Online Course on Prescriptive Analytics (3:0)

Objectives:

To provide business practitioners and those who are interested in Prescriptive Analytics, a selected set of Management Science and Optimization Techniques along with the fundamental concepts, methods, and models for understanding prescriptive-analytics and implementation of these techniques in the era of Big Data. 

Syllabus:

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

Target Group:

Every Business, Industry and Government (BIG) organizations which has “Business Analytics” group to address various problems associated with Prescriptive Analytics. In addition, all Faculty and interested UG and PG Graduates in Engineering and Postgraduate in Business Administration/Management, Operations Research, Computer Science, Computer Applications, Mathematics, Statistics, Economics.

Who Can apply?

BE/BTech, ME/MTech, MSc/MS (in Business Analytics, Operations Research, Computer Science, Mathematics, Statistics), MCA, MA (in Economics), MBA

Reference Books

1. Waynel L Winston. Operations Research : Applications and Algorithms.
2. Thomson, Belmont, CA. [Sixth Indian Reprint 2010].
3. Abben Asllani. Business Analytics with Management Science Models and Methods. Person Education. 2015.
4. U Dinesh Kumar. Business Analytics: The Science of Data-Driven Decision Making. Wiley India, 2017
5. William P Fox. Mathematical Modeling for Business Analytics. CRC Press. Taylor & Francis Group, LLC. 2018