The Master of Technology (Online) programme in Data Science and Business Analytics is offered by the Division of Interdisciplinary Sciences. It is designed for early-career professionals with 2-8 years of experience to upskill them to become technology and business leaders in information-driven enterprises. The designed coursework establishes the foundations of data science, trains on data engineering and machine learning techniques, and imparts practical business analysis skills. These learnings are coupled with a unique capstone project that applies the learnings to a hands-on project relevant to the industry. Faculty from the Departments of Computational and Data Sciences, Management Studies, CiSTUP and RBCCPS lead the courses through online lectures and tutorials.

Program Structure [2023 Batch]

• 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.