Date Submitted: Mon, 15 May 2017 17:25:07 GMT

Viewing: EMSE 6575 : Data Mining and Processing

Last approved: Wed, 08 Mar 2017 09:01:50 GMT

Last edit: Mon, 06 Mar 2017 00:12:00 GMT

Changes proposed by: shicks
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In The Catalog Prerequisites:

EMSE 6577 : Data-Driven Policy
School of Engineering and Applied Sciences
Engineering Management and Systems Engineering (EMSE)
EMSE
6575
Data Mining and Processing
Data Mining and Processing
Fall 2017
0,3
Course Type
Laboratory
Lecture
Default Grading Method
Letter Grade

No
No
APSC 3115, EMSE 2705 or MATH 2184, and EMSE 6574
Corequisites

20

Frequency of Offering
Every Year
Term(s) Offered
Spring
Are there Course Equivalents?
No
 
No
Fee Type


No


Application of commonly used algorithms for data analysis using libraries in the Python programming language such as SciKit-Learn; unsupervised classification techniques, supervised classification techniques, and crowdsourcing for data annotation. Provides preparation for a capstone course in the data analytics sequence.
Learning Outcomes:
As a result of completing this course, students will be able to:
1. Select the data analysis algorithms that are most appropriate to solve specific problems
2. Apply libraries implementing these algorithms to real world data
3. Apply these algorithms on test data sources
4. Evaluate the quality of an algorithm’s output when applied to test data
5. Demonstrate understanding of the basic principles underlying these algorithms, and the conditions under which they may not apply
This course is a unique course in that it focuses on applied machine learning techniques
Course Attribute


Key: 10517