Download Syllabus Fall 2015 Course: Data Mining (Mgmt 635) Professor

Syllabus Fall 2015
Course:
Professor:
Email:
Office Hours:
Office Phone:
Data Mining (Mgmt 635)
Stephan Kudyba
[email protected]
Mon & Wed (4:00 – 5:30p.m.) & by appointment.
(973) 642-4693
Course Objective
Mgmt 635 is designed to provide the student with an understanding of both the theoretical base
of prominent data mining methodologies along with how data mining can be used in the business
environment to enhance operational efficiency. Concepts covered include: Data management
conceptual model building, statistical analysis and building mining models for a variety of
business applications. These objectives are accomplished through the utilization of relevant
textbooks, case studies, software demonstrations and hands on data analysis (class project). The
student should leave this course with a sound understanding of how to utilize data mining to
enhance business productivity in a variety of business applications. The concept of big data will
be covered throughout the semester.
Course Requirements (Overview):
Students will be responsible for assigned readings both from the required text and additional
readings when noted. Additional readings often require a summary to hand in due the following
class. Class lectures will include illustrations of Data Mining software to support topics. There
is a no-laptop/internet browsing policy in this class.
Required Text:
“Data Mining and Business Intelligence: A Guide to Productivity” by S. Kudyba & Hoptroff
(IDEA Group Pub, 2001).
“Managing Data Mining: Advice from Experts” by Stephan Kudyba (CyberTech Publishing
2004).
Grading Criteria:
Mid-Term Exam
Final-Exam
Projects
Class Participation &
Case Summaries
35%
35%
25%
5%
Project:
Depending on class size, projects will be individual or group. Students will be assigned a Data
Mining project which will require an entire management/business plan. (Details to be discussed).
Class Slides
Slides of corresponding lectures will be distributed before class in either hard copy form or
email. There is a no internet browsing policy in this class.
Academic integrity and honesty are core elements to this course. The NJIT Honor Code will be
upheld
Course Outline:
Part I: Developing the Theoretical Groundwork for Data Mining and Business Modeling.
Sep 8 - Introduction to Corporate Productivity (Business Strategy, Business Intelligence and
Data Mining in the “New Economy”). (Chapter 1 DM&BI).
Sep 14 - A Closer Look at Data Mining Techniques (Regression, Clustering, Neural Networks
and Segmentation) and Visualization. (Chapters 1 & 2 DM&BI).
Sep 21 - A Review of Statistical Inference Testing in Business Modeling.
Sep 28 - Essential Steps to Conducting a Robust Mining Analysis, (Asking the right questions,
Data Warehousing and normalization issues). Chapter 3 DMBI.
Oct 5 - Continuation of the Steps to Success and an introduction to Data Mining and Six
Sigma. (Chapter 3 & Appendix 4 DMBI). Project details announced.
Intro to SAS Mining technology
Oct 12 - Using Unstructured Data as a modeling resource. (Outside reading).
Oct 17 - Mid-Term Exam (Theoretical Groundwork has been Completed)
Part II (Applications in the world of Commerce)
Oct 26 - Post Mid-Term Quick overview and an In-Depth analysis of Advertising, Marketing
and Pricing analysis. (Chapter 5 DMBK, 8 DMExperts).
Nov 2 - Data Mining in e-commerce/click and mortar world. Privacy issues and data.
(Chapters 6&7 DMBI). Affinity and Sequence Analysis. Project Discussions.
Nov 9 - A Detailed Look at Data Mining for CRM and HCM Applications. (Chapter 4
DMExperts)
Nov 16 - Mining Business Models on an Enterprise Basis. (Chapter 8DMBI).
Nov 23 - Project Presentations
Nov 30 - Data Mining and Healthcare
Dec 7 - Final Exam Details