First Semester Thoughts | M.Tech. (Data Science & Engineering) | BITS Pilani WILP

Hi everyone, 

This is my second post regarding the M.Tech in Data Science & Engineering program.

If you haven't read the first post - please do so here - https://rupakh.blogspot.com/2020/04/mtech-data-science-engineering-wilp.html

Important Disclaimer: Please note that this post has to be taken only as advice and not a formal directive from the university. I am not paid to write this post nor do I take any responsibility that arises if you decide to make a decision based on my post.

In this post, I will be discussing my experience having gone through (and passed!) the first semester here.

Let me begin by saying that this course is for serious aspirants. Agreed, the admissions were easy and you did not have to clear the GATE examination but that does not meant that the course is also easy.

Remember, it is conducted by one of the best engineering colleges in the country and they have reputation to maintain. Diving your time between work and studies is of utmost importance to do well in this course. The sooner you master this point, the better it is!

It will help if I go subject by subject so let me take this approach. 

The curriculum can be found here: https://bits-pilani-wilp.ac.in/m-tech/cluster/data-science-and-engineering.php#programme-curriculum

Data Mining:

This is a 3 credits course and is definitely one of the more important courses in this semester. This subject forms the base for Data Science & Engineering because in this course you will learn how to use raw data and run algorithms over it like k-means clustering, association rule mining, outlier analysis, etc. Do not take this course lightly because to learning everything in one go is really difficult and you will regret not studying regularly. The textbook of Han Kamber is more than sufficient along with YouTube videos. Practice more problems than learning theory. 

Mathematical Foundations for Data Science:

I will not beat around the bush - this is the toughest subject I have studied till date. I got a panic attack while giving my mid semester exam and I fainted. I was literally this stressed out! But you need not be. Read on.

This is a graduate level course in linear algebra which teaches you vector and matrix algebra, systems of linear algebraic equations and their solutions; eigenvalues, eigenvectors and diagonalization of matrices; graphs and digraphs; trees, lists and their uses; partially ordered sets and lattices; Boolean algebras and Boolean expressions. 

The first half of the course was brutal. I went through the lectures multiple times to understand concepts and referred to MIT and IIT lectures. 

These are the lectures I saw:

MIT - https://www.youtube.com/watch?v=7UJ4CFRGd-U&list=PL221E2BBF13BECF6C

IIT Madras NPTEL - https://www.youtube.com/watch?v=LJ-LoJhbBA4&list=PLbMVogVj5nJQ2vsW_hmyvVfO4GYWaaPp7

I also bought Gilbert Strang's book. It was really helpful. You will find it here: https://www.amazon.in/Linear-Algebra-Applications-Gilbert-Strang/dp/8131501728/

My advise is to watch each lecture as soon as it is taught on Saturday/Sunday and 0.75x speed and write everything down in a notebook and try and understand matrices and vector spaces. This will help you a lot in future courses like Machine Learning and Artificial Intelligence. 

If you aren't understanding everything, it is fine. Do NOT panic. In this course, even the toppers will struggle (a little less than others though). Because of relative grading, you will be fine if you are sincerely studying, submitting all assignments and quizzes on time and studying hard. 

And keep calm. Everything will be fine! :D

Computer Organization and Software Systems: 

This course deals with the hardware and software aspects of computers related to data science in an indirect way. For CSE/IT students, it is a recap of COA and OS learnt during BE/BTech.

The first half of the course is COA/Hardware aspects and the next half of the course if OS/Software aspects.

In my opinion, if you pay attention in class and are regularly studying, you should be fine. The marking in this subject is quite strict - don't know why. I got the worst grade in this subject - a B grade :(

I feel the latter half is easier because I liked the OS part more. The most tricky part in the first half was the design of the processor but this is a great lecture which helped me a lot - https://www.youtube.com/watch?v=0B-y1RPDXjs

Data Structures and Algorithms Design:

This course is also very important because data science effectively deals with building fast and optimal algorithms to process data efficiently. Our faculty was simply great and I enjoyed this course a lot because I like programming in general. It can be a tough course if you do not understand the concepts so be sure to study the concepts well. 

The best book is CLRS. This is the link - https://www.amazon.in/Introduction-Algorithms-Thomas-H-Cormen/dp/B0839JW93F

Even if you can get the used copy, do buy it, it is a must book for any computer scientist. 

Just before exams, you can refer to Prof. Abdul Bari's lectures. They were of great help. You can find them here: https://www.youtube.com/channel/UCZCFT11CWBi3MHNlGf019nw

Final word of advice:

Concentrate more of COSS and DSAD since these are 5 credit courses and will count more towards your CGPA. That being said, DM and MFDS are also important course to help set the right base for future courses so do not neglect them.

Please make sure that you do NOT miss the mid semester and the comprehensive examinations. You may have to repeat the course or you will get some RRA grade which means you will have to spend more time to finish this course. It is better to complete it within the time frame - all you need is to manage your work-life balance effectively. 

Try to score more marks in your quizzes and assignments as they form 40% of the marks. 

I will keep updating this page as and when I get time. But I hope this helped you all. 

I would love to expand my professional network on LinkedIn. Please send me a connection request here: https://www.linkedin.com/in/rupakh/

Thanks!

Cheers, 

Rupak.