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).
Entry Requirements (not applicable to Visiting Students)
|| Students MUST have passed:
Statistical Methodology (MATH10095)
||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?
Course Delivery Information
|Academic year 2022/23, 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:
- 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
|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||Miss Amanda Lenzi
|Course secretary||Miss Greta Mazelyte