CS 567 - Computational Statistics

Description:
Applications of statistics for the computational sciences, including data mining, big data analytics, financial analysis, and signal processing. CS 467 and CS 567 are layered courses; students may not receive credit for both.

Prerequisites:
Prerequisite: CS 301 or undergraduate students may enroll with the permission of the instructor.

Credits:
(4)

Learner Outcomes:
Upon successful completion of this course, the student will be able to:

Analyze data using statistically based data mining and big data analysis techniques.
Assess the ability of statistically based inference to extract and aggregate information from large datasets.
Employ statistical methods for extracting pertinent data from large and noisy datasets.
Evaluate efficacy of existing statistical tools for extracting useful information from complex data sets in a scalable way.
Graduate students will formulate and defend plan for extracting pertinent data from otherwise large and noisy datasets using available tools.
Graduate students will rate plan for extracting pertinent data from otherwise large and noisy datasets using available tools.

Learner Outcomes Approval Date:
1/9/20

Anticipated Course Offering Terms and Locations:
Fall Locations Ellensburg Winter Locations Ellensburg