A four-month live course that teaches data structures and algorithms from scratch, from Big-O notation to graphs, and builds the problem-solving mindset that coding interviews and real engineering work both reward.
Data structures are ways of organising data in memory, such as arrays, linked lists, hash maps, trees, and graphs. Algorithms are the step-by-step procedures that operate on that data, such as searching, sorting, and traversal. Learning DSA means learning to choose the right structure for a problem and to reason about how much time and memory a solution needs.
For a beginner the hard part is rarely the syntax. It is knowing that a problem about counting duplicates wants a hash map, that a problem about the nearest larger value wants a stack, and that a nested loop over a million items will time out. That judgement is what this course builds, in the order that makes it stick.
Every topic follows the same loop: understand the problem the structure was invented to solve, implement it from scratch, then use it on progressively harder problems until the pattern is automatic. You finish able to explain your reasoning out loud, which is exactly what an interviewer is grading.
Most beginners need three to six months of consistent practice to become interview ready, assuming five to eight hours a week. The variable that matters is not talent or hours logged, it is whether practice is structured. Solving random problems from a list of 500 produces a long streak and very little transfer, because you never see the same pattern twice in a row.
This course compresses that timeline to four months by fixing the order. Complexity analysis comes before tricks, each structure is implemented before it is used, and problems arrive in pattern batches so that the fifth two-pointer problem feels obvious rather than novel.
If you cannot yet write a loop or a function without checking a reference, the fastest route is two to three weeks on Python for Beginners before this course. Skipping that step is the single most common reason beginners stall in month one.
Four months, three live classes a week, and one rule: nothing is used before it is understood. Each month ends with a concrete capability rather than a list of topics covered.
Month 1
By the end: You can read a problem, estimate the cost of the brute force, and say what a faster solution must look like.
Month 2
By the end: You stop guessing which container to reach for and can write recursive solutions without tracing every call by hand.
Month 3
By the end: You can choose a sort or search with reasons, and handle any tree question with depth-first and breadth-first traversal.
Month 4
By the end: You solve unseen medium problems under time pressure while explaining your approach out loud.
These are the numbers every interviewer expects you to know without hesitating. In class you derive them rather than memorise them, which is why they stay with you. Complexities are average case unless noted.
| Data structure | Access | Search | Insert | Delete | Typical use |
|---|---|---|---|---|---|
| Array (fixed size) | O(1) | O(n) | O(n) | O(n) | Ordered data you mostly read by index |
| Dynamic array (Python list) | O(1) | O(n) | O(1) amortised at end | O(n) | Growing sequences, the default container |
| Singly linked list | O(n) | O(n) | O(1) at head | O(1) at head | Constant-time insertion at the ends, no indexing |
| Stack (LIFO) | O(1) top | O(n) | O(1) push | O(1) pop | Undo, expression parsing, depth-first search, monotonic stack |
| Queue (FIFO) | O(1) front | O(n) | O(1) enqueue | O(1) dequeue | Breadth-first search, scheduling, buffering |
| Hash map / hash set | O(1) by key | O(1) average, O(n) worst | O(1) average | O(1) average | Counting, deduplication, memoisation, existence checks |
| Balanced binary search tree | O(log n) | O(log n) | O(log n) | O(log n) | Sorted data with range and successor queries |
| Binary heap | O(1) min or max | O(n) | O(log n) | O(log n) root | Priority queues, top-k, merging sorted streams |
| Graph (adjacency list) | O(1) neighbours | O(V + E) traversal | O(1) edge | O(degree) | Networks, dependencies, maps, anything with relationships |
Interviews rarely ask you to implement a sort from memory, but they very often ask why you picked one, whether it is stable, and what it costs in extra memory.
| Algorithm | Best | Average | Worst | Extra space | Stable |
|---|---|---|---|---|---|
| Bubble sort | O(n) | O(n²) | O(n²) | O(1) | Yes |
| Insertion sort | O(n) | O(n²) | O(n²) | O(1) | Yes |
| Selection sort | O(n²) | O(n²) | O(n²) | O(1) | No |
| Merge sort | O(n log n) | O(n log n) | O(n log n) | O(n) | Yes |
| Quicksort | O(n log n) | O(n log n) | O(n²) | O(log n) | No |
| Heapsort | O(n log n) | O(n log n) | O(n log n) | O(1) | No |
| Counting sort | O(n + k) | O(n + k) | O(n + k) | O(k) | Yes |
Want to verify any row yourself? Paste an implementation into our free online Python compiler and time it against a growing input.
