Before my actual M.Tech Data Science & Engineering curriculum kicked off at BITS Pilani WILP, the program ran us through two preparatory courses over a couple of weeks — an Introduction to Python course and a Zero Level Mathematical Foundation course. Eight live sessions in total, zero credits, zero pressure on paper. I went in expecting both to be easy refreshers. One of them genuinely was. The other made me reconsider how much rust I'd built up. In this post I'll walk you through what was covered in each course, what I actually learned, and my honest take on whether the bootcamp was worth the time. If you're considering BITS Pilani WILP M.Tech, planning how to prep, or just want to know what the program actually looks like before semester one starts — this should give you a real picture. Watch the Full Video I've recorded a complete walkthrough of both courses on my YouTube channel. The video covers everything below in more detail, including the slides,...
Master Arrays & Strings Without Memorizing A simple way to actually understand DSA instead of getting stuck. You know arrays. You know strings. But can you solve this quickly? [2, 7, 11, 15], target = 9 Most developers struggle here — not because it’s hard, but because they don’t think the right way. Understanding Arrays (The Right Way) Think of an array like a row of boxes in memory. [10] [20] [30] [40] Index: 0 1 2 3 You can directly jump to any index. That’s why access is extremely fast. Access → O(1) Insert/Delete → O(n) Add your array visualization image here Where Arrays Become Slow [10, 20, 30, 40] Insert 15 at index 1 → [10, 15, 20, 30, 40] Everything shifts → that’s the cost. Let’s Solve a Problem [2, 7, 11, 15], target = 9 The beginner approach is brute force — try all pairs. It works… but it’s slow. The real question is: Can we avoid repeating work? Two Pointer Thinking If the array...