Data Analytics & Artificial Intelligence Driven Academic Orientation: Initiatives at Calcutta Business School

Indranil Ghosh (Assistant Professor,Calcutta Business School)

Abundance of data and rapid development in the field of computational intelligence in the last decade has seen introduction of courses in Business Analytics in management curriculum.

At, Calcutta Business School, several initiatives have been taken to embrace data analytics to train PGDM students in data driven analysis and decision making.

  • As a preliminary requirement in the first year of PGDM program, Business Statistics for Decision Making and Business Research Methodology are being taught.
  • Once the students successfully complete first year coursework, they are open for dual specialization. In the area of finance, an advanced course on Financial Analytics is offered which covers the salient features of global financial markets and state-of-the-art Analytical and Artificial Intelligence tools to analyze financial data for various practical implications. Similarly, in Marketing Specialization, Marketing Analytics course is also offered.
  • Throughout the second year, skill-development workshops in allied areas of analytics is offered. ‘Empirical Research in Finance’ is one of such workshop that imparts hands on training on Econometric and Machine Learning tools in a lab environment.
  • Calcutta Business School is equipped with modern analytical packages like ‘Python’, ‘R’, ‘IBM SPSS’, ‘Weka’ and ‘RapidMiner’ and data repositories like ‘Prowess’ and ‘Metastock’.The infrastructure is fully utilized by students and faculty members.
  • Over the last 3 years, faculty members have put their effort to conduct research in the area of analytics to build research environment in the academic program of Calcutta Business School. Often, students actively engage themselves in research too. Few selected research outcome are listed below:
  1. Tamal Datta Chaudhuri and Indranil Ghosh (2018). Empirical Research in Finance. Lap Lambert Academic Press, ISBN: 978-613-9-89435-2.
  2. Indranil Ghosh, Tamal Datta Chaudhuri (2017). Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates. Studies in Microeconomics, Vol. 5(2), pp. 1-27.
  3. Jaydip Sen, Tamal Datta Chaudhuri (2017), Understanding the Sectors of Indian Economy for Portfolio Choice. International Journal of Business Forecasting and Marketing Intelligence,
  4. Jaydip Sen, Tamal Datta Chaudhuri (2017). Analysis and Forecasting of Financial Time Series Using R: Models and Application. Scholars' Press, Germany, ISBN: 978-3-330-65386-3.
  5. Payal Pattnaik (2016). Using Wavelet Decomposition and Cross Correlation Analysis for Portfolio Formation. Presented at 8 th International Conference on Strengthening Strategies, Shaping Policies and Empowering Personnel: Key to Organizational Competitiveness, Held at Prestige Institute of Management, Gwalior. (Student Contribution)