Unit information: Statistical Computing 2 in 2019/20

Please note: Due to alternative arrangements for teaching and assessment in place from 18 March 2020 to mitigate against the restrictions in place due to COVID-19, information shown for 2019/20 may not always be accurate.

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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 and Engineering

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:

  • Connect R with external formats for data storage, and other types of inter-operability (eg web-interfaces)
  • Programme in at least one other language, typically Python or C/C++

Either

  • Perform large-scale numerical calculations, including data analysis, in a second language,

Or

  • Implement high-performance multicore computing using OpenMP
  • Run high-performance computing applications in the Cloud, including managing very large datasets
  • Assimilate and document new concepts and methods in a reliable and accessible way.

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

No specific book references. Vast array of online R, Python and C references to be utilized

Related links