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for a past academic year. Please see the current academic year for up to date information.
| Unit name |
Statistical Computing 2 |
| Unit code |
MATHM0040 |
| Credit points |
20 |
| Level of study |
M/7
|
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24)
|
| Unit director |
Dr. Fasiolo |
| Open unit status |
Not open |
| Pre-requisites |
Statistical Methods 1 and Statistical Computing 1
|
| Co-requisites |
None
|
| School/department |
School of Mathematics |
| Faculty |
Faculty of Science |
Description including Unit Aims
This unit introduces students to the wider ‘computerverse’, especially those parts of it pertinent to scientific and big-data computing. These include tools to extend R and to enhance its performance, other computer languages, and other computing environments. This is a rapidly-changing area, and undoubtedly some of today’s state-of-the-art methods will be tomorrow’s also-rans, and so there is emphasis on skills for self-development. Parts of this unit will be developed and delivered in conjunction with the University’s Advanced Computing Research Centre.
Intended Learning Outcomes
By the end of the unit students should be able to:
- Interface R with C/C++ to perform computationally intensive tasks, such as Monte Carlo simulations
- Use high-performance computational libraries in C++, such as the Armadillo or Eigen linear algebra libraries
- Build and document R packages that include both R and C++ code
- Write R and/or C++ that runs in parallel via the OpenMP and/or MPI programming interfaces
- Use an HPC cluster for performing computationally intensive jobs
- Perform numerical calculations, including data analysis, in a second interpreted language (typically Python)
Teaching Information
Some lab based instruction as mentioned above in details
Assessment Information
Formative: a homework each week
Summative:
- A personal portfolio of notes, code snippets, and vignettes, 30%.
- Assessed coursework, 2 at 20% each.
- A group project, 30%.
Reading and References
- Chambers, J.M., 2017. Extending R. Chapman and Hall/CRC.
- Eddelbuettel, D., 2013. Seamless R and C++ integration with Rcpp. New York: Springer.
- Matloff, N., 2015. Parallel computing for data science: with examples in R, C++ and CUDA. Chapman and Hall/CRC.
- Wickham, H., 2014. Advanced r. Chapman and Hall/CRC.
