General Recommendations:

This course plan is for those who would like to explore data science, analytics or machine learning as a tool in any other field, but don’t wish to delve into a lot of mathematical or theoretical computer science rigour. The following plan is well suited for those venturing into analytics, finance or quantitative roles in the future. It is not necessarily recommended to choose all the courses recommended for a particular semester, please take your time commitments and the credit limits into consideration. Note : EE769 and CS419 require instructor approval and have prerequisites. Please plan ahead before choosing to take these courses.

3rd Semester:

• DS203 Programming for Data Science
• ME781 Statistical Machine Learning and Data Mining (DS elective)

4th Semester:

• DS303 Introduction to Machine Learning
• EE325 Probability and Random Processes (DS elective)

5th Semester:

• IE621 Probability and Stochastic Processes I (DS elective)
• IE605 Engineering Statistics (DS elective)
• ME781 Statistical Machine Learning and Data Mining (DS elective)
• IE609 Mathematical Optimization Techniques (DS elective)
• DH307 RnD Project (Related Project Course)

6th Semester:

• SC607 Optimization (if not IE 501/609) (DS elective)
• ME604 Introduction to Robotics (DS elective)
• IE616 Decision Analysis and Game Theory (DS elective)

7th Semester:

• SC631 Games and Information (if not IE616) (DS elective)
• ET610 Learning Analytics and Educational Data Mining (DS elective)
• IE643 Deep Learning Theory and Practice (DS elective)

8th Semester:

• IE630 Simulation Modeling and Analysis (DS elective)
• SLP/BTP

Link to all Possible Electives: CMInDS Minor