GRADUATE AND EXECUTIVE CERTIFICATE PROGRAMS
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Module 3: Leveraging Organizational Knowledge
Data Mining I
In this session, we examine the role of data mining in business intelligence, understand the kinds of managerial problems that can be modeled using data mining methods, and examine a couple of data mining methods: logistic regression-based modeling and decision trees.
Data Mining II
We continue our exploration of data mining. The session covers two major topics: (1) preparing data for data mining, and (2) clustering methods in data mining.
Data Mining III
In this session, we focus on two additional methods for data mining: (1) Neural Networks and (2) Market Basket Analysis using Association Rules. We also discuss some trends in data mining, such as text mining, multimedia data mining and Web mining.
Customer Relationship Management I
This session begins the study of Customer Relationship Management (CRM) by applying regression techniques to the modeling of customer behavior over time. Participants will learn how and when to apply multiple linear regression, logistic, and censored regression techniques to model customer acquisition and retention. Participants will learn to integrate these models in order to estimate the lifetime value of a customer, a fundamental building block of CRM.
Customer Relationship Management II
In this session, we continue to explore Customer Relationship Management (CRM), the business process analytics and technology that enable firms to attract and retain the most profitable customers. You will learn what CRM software does and who the key competitors are, as well as exploring why many CRM initiatives fail and the keys for a successful CRM implementation. The session concludes with a brief review of all the methods/techniques covered in Business Intelligence Graduate Certificate Program and a challenge for you to specify follow-up actions to apply what you’ve learned to your own organization.
