Programme Content
Module 1
1. Introduction to Business Analytics
Module 1Introduces business analytics and its main levers that organizations use to capture value through it.
Module 12. Leading Analytics
Module 1Review a common business analytics tool to leverage the data and for predicting outcomes: Logistic Regression.
Module 21. Recommendation Analytics
Module 2Discuss the opportunities offered by Big Data and the possibilities for mass customization of services.
Module 2 2. Quality of Predictions Module 2 Discover how to assess the quality of predictions and the quantity of errors one may make when predicting 0/1 outcomes. Module 3 1. Financial Analytics - Training and Testing Models Module 3 Examine the notion of in-sample and out-of-sample predictions, and how the latter is key to properly assess the quality of predictions. Module 3 2. Skill Versus Luck - Sports Analytics Module 3 Learn how to disentangle skill versus luck when attempting to make predictions about the future performance of, e.g., sports players or investors. Module 4 1. Testing - Channel Management in Retail Module 4 Introduces Difference in Differences, a tool to test the quality of changes in decisions in complex environments in the absence of perfect experiments. Module 4 2. Simulation - Pension Analytics Module 4 Introduces simulation, a business analytics tool used to evaluate decisions in the presence of uncertainty. Module 5 1. Optimization Analytics - Pharmaceutical Detailing Module 5 Discover how to formalize the optimization of many decisions while accounting for different kinds of physical and business constraints. Practice making business analytics decisions in the presence of multiple objectives by exploring the notion of an efficient frontier. Module 5 2. From Concept to Deployment Module 5 Bring together many of the concepts developed during this business analytics program and provide a view of the entire analytics life-cycle. Module 6 Business Analytics: Frontier and Emerging Challenges Module 6 Review some of the main business analytics concepts covered and discuss emerging challenges such as bias & algorithmic fairness. Bonus Module For Python users, learn how to implement key business analytics techniques such as logistic regression and optimization using Python.