This is the sequence the course follows, and it works whether you join a cohort or study alone. The order is the part people get wrong most often.
You cannot learn algorithms and syntax at the same time. If loops, functions, and lists still feel slow, spend two weeks on fundamentals before starting DSA.
Python for BeginnersComplexity is the vocabulary of every later topic. Once you can say why a nested loop over 10⁵ items fails, optimisation stops being guesswork and becomes arithmetic.
Run the examples in a browser Python compilerWrite your own dynamic array, linked list, stack, hash map, and heap. It takes an afternoon each and permanently removes the mystery from the built-in versions.
Batch problems by technique: five sliding window problems in a row, then five monotonic stack problems. Recognition is the skill being trained, and randomness prevents it.
Browse real interview questions by companyInterviewers score reasoning, not typing. Narrate the approach, the complexity, and the trade-off before you write code, and do it while someone is watching.
Practise with a mock interviewA strong coding round still loses to a weak resume screen or a rambling behavioural answer. Prepare the full loop in parallel with your DSA practice.
Build an ATS-friendly resumeBeginners lose months to the wrong starting point: interview drills before fundamentals, or fundamentals long after they were needed. Find the row that describes you today.
| Course | Start here if | What comes next |
|---|---|---|
| Python for Beginners | You have never written code, or you copy syntax from tutorials without following it. | Then take this DSA course |
| Data Structures and Algorithms for BeginnersThis course | You can write loops and functions but freeze when a problem has no obvious loop. | Then Python for Coding Interviews |
| Python for Coding Interviews | You know the structures and now need interview speed, idioms, and pattern recall. | Then System Design for Beginners |
| System Design for Beginners | You are interviewing for roles with a design round and have never designed a system. | Then the full interview bootcamp |
| Software Engineering Interview Prep Bootcamp | You want DSA, system design, and behavioural rounds covered in one guided program. | Interview with a prepared loop |
| AI Engineering | You already ship code and want to build with large language models, RAG, and agents. | Build production AI projects |
This course is taught by Rituraj, a former Facebook and Microsoft engineer and a Columbia University alumnus. As the founder of a Y Combinator-backed startup valued at $125 million, he has interviewed candidates for these exact rounds and built production systems where the difference between an O(n log n) and an O(n²) decision is measured in server bills.
Data structures and algorithms are the backbone of every system that has to stay fast as it grows. Search engines, feeds, payment systems, and recommendation engines are all built on the same handful of structures you learn here, applied at scale. For a beginner, this is the difference between code that works on ten rows and code that works on ten million.
The hiring side has not changed as much as the internet claims. Coding rounds at large technology companies still test structures, complexity, and communication, and levelling decisions still hinge on how cleanly a candidate reasons under pressure. Knowing where a role sits before you interview is worth as much as the practice itself.
Generative tools have raised the floor, not removed it. Anyone can produce a plausible function now, which means the valuable engineer is the one who can tell an O(n log n) approach from an accidental O(n²) one, spot the missing edge case, and justify the choice in review. That judgement comes from the fundamentals on this page.
If you are preparing for a specific company or level, it is worth reading the target before you train for it: our interview question library covers what each round asks, Google engineer levels and career levels across IT companies explain the scope expected at each rung, and salary benchmarks tell you what the round is worth.
Everything below pairs with this course: the programs that come before and after it, the free tools our learners practise with, and the hiring products companies use to evaluate the same skills.
Start here if the syntax itself is still slow.
The natural sequel: patterns at interview speed.
The other half of a technical loop, from first principles.
DSA, design, and behavioural rounds in one program.
