Predictive Analytics

By Dinesh Kumar | Indian Institute of Management Bangalore (IIMB)

Learners enrolled: 11036

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.

Summary

Course Status :	Completed
Course Type :	Core
Duration :	6 weeks
Category :	Management Studies
Credit Points :	2
Level :	Postgraduate
Start Date :	31 Jan 2023
End Date :	30 Apr 2023
Enrollment Ends :	15 Mar 2023
Exam Date :

Page Visits

Course layout

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

Instructor bio

Dinesh Kumar

Indian Institute of Management Bangalore (IIMB)

Professor Dinesh Kumar is a professor of Quantitative Methods and Information Systems at the Indian Institute of Management Bangalore. Recognized as one of the Top 10 Most Prominent Analytic Academicians in India, Professor Dinesh is the course director of Business Analytics and Intelligence Executive Education Programme conducted by IIM Bangalore. His main research and teaching interest are Business Analytics and Systems Engineering. He has published a number of case studies at the Harvard Business Publishing on the use of predictive and prescriptive analytics by the Indian companies, and authored more than 70 research articles and 2 books.

Course certificate

Grading Policy:

There will be (a) one Mid-term Internal Assessment and (b) one Final Exam.

Weightage:

· Mid-term Internal Assessment: This will carry 25% weightage. This mid term will have questions from first three weeks.

· Final Exam: This will carry 75% weightage. This final exam will have questions from all the weeks.

Passing Marks:

· You will be eligible for a certificate only if you score minimum 40% in Mid-term Internal Assessment and minimum 40% in Final Exam.

Badge on Certificate:

· If you score 90% or more overall, your certificate will include "badge (gold)" printed.

Final Examination:

· Type of exam: Computer based exam

· Exam Center: You will have to appear at the allotted exam centre and take the exam in person. (You can find the final allotted exam centre details in the admit card (hall ticket)).

· Admit Card: You can download the Admit card (Hall ticket) around one week before the exam.

· QP Pattern: The type of questions may include multiple choice questions, fill in the blanks, essay type (subjective) type, etc. But generally MCQs are commonly provided.

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