DSA & Data Structures Training in Amritsar
Data Structures, Algorithms & Competitive Programming — our DSA & Data Structures course in Amritsar, 20 Weeks. Build the algorithmic foundation that top tech companies demand. Master trees, graphs, dynamic programming, and competitive programming patterns through rigorous problem-solving.
Course Overview
DSA is not about memorizing 500 problems. It is about recognizing patterns. Once you see that most interview problems are sliding window, two pointers, BFS/DFS, or dynamic programming in disguise, everything clicks. We start from the fundamentals: arrays, strings, linked lists. Not just how to use them, but how they live in memory, why Big-O matters, and when a hash map turns an O(n^2) solution into O(n).
Trees and graphs are where most people get stuck. We fix that. You will build trees from scratch, traverse them every way possible, and understand why BFS uses a queue while DFS uses a stack. Graph algorithms are taught with real intuition: why Dijkstra fails on negative edges, why union-find is nearly O(1), and why topological sort only works on DAGs. No hand-waving. You will understand the why behind every algorithm.
Dynamic programming is the hardest topic for most students, and we spend three full weeks on it. We start with memoization vs tabulation, build up from fibonacci to knapsack, then tackle bitmask DP, digit DP, and state machine DP. You will not just learn to solve DP problems. You will learn to recognize them, define the state, write the recurrence, and optimize the space.
The final stretch is competitive programming and interview mastery. Segment trees, Fenwick trees, string algorithms, game theory, and 200+ LeetCode problems solved with editorial explanations. We run mock interviews, practice whiteboard problem solving, and do Codeforces virtual contests. Twenty weeks, and you walk into any technical interview knowing you can solve whatever they throw at you.
Curriculum Blueprint
Weekly module breakdown covering core DSA, advanced graphs, dynamic programming, and competitive programming mastery.
- Big-O notation: time complexity, space complexity, and why O(n) vs O(n log n) matters at scale
- Arrays fundamentals: indexing, traversal, and common patterns (reverse, rotate, deduplicate)
- Two pointers technique: fast-slow pointers, left-right pointers, and when to use each pattern
- Strings: sliding window, substring search, anagram detection, and string matching basics
- Prefix sums and difference arrays: range sum queries in O(1) and why this pattern is underrated
- Kadane's algorithm: maximum subarray sum, and understanding the intuition behind it
- Linked lists: singly, doubly, circular, and pointer manipulation without losing your head
- Linked list patterns: cycle detection with Floyd's algorithm, merge two sorted lists, reverse in groups
- Linked list edge cases: dummy nodes, handling null pointers, and why most bugs are pointer bugs
- Stacks: LIFO principle, monotonic stack for next greater element, and balanced parentheses
- Queues: FIFO principle, deque for sliding window maximum, and circular queue implementation
- LRU cache: combining hash map with doubly linked list, and why this is a favorite interview question
- Hash tables: hash function design, collision resolution (chaining vs open addressing), and load factor
- Hash map patterns: two sum, frequency counting, grouping, and when hashing is not the right approach
- Recursion basics: base case, recursive case, call stack, and why recursion is hard until it clicks
Tools Covered
Prerequisites
- Fundamental understanding of mathematical logic and discrete mathematics
- Basic exposure to any programming language (C++ preferred)
- Commitment to daily problem-solving practice (minimum 2 hours)
- Absolute commitment to master core underlying software engineering over syntax rules
Lab Access
- Cloud sandbox environment
- Real production server access
- Local AI model playground
- 24/7 Git repository access
Quick Inquiry
Interested? Drop your details and we will reach out.
Common Questions
The things people ask us on the phone before they enrol — answered the same way we would answer them there.
There is no separate price for it. WebPrims charges one fee — ₹4,500 / month — and it covers any course in the catalogue, so the DSA course works out at about 5 months — around ₹22,500 in total. No admission fee, no registration fee, and the certificate is included.
Monday to Saturday, with batches starting at 11:00 AM, 1:00 PM, 3:00 PM and 5:00 PM. Each slot runs up to two hours — sometimes a session finishes early, never late. That works out at roughly 240 hours of class time across the 20 Weeks. Pick whichever time fits around college or work, and move to another later if your timetable changes; it is the same DSA syllabus in each. We are closed on Sunday.
20 Weeks of taught material, which is about 5 months of DSA classes. That is the pace of the syllabus; how long you actually take depends on how often you turn up, and nobody is pushed to keep up with the batch.
Yes — you need one language you are comfortable in. C++, Java or Python all work. This is not where you learn to code; it is where you learn to solve.
Not a product. A body of solved problems: trees, graphs, dynamic programming, and enough repetition that you recognise a pattern instead of rediscovering it under pressure.
It is the part of the preparation we can teach. Twenty weeks of patterns and practice is what those interviews test. Whether you get the interview is a separate problem, and we do not run placements — we say so plainly elsewhere on this site.
Yes. Book a free demo class and sit in a real DSA session — one that was running anyway, not a presentation arranged for you. Write some code on one of our machines, ask the students already in it what it is like, and decide afterwards. Nothing to pay and no obligation.
Something here not answered? Ask us directly or book a free demo class and put it to the DSA mentor in person.
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