Predictive Analytics

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 learnUnderstand 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 1:Introduction to Analytics

• Introduction to Analytics
• Analytics in Decision Making
• Game changers & Innovators
• Predictive Analytics
• Experts view on Analytics

Week 2:Simple Linear Regression (SLR)

• Case-let Overview
• Introduction to Regression
• Model Development
• Model Validation
• Demo using Excel & SPSS

Week 3:Multiple Linear Regression (MLR)

• Multiple Linear Regression
• Estimation of Regression Parameters
• Model Diagnostics
• Dummy, Derived & Interaction Variables
• Multi-collinearity
• Model Deployment
• Demo using SPSS

Week 4:Logistic Regression

• Discrete choice models
• Logistic Regression
• MLE Estimation of Parameters
• Logistic Model Interpretation
• Logistic Model Diagnostics
• Logistic Model Deployment
• Demo using SPSS

Week 5:Decision Trees and Unstructured data analysis

• Introduction to Decision Trees
• CHI-Square Automatic Interaction Detectors (CHAID)
• Classification and Regression Tree (CART)
• Analysis of Unstructured data
• Naive Bayes algorithm
• Demo using SPSS

Week 6:Forecasting and Time series Analysis

• Forecasting
• Time Series Analysis
• Additive & Multiplicative models
• Exponential smoothing techniques
• Forecasting Accuracy
• Auto-regressive and Moving average models
• Demo using SPSS