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Viewing: STAT 6240 : Statistical Data Mining

Last approved: Mon, 24 Apr 2017 08:03:43 GMT

Last edit: Sun, 16 Apr 2017 17:23:28 GMT

Catalog Pages referencing this course
Columbian College of Arts and Sciences
Statistics (STAT)
STAT
6240
Statistical Data Mining
Statistical Data Mining
Fall 2017
3
Course Type
Lecture
Default Grading Method
Letter Grade
statistics majors or with the permission of the instructor
No
No
STAT 6201, STAT 6202, and STAT 6214 or equivalents
Corequisites
coursework in mathematical statistics, applied linear models, and multivariate statistics
30
Tatiyana V. Apanasovich,
Frequency of Offering

Term(s) Offered

Are there Course Equivalents?
No
 
No
Fee Type


No


Introduction to basic data mining concepts and techniques for discovering interesting patterns hidden in large-scale data sets, focusing on issues relating to effectiveness and efficiency. Students are expected to be familiar with R programming.
Objective: The student will be able to properly formulate data mining problems, understand and utilize fundamental data mining techniques, and evaluate the data mining results.

Learning Outcomes: By the end of the class, the student will be able to

1. Display a comprehensive understanding of different data mining tasks and the algorithms most appropriate for addressing them.

2. Demonstrate capacity to perform a self directed piece of practical work that requires the application of data mining techniques.

3. Conceptualize a data mining solution to a practical problem.
This course is about statistical methods in Data mining. It is less about specific tools and more about concepts and techniques and statistical methods in Data mining.
Course Attribute

This was offered as a Topic Course (STAT 6289) in Fall 2016 and Spring 2017.
jbb (Thu, 16 Feb 2017 21:29:21 GMT): Rollback: The Middle States Commission on Higher Education now requires that all syllabi include average minimum amount of out-of-class or independent learning expected per week. For more detail see https://provost.gwu.edu/files/downloads/Resources/Assignment-of-Credit-Hours_Final_Oct-2016.pdf
Key: 10736