Day 1/8 - 🔥 Master Time & Space Complexity from Scratch | DSA Foundations (Preview Lecture)

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📘 Time & Space Complexity – The Right Way to Think (Not Memorize)

Most interview candidates freeze when asked:
“What’s the time and space complexity?”
Not because they’re bad coders — but because they were never taught how to reason about performance.

In this video, we fix that.

This is Part 1 of the Babua Time & Space Complexity Course, where you’ll learn how interviewers actually expect you to think — logically, clearly, and at scale — without heavy math or rote memorization.

🚀 What you’ll learn in this video:

Why interviewers really ask about time & space complexity

How performance affects real production systems

Why bad algorithms fail silently at scale

Bubble Sort vs Merge Sort vs Java Built-in Sort (with real benchmarks)

Why algorithm choice matters more than programming language

Time vs Space trade-offs (and when to choose which)

How to compare algorithms using growth, not stopwatch timing

Best, worst, and average case — and why interviews care about worst case

How to analyze algorithms step-by-step using instruction counts

Full breakdown of Insertion Sort worst-case analysis

How nested loops lead to O(n²) growth (with intuition)

Why we generalize and move from exact counts to Big-O

Proof of sum of n natural numbers (used in complexity analysis)

🎯 Who is this for?

Beginners confused by time & space complexity

Intermediate devs who can code but can’t explain performance

Interview candidates preparing for product-based companies

Anyone who wants to think like a system designer, not just a coder

💡 Key idea:

Complexity is not about formulas.
It’s about understanding how your code behaves as input grows.

This video builds the foundation you need to analyze any algorithm confidently — in interviews and in real systems.

📌 Comment “Start” to begin the course
📌 Comment “Done” once you finish Part 1

Next videos will build on this and make complexity feel obvious, not scary.

CHAPTERS
00:00 - Introduction & Recap
05:53 - Why Does Time & Space Complexity Matter - Theory
11:51 - Sorting Example - Exercise
19:18 - Time vs Space Trade-off - Theory
23:54 - Importance - Summary
25:10 - Algorithm Performance - Theory
27:33 - The Fast Programmer Analoggy - Theory
33:28 - Why Tight Bounds are Important - Theory
39:27 - Input Matters More Than You Think - Theory
43:20 - Uniform Comparison Framework - Theory
44:30 - Best, Worst and Average Case - Theory
46:57 - Why we care about worse case - Theory
50:27 - Lets Analyse Algorithms - Theory
01:06:24 - How We Calculate Cost per Iteration - Theory
01:23:36 - Running time as N increases(Worst case) - Theory
01:32:12 - Conclusion


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