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Viewing: DATS 6103 : Introduction to Data Mining

Last approved: Tue, 24 May 2016 08:19:31 GMT

Last edit: Wed, 06 Apr 2016 17:58:37 GMT

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Columbian College of Arts and Sciences
Data Science (DATS)
DATS
6103
Introduction to Data Mining
Introduction to Data Mining
Fall 2016
3
Course Type
Lecture
Default Grading Method
Letter Grade
candidates for the MS or graduate certificate in data science; permission of the instructor may be substituted
No
No
DATS 6101 or permission of the instructor
Corequisites

25
Dr. Larry Medsker
Frequency of Offering

Term(s) Offered

Are there Course Equivalents?
No
 
No
Fee Type


No


Concepts, principles, and techniques related to data mining; strengths and limitations of various data mining techniques, including classification, association analysis, and cluster analysis.
After completing this class, students should be able to apply data analysis methods to real-world problems, demonstrate knowledge of foundational material for taking more advanced courses in data science, and apply the knowledge to deeper data mining concepts and the basic techniques in the practice of data science. Students will also demonstrate a basic under standing of the language R.
Uploaded a Course Syllabus
Students will gain practical skills typical of those of data scientists and gain experience applying the skills to real data.
Course Attribute

This is to be a first-year core course in the Master of Science in Data Science program.
twilson (Thu, 10 Dec 2015 15:52:00 GMT): Rollback: Please update prerequisites
gharris (Tue, 23 Feb 2016 17:30:36 GMT): Rollback: Please add week-by-week class schedule and u. religious holidays policy to syllabus.
Key: 9853