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Data Science for Managers

Title Introductory Data Science for Managers
Quarter Spring 2017
Instructor Greg Zaric
Syllabus Course Description
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
Instruction Format
Assessment
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
Pre-Requisite Familiarity with statistics and linear regression at the level of an MBA core class; comfort in using Microsoft Excel
Time TBA
Location TBA
TA Information TBA
Effort Required 10-15 hours/week
Certification Participants who complete the course will receive an instructor-signed certificate with a letter grade
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.