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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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 2019/20, 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:
- Apply the basic concepts of computer programming.
- Understand some computer intensive methods for statistical inference.
- Write efficient statistical functions and have the ability to debug such functions using the computer package R.
|
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 | Prof Finn Lindgren
Tel: (0131 6)50 5769
Email: Finn.Lindgren@ed.ac.uk |
Course secretary | Miss Sarah McDonald
Tel: (0131 6)50 5043
Email: sarah.a.mcdonald@ed.ac.uk |
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