Decision makers often struggle with questions such as: What should be the right price for a product? Which customer is likely to default in his/her loan repayment? Which products should be recommended to an existing customer? Finding right answers to these questions can be challenging yet rewarding. Predictive analytics is emerging as a competitive strategy across many business sectors and can set apart high performing companies. It aims to predict the probability of the occurrence of a future event such as customer churn, loan defaults, and stock market fluctuations – leading to effective business management. Models such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks are frequently used in solving predictive analytics problems. Regression models help us understand the relationships among these variables and how their relationships can be exploited to make decisions. This course is suitable for students/practitioners interested in improving their knowledge in the field of predictive analytics. The course will also prepare the learner for a career in the field of data analytics. If you are in the quest for the right competitive strategy to make companies successful, then join us to master the tools of predictive analytics.
What you'll learn
Understand how to use predictive analytics tools to analyze real-life business problems.
Demonstrate case-based practical problems using predictive analytics techniques to interpret model outputs.
Learn regression, logistic regression, and forecasting using software tools such as MS Excel, SPSS, and SAS.
Week 0:Course Prerequisites
Analysis of Variance
Week 1:Introduction to Analytics
Introduction to Analytics
Analytics in Decision Making
Game changers & Innovators
Experts view on Analytics
Week 2:Simple Linear Regression (SLR)
Introduction to Regression
Demo using Excel & SPSS
Week 3:Multiple Linear Regression (MLR)
Multiple Linear Regression
Estimation of Regression Parameters
Dummy, Derived & Interaction Variables
Demo using SPSS
Week 4:Logistic Regression
Discrete choice models
MLE Estimation of Parameters
Logistic Model Interpretation
Logistic Model Diagnostics
Logistic Model Deployment
Demo using SPSS
Week 5:Decision Trees and Unstructured data analysis