Postgraduate Course: Statistical Programming (MATH11176)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The course will include extensive use of the high-level statistical programming language R, and aims to introduce the principles and use of computer programming with special focus on statistics using R. |
| Course description |
Introduction to R
Basic commands of R (arithmetic, vectors, logicals, simulation of random variables)
Data structures and data manipulation (characters, objects, lists, factors, dataframes, matrices)
Graphics in R (plots, lines and points, legends)
Functions and scripts (simple functions, for/while loops, if/ifelse conditional constructs)
Fast looping and efficient programming (vector arithmetic, vectors vs functions, apply, mapply)
Computer intensive techniques (sample topics: inverse/rejection/importance sampling, Monte Carlo integration, bootstrap, Gibbs sampling though the exact techniques may vary from year to year)
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2016/17, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 24,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
74 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Coursework 100%, Examination 0% |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Be familiar with the principles of computer programming.
- Ability to write efficient statistical functions and have experience of debugging.
- Demonstrate expertise in specialised software.
- Show appreciation of simulation based methods for statistical inference.
- Demonstrate expertise in widely used computationally intensive routines.
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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
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | SP,statistics,programming language |
Contacts
| Course organiser | Dr Ioannis Papastathopoulos
Tel: (0131 6)50 5020
Email: i.papastathopoulos@ed.ac.uk |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: f.c.reid@ed.ac.uk |
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