Build LLM, RAG, and agent systems once fundamentals are solid.
The full catalogue in one place.
Run every example from class in the browser, no setup.
Real questions by company and role.
Practise explaining solutions out loud, with scoring.
Timed problem sets to keep recall sharp between classes.
Get past the screen that happens before the coding round.
Ask questions and compare notes with other learners.
What gets asked, round by round.
Company-specific coding and behavioural sets.
L3 to L10, scope and pay at each rung.
Grade codes and promotion timelines at TCS, Infosys, Wipro, and more.
Benchmark an offer before you accept it.
Hiring, screening, and employment products.
Outsource technical interviews to vetted interviewers.
Automated sourcing, resume ranking, and screening.
Unlimited jobs, candidates, and team seats.
Pre-vetted engineers, screened on the fundamentals above.
Yes. The course assumes no prior knowledge of data structures or algorithms and starts with how memory and arrays work before any problem solving begins.
The only prerequisite is basic programming: variables, loops, and functions in any language. If that is not yet comfortable, spend a few weeks on Python for Beginners first and join the next cohort.
Most beginners need three to six months of structured practice at five to eight hours a week to reach interview level. This course is four months of live classes plus guided practice between them.
Unstructured self-study usually takes far longer, not because the material is harder alone, but because problems get solved in random order and patterns never consolidate.
No. You need to be able to write a loop and a function in some language; the Python specifics you need are taught as they come up.
You can follow along and run every example in our browser-based Python compiler, so there is nothing to install on your machine.
Examples are taught in Python because it keeps the focus on the idea rather than boilerplate, and it is accepted in coding interviews everywhere.
The patterns are language-agnostic. Learners interviewing in Java, C++, JavaScript, or Go follow the same classes and write solutions in their own language during practice.
Around 250 deliberately chosen problems beat 1,000 random ones. What matters is coverage of every major pattern and revisiting each one until recognition is instant.
This course works through 250+ problems in pattern batches, from warm-ups to genuine interview questions, with the reasoning discussed live rather than left in an editorial.
Yes, and arguably more than before. Coding assistants produce plausible code quickly, which shifts the value to engineers who can spot an accidental O(n²), catch a missing edge case, and justify a data structure choice in review.
Interviews have also not stopped testing it. Coding rounds at large technology companies still centre on structures, complexity, and how clearly you reason out loud.
This course teaches the structures and algorithms themselves, assuming you have never seen them. Python for Coding Interviews assumes you already know them and drills interview-speed patterns, idioms, and timed problem solving.
Beginners usually take them in that order. If you can already implement a hash map and analyse a nested loop, start with the interview course instead.
Yes. The patterns taught here map directly to what those coding rounds test, and Month 4 includes timed mock interviews with feedback on both the solution and the explanation.
It also helps to know the specific shape of each company's loop before you apply, which our company interview question library covers in detail.
You need less depth than a backend candidate, but the screening round is often the same. Arrays, strings, hash maps, recursion, and complexity analysis show up in nearly every technical loop regardless of specialism.
Graph algorithms and advanced dynamic programming matter more for backend and infrastructure roles, and those sit at the end of this syllabus rather than the start.
Classes are live, three times a week, with dedicated doubt-solving time so you are never stuck on a concept for a week.
Recordings are available afterwards for revision, but the cohort format exists precisely because beginners stall when nobody can see where their reasoning went wrong.
Every learner gets 1:1 resume and interview feedback, and you keep access to our practice tools, mock interviews, and interview question library.
Most graduates continue with System Design for Beginners or the Software Engineering Interview Prep Bootcamp while they are actively interviewing.
Apply through the form on this page. Our team responds with current cohort dates, fees, and payment options, and confirms whether this course or another program is the right starting point for your background.
Cohorts are small so that live classes stay interactive, and early applicants are prioritised when a batch fills.
Cohorts stay small so live classes remain interactive. Apply and our team will confirm the next start date, fees, and whether this is the right course for your background.
Beginner-friendly: no computer science degree required.
Early applicants are prioritised when a batch fills.
