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 | Visiting students are advised to check that they have studied the material covered in the syllabus of any pre-requisite course listed above before enrolling. Visiting students are advised to check that they have studied the material covered in the syllabus of each prerequisite course before enrolling. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2022/23, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 8,
Supervised Practical/Workshop/Studio Hours 16,
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% |
| 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 Greta Mazelyte
Tel:
Email: greta.mazelyte@ed.ac.uk |
|
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