Postgraduate Course: Statistical Programming (MATH11176)
|School||School of Mathematics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||The course covers the fundamentals of Statistical Programming, using the R language for practical work.
The aims are
1. To teach good programming practice: design, structure, documentation/commenting, testing, debugging, version control and reproducibility.
2. To teach the key programming skills and methods required for statistics and data science. These are stochastic simulation, visualization, data handling, matrix computation and linear modelling.
Topics to be covered include:
- Version control.
- The R programming language (data structures, operators, functions, classes).
- Structured programming, design, testing, commenting and debugging.
- Stochastic simulation: stochastic models and simulation studies
- Data handling: Files, re-arrangement and tidying, regular expressions.
- Computing with linear models
- Matrix computation in general
Entry Requirements (not applicable to Visiting Students)
|Prohibited Combinations|| Students MUST NOT also be taking
Extended Statistical Programming (MATH11242)
||Other requirements|| * This course is only available to students on an Mathematics MSc or MSc Data Science (Informatics)*
Course Delivery Information
|Academic year 2023/24, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 24,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Coursework 100%, Examination 0% - students will be expected to complete several individual and team based computing assignments.
|| Written feedback for each assignment.
|No Exam Information
On completion of this course, the student will be able to:
- Write reasonably efficient, well structured and documented computer programs in R.
- Write efficient implementations of statistical methods.
- Be able to process data effectively, in particular preparing data for analysis and visualizing data.
- Show appreciation of reliable and reproducible computational methods, and the nature of a statistical analysis.
- Demonstrate expertise in commonly used statistical computing methods.
|Advanced R - Wickham (2nd ed.) - Chapman and Hall/CRC, 2014 (978-0815384571)|
R for Data Science - Grolemund, Wickham - O'Reilly, 2016 (978-1491910399)
|Graduate Attributes and Skills
|Course organiser||Prof Simon Wood
|Course secretary||Miss Gemma Aitchison
Tel: (0131 6)50 9268