Unit name | Statistical Computing 1 |
---|---|
Unit code | MATHM0039 |
Credit points | 20 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Professor. Anthony Lee |
Open unit status | Not open |
Pre-requisites |
None |
Co-requisites |
None |
School/department | School of Mathematics |
Faculty | Faculty of Science |
This unit introduces statistical computing. The basic concepts and typical workflows of statistical software development are exemplified using state-of-the art tools with R as the primary programming language. Students will be introduced to literate programming, package development, version control and testing. Basic scientific programming concepts will be covered, such as vectorization, functional and object-oriented programming. Some data science packages in the tidyverse will be introduced to enable data manipulation and visualization. The use of important statistical tools will be covered, such as sparse matrix algebra, numerical optimization and numerical integration.
By the end of the unit students should be able to:
The unit will be taught through a combination of
Formative: a homework each week
Summative:
Matloff, N. (2011) The Art of R Programming;
Watkins, D.S. (2010) Fundamentals of Matrix Computations
Davis, T.A. (2006) Direct Methods for Sparse Linear Systems