Interested to know whether a customer is likely to churn based on his or her cell phone usage pattern? What is the best segment of customers a catalogue should be sent to? What is the profile of IRS tax-evasion suspects? Whether a flight is likely to be delayed based on the day of the week, time of day, and airport it departs from? If you answered YES to some of these questions, you are in the right course…….

COURSE DESCRIPTION

This course is designed to provide business students with the skills to conduct data mining and statistical analysis for dealing with common managerial decision-making tasks, such as prediction, classification and clustering.

The emphasis is on understanding the application of a wide range of modern techniques to specific managerial situations, rather than on mastering the mathematical and computational foundations of the techniques.

Upon successful completion of the course you should possess valuable analytical skills that will give you a competitive edge in many industry sectors, in a wide range of managerial and analytical positions.

INTEGRATION WITH BUSINESS DISCIPLINES

Data Mining can cater to all business functions. The examples used in class and homework projects pertain to a wide range of managerial disciplines, including marketing (e.g., predicting beer preference of individuals based on their demographics), accounting and finance (e.g., predicting the solvency of firms based on financial ratios), and e-commerce (e.g., predicting the bidding intensity on eBay based on product characteristics and seller ratings).

COURSE OBJECTIVES

• To introduce and use advanced statistical and data mining techniques
• To understand in which managerial situation which technique should be used
• To become a skeptical consumer of statistical techniques & information
• To enable interaction with senior managers, consultants and data analysts

HANDS ON

We will be using a textbook that comes with an installation code for a data mining add-on for Excel. This add-on will allow you to have some hands-on experience with data mining using the commonly used XL environment.