Hi everyone,
This is another post regarding the M.Tech. in Data Science & Engineering program.
If you haven't read my earlier post - please do so here - https://rupakh.blogspot.com/2020/04/mtech-data-science-engineering-wilp.html
In this post, I will be discussing my experience having gone through (and passed!) the second semester here.
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
Introduction to Statistical Methods:
This is a very important course with respect to understanding the basics of statistics. The course begins with an introduction to probability which has very important implications and applications in machine learning (probabilistic learning - Chapter 6 of Tom Mitchell - Bayesian Learning) and in artificial intelligence (Hidden Markov Models) and in probabilistic graphical models in Semester 3. We then move on to mean, median mode, standard deviation, and variance concepts which will help you understand the core of statistics. Then, we will study statistical distributions, and move on to Hypothesis Testing. Each and every chapter is linked to the previous chapters so do not miss even a single class. We then study regression (which helps you understand regression studied in ML much better) and then finally to Time Series Analysis.
Time Series Analysis will be a separate topic and it is very interesting.
The textbooks for this course are:
Probability and Statistics for Engineering and Sciences,8th Edition, Jay L Devore, Cengage Learning (https://www.amazon.in/Probability-Statistics-Engineering-Sciences-9E/dp/9353506247)
and
Business Forecasting - John Hanke (https://www.amazon.in/Business-Forecasting-John-Hanke/dp/0132301202)
These two textbooks will suffice and it will be better to have the hard copies of these textbooks even for future purposes.
Our examinations was online and 3 out of 6 problems were to solve in in Microsoft Excel spreadsheets for this subject especially for Time Series Forecasting module. So, make sure to learn the theory well and also how to use Excel to implement the problems in the exam.
Introduction to Data Science:
This is a theory subject with some overlap with Data Mining subject.
I had used this book - Introducing Data Science:
This a very theoretical course in my experience, class lectures and notes were enough along with the textbook mentioned above. Nothing more to write about this subject except that it can be managed.
Machine Learning:
This course is very important in this semester and will also help you in your future job search. We will learn basics of all machine learning techniques beginning with Bayesian Learning, Regression, Decision Trees (repeated from Data Mining), Neural Network (which will form the basis of Deep Learning in Sem 3), Instance-based Learning, Ensemble Learning, Support Vector Machine (very important concept), and finally Unsupervised Learning. We used to refer to lectures of Dr. Y V K Kumar. His lectures were the best!
Textbooks:
Machine Learning - Tom M. Mitchell (the best book for ML) - a must have!
This book should be more than enough along with the PPTs shared by BITS and the lectures.
Artificial Intelligence:
I had opted for this elective since I was not interested in a hardware based course of Systems for Data Analytics -SDA (an extension of COA) and in data visualization course of Data Visualization & Interpretation - DVI.
I found this course quite difficult because, in my opinion, the faculty was not too great. The question papers were quite tough but NPTEL courses helped along with the standard textbook. The textbook needs to be read properly at least once and you will get a fair idea about the course. It is very well explained. The textbook is simply the bible for this course. No other books are needed.
NPTEL Courses:
Artificial Intelligence - Russell/Norvig
Final word of advice:
Concentrate more on ACI and ML since these are AI is a 5 credit course and ML is a 4 credit course and will count more towards your CGPA. That being said, ISM is also an important course to helps you understand the statistical aspect of algorithms.
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.
