To provide business practitioners and those who are interested in Prescriptive Analytics with a selected set of Management Science and optimization techniques and discuss the fundamental concepts, methods, and models needed to understand prescriptive-analytics and implement these techniques in the era of Big Data.

Introduction to Business Analytics, Linear/Integer/Non-Linear Optimization, Optimization of Network Models, Dynamic Programming, Introdcution to Heuristic Programming, Goal Programming, Selected Multi-Attribute Decision Making Methods, and Monte Carlo Simulation, which are believed to be among the most popular Prescriptive Analytics tools to solve a majority of business optimization problems, with case studies from Business, Industry, and Government applications using LINDO/LINGO/CPLEX/Pulb optimization package(s).

BE/BTech, ME/MTech, Masters in – Business Administration, Management, Operations Research, Computer Science, Computer Applications, Mathematics, Statistics, Commerce, Economics. (3rd year & Final year learners are also eligible to apply.

(1) Wayne L Winston. Operations Research: Applications and Algorithms (Latest Edition). Duxbury Press. An Imprint of Wadsworth Publishing Company, Belmont, California, USA
(2) Anderson, Sweeney and Williams. An Introduction to management Science: Quantitative Approaches to Decision Making (Latest Edition). South-Western College Publishing.
(3) U Dinesh Kumar. Business Analytics: The Science of Data-Driven Decision Making. Wiley India, 2017
(4) Ravindra A., Philips D.T., and Solberg J.J.. Operations Research – Principles and Practice (Latest Ed.), John Wiley and Sons.