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Viewing: DATS 6201 : Numerical Linear Algebra and Optimization

Last approved: Wed, 06 Apr 2016 09:27:07 GMT

Last edit: Mon, 28 Mar 2016 20:46:23 GMT

Catalog Pages referencing this course
Programs referencing this course
Columbian College of Arts and Sciences
Data Science (DATS)
DATS
6201
Numerical Linear Algebra and Optimization
Num. Lin. Alg. & Optimization
Fall 2015
3
Course Type
Lecture
Default Grading Method
Letter Grade
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
No
No
MATH 2184 or MATH 2185
Corequisites
An undergraduate degree with a strong background in science, mathematics, or statistics
15
Dr. Yanxiang Zhao
Frequency of Offering

Term(s) Offered

Are there Course Equivalents?
No
 
No
Fee Type


No


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.
Students will gain practical skills typical of those of data scientists and gain experience applying the skills to real data.
Course Attribute

This is to be a first-year course in the Master of Science in Data Science program.
Key: 9854