Competitive Coding
Unit 1: Behaviour Analysis
Master the art of analysing algorithm behaviour — understand Big-O notation, time & space complexity, and learn to pick the right algorithm for every competitive coding problem.
⏱️ 5 hrs theory + 3 hrs practice | 💰 Earning Potential: ₹5K–₹20K/month | 📝 30 MCQs (Bloom's Mapped)
💼 Jobs this unlocks: SDE Intern (₹3–6 LPA) | Competitive Programmer (₹5–15 LPA) | Algorithm Engineer (₹8–20 LPA)
Opening Hook — The Speed That Separates Good From Great
⚡ Why Does Your Code Take 10 Seconds While Theirs Takes 10 Milliseconds?
When you search for a restaurant on Zomato, the app searches through 10 lakh+ restaurants and returns results in milliseconds. How? They use hashing (O(1) lookup), not a linear scan (O(n)). That single design choice makes the difference between a 0.001-second response and a 10-second freeze.
Google indexes 10 billion+ web pages and returns results in 0.5 seconds. Behind the scenes, algorithms like PageRank, inverted indices, and distributed sorting work together — each carefully analysed for time and space complexity.
In competitive programming, you have 1–2 seconds to process up to 10⁶ inputs. A brute-force O(n²) solution that works for n=1000 will TLE (Time Limit Exceeded) for n=10⁵. Understanding algorithm behaviour isn't optional — it's the single most important skill that separates a Codeforces Specialist from a Grandmaster.
This chapter teaches you to think like an algorithm analyst. You'll learn to look at any code and instantly know: "This is O(n log n), it'll pass for n≤10⁵" or "This is O(n²), I need to optimise."
Learning Outcomes — Bloom's Taxonomy Mapped
| Bloom's Level | # | Learning Outcome |
|---|---|---|
| 🔵 Remember | 1 | Define Big-O, Big-Ω, and Big-Θ notation and state their mathematical definitions |
| 🔵 Remember | 2 | List the 7 common complexity classes: O(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ), O(n!) |
| 🟢 Understand | 3 | Explain why worst-case analysis is preferred over average-case in competitive programming |
| 🟢 Understand | 4 | Describe the time-space tradeoff with real examples (HashMap vs binary search) |
| 🟡 Apply | 5 | Calculate time complexity of given code snippets involving loops, recursion, and nested structures |
| 🟡 Apply | 6 | Apply Big-O simplification rules (drop constants, drop lower-order terms) to mathematical expressions |
| 🟠 Analyze | 7 | Compare the efficiency of brute-force vs optimised algorithms for the same problem |
| 🟠 Analyze | 8 | Analyze nested loop patterns, divide-and-conquer recurrences, and sliding window techniques for complexity |
| 🔴 Evaluate | 9 | Judge which algorithm/data structure is best suited for a problem given its constraints (n ≤ 10⁵ vs n ≤ 10³) |
| 🔴 Evaluate | 10 | Evaluate time-space tradeoff decisions and justify when to use extra memory for faster execution |
| 🟣 Create | 11 | Design optimised solutions by selecting appropriate algorithmic patterns based on constraint analysis |
| 🟣 Create | 12 | Write formal complexity analysis reports for competitive programming solutions with proof of correctness |