its specialized variants.

Module 17: Queues

17 topics. FIFO data structures and the monotonic queue technique.

Module 18: Sorting (Advanced Applications)

11 topics. Sorting as a tool for solving other problems.

Module 19: Recursion & Backtracking

23 topics. The mental model for problems with branching choices.

Module 20: Divide and Conquer

5 topics. The divide-conquer-combine pattern.

Module 21: Binary Search

25 topics. Search in O(log n), and the broader pattern of searching on monotonic functions.

Module 22: Binary Tree

47 topics. Tree data structures organized by traversal pattern.

Module 23: BST / Ordered Set

18 topics. Tree-based ordered structures.

Module 24: Tries

15 topics. Prefix-tree data structure for string-search problems.

Module 25: Heaps

19 topics. Priority queues and the "K-something" pattern.

Module 26: Intervals

17 topics. Range-based problems and the merge/insert patterns.

Module 27: Data Structure Design

22 topics. Implementing the data structures themselves.

Module 28: Greedy

19 topics. The "make the locally optimal choice" pattern.

Module 29: Graphs

76 topics. The largest section, covering traversal, shortest path, MST, and topological sort.

Module 30: Dynamic Programming

91 topics. The largest single section. The "store-and-reuse" pattern, grouped by problem shape.

Module 31: Maths / Geometry

18 topics. Number theory, modular arithmetic, and 2D geometry.

Module 32: Advanced Topics

17 topics. Senior-level techniques that show up in harder interviews.

Module 33: Pattern Guides

Fifteen cross-module decision frameworks. Each guide answers a single question: "given a problem, which technique do I reach for first?" — they collect signals from across the course into 30-second classifiers.

Module 34: Reference & Study Plan

The companion library for the course — priority curation with 1, 2, 4, and 8-week study plans, a LeetCode reverse index, dynamic-programming and graph-algorithm synthesis, the complexity appendix, comparative tables, and the glossary.

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