Undergraduate Course: Statistical Computing (MATH10093)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course provides an introduction to programming within the statistical package R. Various computer intensive statistical algorithms will be discussed and their implementation in R will be investigated. |
| Course description |
Topics to be covered include :
- basic commands of R (including plotting graphics);
- data structures and data manipulation;
- writing functions and scripts;
- optimising functions in R; and
- programming statistical techniques and interpreting the results (including bootstrap algorithms).
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Statistical Methodology (MATH10095)
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | This is a Year 3, Honours level course. Visiting students are expected to have an academic profile equivalent to the first two years of the BSc (Hons) Mathematics programme (UTMATHB). Students should have passed courses equivalent to Statistical Methodology (MATH10095). |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2025/26, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Supervised Practical/Workshop/Studio Hours 22,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
54 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 100% |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Write structured code for reproducible statistical data analysis
- Construct simulation studies for given statistical models to assess the estimation and prediction performance of numerical statistical methods
- Choose, implement, and analyse computer intensive statistical methods for a given problem
- Correctly interpret the output of numerical statistical methods in their motivating contexts
- Apply proper scoring rules to out-of-sample prediction analysis and model selection
|
Reading List
Crawley. M. (2013). The R Book (2nd edition). Wiley.
Venables, W. N. and Ripley, B. D., (2002). Modern Applied Statistics with S (4th edition). Springer. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | SComp,Statistics,Computing |
Contacts
| Course organiser | Miss Amanda Lenzi
Tel:
Email: amanda.lenzi@ed.ac.uk |
Course secretary | Miss Kirstie Paterson
Tel:
Email: Kirstie.Paterson@ed.ac.uk |
|
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