MA 371
Comp Methods for Data Analysis
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).
Prerequisites: CP104 or MA207; MA200 or
both MA104 and MA201; ST230 or ST260.
Exclusion: MA307 and CP315/PC315.
3 lecture hours; 2 lab hours every other week
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).
Prerequisites: CP104 or MA207; MA200 or
both MA104 and MA201; ST230 or ST260.
Exclusion: MA307 and CP315/PC315.
3 lecture hours; 2 lab hours every other week
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).
Prerequisites: CP104 or MA207; MA200 or
both MA104 and MA201; ST230 or ST260.
Exclusion: MA307 and CP315/PC315.
3 lecture hours; 2 lab hours every other week
