Degree Level Course
Thematic Ideas in Data Science
To enable the students to work on advanced data science topics, to utilize the overarching principles used in data science to solve novel problems, and to to use the principles learnt in multiple application areas.
Course ID: BSCS4003
Course Credits: 4
Course Type: Elective
Pre-requisites: None
What you’ll learn
Course structure & Assessments
12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.
| WEEK 1 | Notion of thematic ideas, feature engineering |
| WEEK 2 | Data imputation, Handling class imbalance |
| WEEK 3 | Bootstrapping |
| WEEK 4 | Cross-validation |
| WEEK 5 | Kernel trick/lifting to higher dimensions |
| WEEK 6 | Boosting |
| WEEK 7 | Analysis of complexity of models |
| WEEK 8 | Overparameterization, local and global optimization |
| WEEK 9 | Universal function approximation |
| WEEK 10 | Brief introduction to other interesting thematic ideas in ML |
| WEEK 11 | Application examples |
Prescribed Books
The following are the suggested books for the course:
Raghunathan Rengaswamy and Resmi Suresh, “Data Science for Engineers”, to be published by December 2021.
Instructor Notes
About the Instructors
Raghunathan Rengaswamy is the Marti Subrahmanyam Institute Chair Professor, Dean Global Engagement, and a core member of the Robert Bosch Center for Data Science and AI (RBC-DSAI) at IIT Madras. He is a co-Founder and Director of three IITM incubated companies: Gyan Data Pvt. Ltd. in data analytics, GITAA Pvt. Ltd., a data science training company, and Elicius Energy, developing a novel hydrogen PEM fuel cell. Prior to this, he worked for several years as a faculty in the US and at IIT Bombay.
Raghu’s work is in systems engineering with a focus on modeling and the use of data science, ML and AI techniques. He also approaches problems in energy systems, systems biology and droplet microfluidics using systems principles. His work in these areas has resulted in more than 125 papers, two US patents and several conference papers and presentations. An undergraduate process control book that he co-authored incorporating latest ideas in the field was published in May 2020. Twenty-four PhD students have graduated from his group with several going on to become faculty at IITs and USA.
Overall, his work has been cited more than 12000 times with an h-index of 43 (Google Scholar). He has given several keynote lectures and his work has been recognized through best paper awards in a journal and conferences. He has run several workshops for corporates in India and abroad on the use of AI/ML in industry. He does consulting for companies in India and abroad on data science projects. Based on a survey, Class Central declared his NPTEL course “Python for Data Science” as one of the top 30 MOOC courses introduced world-wide in 2019. He has participated in several committees: program advisory committee (PAC) of Department of Science and Technology (DST), India; member of research council, Naval Materials Research Laboratory (NMRL), DRDO, India; advisor to Confederation of Indian Industry (CII) for their AI center of excellence. He has received awards for his research: Young Engineer Award for the year 2000 awarded by INAE, the Graham faculty research award at Clarkson University in 2006. He has also received teaching awards: Omega Chi Epsilon professor of the year award at Clarkson in 2003, and Dr. Y.B.G. Varma award for teaching excellence at IIT Madras in 2018. He was elected a fellow of Indian National Academy of Engineering in 2017.