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)
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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. |
High Demand Course? |
Yes |
Course Delivery Information
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Academic year 2024/25, Available to all students (SV1)
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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 )
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Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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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
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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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