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MA 571 Prerequisites

No Prerequisite Information Available

MA 571 Leads To

MA 677, ST 673, MA 671

MA 571 Restrictions

Must be enrolled in one of the following Levels:

Graduate (GR)

MA 571

Comp Methods for Data Analysis

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The course covers computational techniques used in data analysis. All topics are illustrated with the use of R and/or Matlab. Topics may include some of the following: numerical linear algebra (solving linear systems, eigenvalue problem, factorization), methods of interpolation and curve-fitting, numerical optimization methods, statistical modelling (simulation of random variables and processes, introductory computational statistics). Exclusions: MA307, PC315, CP315, MA371, MA507, or equivalent.

The course covers computational techniques used in data analysis. All topics are illustrated with the use of R and/or Matlab. Topics may include some of the following: numerical linear algebra (solving linear systems, eigenvalue problem, factorization), methods of interpolation and curve-fitting, numerical optimization methods, statistical modelling (simulation of random variables and processes, introductory computational statistics). Exclusions: MA307, PC315, CP315, MA371, MA507, or equivalent.

0%Liked

Easy

0%

Useful

0%

0 ratings

The course covers computational techniques used in data analysis. All topics are illustrated with the use of R and/or Matlab. Topics may include some of the following: numerical linear algebra (solving linear systems, eigenvalue problem, factorization), methods of interpolation and curve-fitting, numerical optimization methods, statistical modelling (simulation of random variables and processes, introductory computational statistics). Exclusions: MA307, PC315, CP315, MA371, MA507, or equivalent.

MA 571

Comp Methods for Data Analysis

0%Liked

Easy

0%

Useful

0%

0 ratings

The course covers computational techniques used in data analysis. All topics are illustrated with the use of R and/or Matlab. Topics may include some of the following: numerical linear algebra (solving linear systems, eigenvalue problem, factorization), methods of interpolation and curve-fitting, numerical optimization methods, statistical modelling (simulation of random variables and processes, introductory computational statistics). Exclusions: MA307, PC315, CP315, MA371, MA507, or equivalent.

The course covers computational techniques used in data analysis. All topics are illustrated with the use of R and/or Matlab. Topics may include some of the following: numerical linear algebra (solving linear systems, eigenvalue problem, factorization), methods of interpolation and curve-fitting, numerical optimization methods, statistical modelling (simulation of random variables and processes, introductory computational statistics). Exclusions: MA307, PC315, CP315, MA371, MA507, or equivalent.

0%Liked

Easy

0%

Useful

0%

0 ratings

The course covers computational techniques used in data analysis. All topics are illustrated with the use of R and/or Matlab. Topics may include some of the following: numerical linear algebra (solving linear systems, eigenvalue problem, factorization), methods of interpolation and curve-fitting, numerical optimization methods, statistical modelling (simulation of random variables and processes, introductory computational statistics). Exclusions: MA307, PC315, CP315, MA371, MA507, or equivalent.

Course Schedule