MA 307
Numerical 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).
3 lecture hours, 2 lab hours every other week.
Prerequisites: MA122, MA205; either CP104 or MA207; either MA200 or both MA104
and MA201.
Exclusion: MA371 and CP315/PC315.
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).
3 lecture hours, 2 lab hours every other week.
Prerequisites: MA122, MA205; either CP104 or MA207; either MA200 or both MA104
and MA201.
Exclusion: MA371 and CP315/PC315.
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).
3 lecture hours, 2 lab hours every other week.
Prerequisites: MA122, MA205; either CP104 or MA207; either MA200 or both MA104
and MA201.
Exclusion: MA371 and CP315/PC315.
