Jan 29, 2022  
2017-2018 Graduate Catalog 
    
2017-2018 Graduate Catalog [ARCHIVED CATALOG]

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CS 567 - Computational Statistics


Description:
Applications of statistics for the computational sciences, including data mining, big data analytics, financial analysis, and signal processing.  Formerly MATH 567, students may not receive credit for both.

Prerequisites:
Prerequisites: 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.
  • Formulate and defend plan for extracting pertinent data from otherwise large and noisy datasets using available tools.
  • Rate plan for extracting pertinent data from otherwise large and noisy datasets using available tools.
Learner Outcomes Approval Date:
12/03/15



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