Postgraduate Course: Statistical Programming (MATH11176)
Course Outline
| 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 |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| 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. |
| Course description |
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)
| Pre-requisites |
|
Co-requisites | |
| 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 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 24,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
74 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 100%, Examination 0% - students will be expected to complete several individual and team based computing assignments. |
| Feedback |
Written feedback for each assignment. |
| No Exam Information |
Learning Outcomes
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.
|
Reading List
Advanced R - Wickham (2nd ed.) - Chapman and Hall/CRC, 2014 (978-0815384571)
R for Data Science - Grolemund, Wickham - O'Reilly, 2016 (978-1491910399)
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | SP,statistics,programming language |
Contacts
| Course organiser | Prof Simon Wood
Tel:
Email: simon.wood@ed.ac.uk |
Course secretary | Miss Kirstie Paterson
Tel:
Email: Kirstie.Paterson@ed.ac.uk |
|
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