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Apr 6, 2016 by Larry Medsker (lrm)
DATS 6201 : Numerical Linear Algebra and Optimization
Wed, 06 Apr 2016 09:27:07 GMT
Mon, 28 Mar 2016 20:46:23 GMT
Catalog Pages referencing this course
Data Science (DATS)
Programs referencing this course
DS-GC: Data Science
DS-MS: Data Science
Columbian College of Arts and Sciences
Data Science (DATS)
Long Course Title
Numerical Linear Algebra and Optimization
Short Course Title
Num. Lin. Alg. & Optimization
Number of Credits
Default Grading Method
Designed primarily for students in the Data Science program, however other students with appropriate backgrounds can register for the course with permission of the instructor
Repeatable for Credit?
MATH 2184 or MATH 2185
An undergraduate degree with a strong background in science, mathematics, or statistics
Dr. Yanxiang Zhao
Frequency of Offering
Are there Course Equivalents?
Are Fees Applicable?
Explanation and Description of Fees
Are Additional Resources Required?
Explanation of Additional Resources
Justification for Additional Resources
Describe any Sources of Additional Funding
This course is a study of linear and quadratic programming, nonlinear equations, global and unconstrained optimization, and general linearly and nonlinearly constrained optimization as used in data science.
Students should be able to explain fundamental ideas and concepts of numerical linear algebra and optimization methods, demonstrate the knowledge of where the methods can be applied to problems in data science, and be able to use related techniques to solve typical problems.
Uploaded a Course Syllabus
DATS6201 DS_Numerical LA and Optimization.pdf
Explanation of how the course differs from similar GW courses
Students will gain practical skills typical of those of data scientists and gain experience applying the skills to real data.
This is to be a first-year course in the Master of Science in Data Science program.
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