DSA & Deep Problem Solving
Go beyond syntax. Build the algorithmic thinking and problem-solving depth that top tech companies test in interviews — and that makes you a better engineer every day.
Why DSA & Problem Solving?
DSA interviews don't just test whether you know the algorithm — they test whether you can reason through a problem you've never seen, explain your thinking, and defend every trade-off.
of MAANG/FAANG technical interviews include at least two DSA rounds — and the same is true of most competitive product company processes.
code quality from engineers who understand complexity — they make different architecture choices from the start, not just at the interview.
Google, Amazon, Flipkart, Atlassian, Meesho, PhonePe, Zepto, and top-tier startups.
Want the full programme details?
Download our comprehensive brochure (PDF)
Learning Outcomes
By completing this programme, you will be able to:
- Solve medium and hard LeetCode problems systematically — not by memorising solutions.
- Analyse time and space complexity of any algorithm with confidence.
- Apply the right data structure and pattern to any problem you've never seen before.
- Explain your approach clearly in interviews — narrating your thought process as you code.
- Handle strings, trees, graphs, DP, and greedy problems under timed conditions.
- Write clean, optimised code that handles all edge cases and passes all test cases.
Your Learning Roadmap
A week-by-week path from foundations to job-readiness.
Complexity Analysis & Big-O
Time and space complexity — the foundation every DSA topic builds on.
Arrays, Strings & Sliding Window
The most common interview topic — mastered through pattern recognition.
Linked Lists, Stacks & Queues
Pointer manipulation and classic linear data structure problems.
Binary Search & Two Pointers
Efficient search patterns that appear in 30%+ of interview problems.
Trees & Binary Search Trees
Traversals, construction, validation, and LCA problems demystified.
Graphs: BFS, DFS & Shortest Path
From adjacency lists to Dijkstra — graph problems step by step.
Dynamic Programming Foundations
Memoisation, tabulation, and the framework that makes DP approachable.
Advanced DP & Greedy Algorithms
Interval scheduling, knapsack, and classic greedy patterns.
Heaps, Tries & Union-Find
Advanced data structures that unlock efficient solutions.
Mock Interviews & Simulations
Timed interview simulations with structured feedback from Dev Kapoor.
Skill Gain Forecast
Proficiency benchmarks based on cohort outcomes. These are measured at your Week 8 oral viva — not estimated from syllabus coverage.
Proficiency you'll reach by Week 8
Proficiency you'll reach by Week 8
Proficiency you'll reach by Week 8
Proficiency you'll reach by Week 8
Proficiency you'll reach by Week 8
Proficiency you'll reach by Week 8
Skills You Will Master
Arrays & Strings
Two pointers, sliding window, prefix sums
Linked Lists
Pointer manipulation, cycle detection
Binary Search
Monotonic conditions & search space
Trees
Traversal, LCA, BST operations Master
Graphs
BFS, DFS, Dijkstra, topological sort
Dynamic Programming
Memoisation, tabulation, state design
Heaps / Priority Q
Heapify, K-th element problems Mastery
Complexity Context
Big-O, trade-offs, amortised analysis
Tools Covered
Platforms and environments used in the cohort.
Job Roles You Can Target
- Software Development Engineer (SDE)
- Backend Developer
- Full-Stack Engineer
- Algorithm Engineer
- Competitive Programmer
- SDE-2 / Senior SDE
Who Is This For?
Students & Graduates
Developers preparing for FAANG/MAANG or top Indian product company interviews.
Working Professionals
Engineers who have been rejected in DSA rounds and want a structured, coached path.
Career Changers
CS graduates or self-taught devs who want to build strong algorithmic foundations.
Is This Course Right For You?
This course is deliberately specific. Here's who gets the most out of it — and who should look at another stream first.
✓ Great fit
✕ Not the fit
DSA Mastery FAQs
Frequently Asked Questions
Python is the primary language. You can solve in Java or C++ — concepts are language-agnostic and we discuss trade-offs.
The core curriculum covers 100+ problems. Practice sets typically push students to solve 150–200 by the end.
Primarily yes — but the thinking skills transfer to writing better production code and architecting efficient systems.
Four simulated 45-min coding interviews under timed conditions, with feedback on approach, communication, and quality.
Yes. Self-taught practice often lacks structure. We provide a systematic framework for pattern recognition.