Undergraduate Course: Statistical Computing (MATH10093)
|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
|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.
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).
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.
|High Demand Course?
Course Delivery Information
|Academic year 2017/18, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 8,
Supervised Practical/Workshop/Studio Hours 16,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
|No Exam Information
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.
|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.
|Graduate Attributes and Skills
|Course organiser||Prof Finn Lindgren
Tel: (0131 6)50 5769
|Course secretary||Ms Hannah Burley
Tel: (0131 6)50 4885