Date Submitted: Mon, 15 May 2017 17:33:36 GMT

Viewing: EMSE 6577 : Data-Driven Policy

Last approved: Fri, 14 Apr 2017 08:01:45 GMT

Last edit: Mon, 06 Mar 2017 00:13:53 GMT

Changes proposed by: shicks
Catalog Pages referencing this course
Programs referencing this course
School of Engineering and Applied Sciences
Engineering Management and Systems Engineering (EMSE)
EMSE
6577
Data-Driven Policy
Data-Driven Policy
Fall 2017
0,3
Course Type
Laboratory
Lecture
Default Grading Method
Letter Grade

No
No
EMSE 6705, EMSE 6575 and EMSE 6765
Corequisites

20
Broniatowski
Frequency of Offering
Every Year
Term(s) Offered
Spring
Are there Course Equivalents?
No
 
No
Fee Type


No


The application of data mining algorithms and other computational techniques to answer questions related to policy; problem formulation, tool selection, and interpretation of analysis results; volume, velocity, variety, veracity, and value. May serve as a capstone course in the data analytics sequence.
As a result of completing this course, students will be able to:
1. Reduce large quantities of data to manageable size
2. Critically evaluate the strength of inferences drawn from big data
3. Bring multiple data sources to bear on a complex problem
4. Employ “nowcasting” and “forecasting” to make predictions as new data become available
5. Distill policy-relevant results from data
Uploaded a Course Syllabus
This course is unique
Course Attribute


cbeil (Fri, 21 Oct 2016 15:10:29 GMT): Rollback: Per federal regulations all new and current courses need to include on the syllabus the number of hours students spend in direct learning with an instructor and the number of hours spent out of class doing course work. A copy of the new policy can be found at: https://provost.gwu.edu/sites/provost.gwu.edu/files/downloads/Resources/Assignment-Credit-Hours-9-2016.pdf
Key: 10526