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ANLY600 - Data Mining

Course Details

Course Code: ANLY600 Course ID: 4875 Credit Hours: 3 Level: Graduate

This course covers data mining using the R programming language. It offers hands on experience approach through a learn-by-doing-it strategy. It further integrates data mining topics with applied business analytics to address real world data mining cases. It continues the examination of the role of “Data Mining in R”, and review statistics techniques in prescriptive analytics, and some predictive analytics. Additionally, some standard techniques and excel functions will be also covered.





Course Schedule

Registration Dates Course Dates Session Weeks
04/29/19 - 10/04/19 10/07/19 - 12/01/19 Fall 2019 Session B 8 Week session
07/29/19 - 01/03/20 01/06/20 - 03/01/20 Winter 2020 Session B 8 Week session

Current Syllabi

After successfully completing this course, you will be able to

  • LO 1: Demonstrate advanced knowledge of data mining concepts and techniques.
  • LO 2: Apply the techniques of clustering, classification, association finding, feature selection and visualization on real world data
  • LO 3: Determine whether a real world problem has a data mining solution
  • LO 4: Apply data mining software and toolkits in a range of applications
  • LO 5: Set up a data mining process for an application, including data preparation, modelling and evaluation
  • LO 6: Demonstrate knowledge of the ethical considerations involved in data mining.
NameGrade %
Discussions 28.00 %
Week 1 Discussion 3.50 %
Week 2 Discussion 3.50 %
Week 3 Discussion 3.50 %
Week 4 Discussion 3.50 %
Week 5 Discussion 3.50 %
Week 6 Discussion 3.50 %
Week 7 Discussion 3.50 %
Week 8 Discussion 3.50 %
Assignments 72.00 %
Week 1 Assignment 12.00 %
Week 2 Assignment 12.00 %
Week 3 Assignment 12.00 %
Week 5 Assignment 12.00 %
Week 6 Assignment 12.00 %
Week 7 Assignment 12.00 %

Required Course Textbooks:

Data Mining and Business Analytics with R

Johannes Ledolter

URL: https://www.vitalsource.com/referral?term=9781118593639

Additional Resources

Additional resources will be provided in the class.

Web Sites

In addition to the required course texts, the following public domain web sites are useful. Please abide by the university’s academic honesty policy when using Internet sources as well. Note web site addresses are subject to change.

Site Name

Web Site URL/Address

Machine Learning Videos

http://work.caltech.edu/library/index.html

OnePageR

http:// togaware.com/onepager/

Data Mining Resources

http://www.ngdata.com/data-mining- resources/

Top Free Data Mining Software

http://www.predictiveanalyticstoday.com/top- free-data-mining-software/

Book Title:Data Mining and Business Analytics with R
ISBN:9781118447147
Publication Info:Wiley
Author:Johannes Ledolter
Unit Cost:$104.60

Previous Syllabi

Not current for future courses.