Program Structure [2023 Batch onwards]

• Core courses (12 credits): These are typically taken in the first and second semesters.
  • Data Science in Practice (3:1)
  • Applied AI: Building Practical and Scalable ML Systems (3:1)
  • Data Engineering at Scale (3:1)


• Sample Elective courses (at least 20 credits):
  • Financial Analytics
  • Probabilistic Machine Learning: Theory and Applications
  • Data Mining
  • Artificial Intelligence for Medical Image Analysis
  • Tensor computations for Data science
  • Applied Artificial Intelligence in Healthcare
  • Linear optimization and Network science
  • Students may also take courses from any of the three streams as an elective. These are the minimum number of elective credits. More may be taken as well.



• Project (32 credits): Students can start this three-term project for 32 credits after successfully completing all the core courses. Students will complete 20 project credits over two terms followed by a midterm evaluation, and the remaining 12 project credits in an exclusive semester for the final evaluation.
  
  Student will propose the topic in consultation with their Guide from within the organization, and an IISc faculty mentor will approve project goals. The faculty mentor will offer high-level feedback on the project and its progress, and coordinate the evaluations, while the in-house company Guide will offer active feedback and close support. The evaluation committee, which includes the faculty mentor and company guide, is appointed by the PCC.