Skip to main content

Data Science: DS102
Data Science for Managers

Data Science for Managers
Enrollment is Closed

This course provides an introduction to data science and data mining concepts for managers with emphasis on data science applications and less on underlying mathematics .
Enrollment is Closed
About This Course

This course provides an introduction to data science and data mining concepts for managers. The goal is to give managers an overview of the field so that they can understand the potential of these methods in their own business environment. The course is taught at the MBA/Executive MBA level and focuses on understanding the managerial significance of the tools rather than the underlying mathematics. Case studies will be used to illustrate the application of techniques in a business setting.

Course Contents
  • Linear and logistic regression as foundational concepts for the course
  • Model selection and variable selection
  • Model testing and validation
  • Overview of many data mining techniques and concepts including supervised vs. unsupervised learning, clustering algorithms, classification and regression trees (CART), k-nearest neighbors (kNN), neural networks,dimension reduction
  • Application of techniques to case studies
Course Objectives

At the completion of the course, students will be able to do the following:

  • Explain key concepts in data science
  • Understand the differences between several algorithms and know when it is appropriate to use each one
  • Interpret the output from data mining algorithms
  • Explain the implications of an analysis in a managerially meaningful way
TextBook:

Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, Third Edition, by Shmueli, Bruce and Patel https://www.amazon.com/Data-Mining- Business-Analytics-Applications/dp/1118729277

PreRequisites:

Familiarity with statistics and linear regression at the level of an MBA core class; comfort in using Microsoft Excel

Computer Requirements

Examples and demonstrations in class will make use of the XLMiner add-in from Frontline systems. A four-month license for this software is included with a purchase of the textbook. This software only works in a PC environment.

Participants may also use R and the R Commander add-in. However, this software will not be formally supported as part of the class.

Course Staff
Course Staff Image #1
Greg Zaric

Greg Zaric is a Professor of Management Science at the Ivey Business School, University of Western Ontario. He is also cross appointed in the Department of Statistical and Actuarial Sciences and the Department of Epidemiology and Biostatistics at Western. At Ivey he teaches at the undergraduate, master’s MBA and Executive MBA levels, and has twice won the Lawrence G. Tapp award for Excellence in MBA teaching. His research focuses on applications of statistics, analytics and operations research to problems in health economics and healthcare operations management. He received a BSc from the University of Western Ontario, an MSc from the University of Waterloo, and an MS and PhD from Stanford University.

Enrollment is Closed