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 | This course focuses on teaching modern best practices for Statistical computing using the R programming language.
* This course is available to Mathematics MSc students only. * |
| Course description |
R as a programming language - syntax, data structures, control flow
Version Control - git, GitHub
Tidy data principals - tidyverse, data munging , data manipulation and cleaning, hierarchical data
Visualization visual design, ggplot2
Efficient computation - profiling, parallelization, working with big data, databases
Additional topics - text data & regular expressions, web scraping, interactive web apps (Shiny)
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | * This course is available to Mathematics MSc students only. * |
Course Delivery Information
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| Academic year 2019/20, 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 %
|
| Additional Information (Assessment) |
Coursework 100%, Examination 0% - students will be expected to complete several individual and team based computing assignments. |
| Feedback |
Each assignment will be marked and feedback will be provided via Learn. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Show familiarity with the principles of computer programming.
- 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
Advanced R - Wickham (2nd ed.) - Chapman and Hall/CRC, 2014 (978-0815384571)
R for Data Science - Grolemund, Wickham - O'Reilly, 2016 (978-1491910399)
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | SP,statistics,programming language |
Contacts
| Course organiser | Dr Colin Rundel
Tel: (0131 6)50 5776
Email: colin.rundel@ed.ac.uk |
Course secretary | Miss Gemma Aitchison
Tel: (0131 6)50 9268
Email: Gemma.Aitchison@ed.ac.uk |
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