Day 3 Report – AICTE-ATAL Faculty Development Programme by MBA Department

As part of the ongoing AICTE-ATAL Faculty Development Programme (FDP) on “Analytics and Modelling for Business Research”, organized by the Department of Business Administration, Shri Madhwa Vadiraja Institute of Technology and Management (SMVITM), Bantakal, two expert sessions were held on Day 3 (8 October 2025).

Session 1: Exploring Analytics and Modelling for Business Research

Speaker: Dr. Kartikeya Bolar, Associate Professor and Chairperson (Information Systems and Analytics), TAPMI, Manipal
Time: 9:30 a.m. – 12:30 p.m.

The first session was conducted by Dr. Kartikeya Bolar, who shared his expertise on “Analytics and Modelling for Business Research.” With over 15 years of academic and research experience, he provided participants with a deep understanding of the role of analytics in enhancing business research outcomes.

Dr. Bolar emphasized the importance of integrating data-driven insights with theoretical frameworks to facilitate effective decision-making. The session covered key topics such as Structural Equation Modelling (SEM) and the fundamentals of Predictive Analytics, offering a structured pathway for researchers to adopt analytical approaches in their studies.

He also introduced various analytical tools useful in academic research and offered practical guidance on overcoming methodological challenges commonly faced by researchers. His blend of theoretical clarity and hands-on demonstration helped participants gain valuable insights into applying analytics for impactful business research.

Session 2: Building Financial Modelling and Machine Learning Foundations

Speaker: Mr. Praveen Kumar, Software Engineer II, J.P. Morgan Chase & Co.
Time: 2:00 p.m. – 5:00 p.m.

The second session, led by Mr. Praveen Kumar, focused on “Financial Modelling and Machine Learning.” Drawing upon his decade-long industry experience, Mr. Kumar explained how financial modelling integrates with modern data analytics and machine learning to aid informed business and investment decisions.

He began with a clear introduction to Machine Learning (ML) concepts, explaining data types and their real-world relevance in financial contexts. Participants were also introduced to platforms such as Kaggle and GitHub, learning how these can be leveraged for collaborative research, data sharing, and continuous upskilling in analytics.

Mr. Kumar’s practical insights and relatable examples effectively bridged the gap between academic learning and real-world application, providing participants with a solid foundation to explore financial modelling and machine learning in business research and decision-making.