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 covers the fundamentals of Statistical Programming, using the R language for practical work.
The aims are
1. To teach good programming practice: design, structure, documentation/commenting, testing, debugging, version control and reproducibility.
2. To teach the key programming skills and methods required for statistics and data science. These are stochastic simulation, visualization, data handling, matrix computation and linear modelling. 
Course description 
Topics to be covered include:
 Version control.
 The R programming language (data structures, operators, functions, classes).
 Structured programming, design, testing, commenting and debugging.
 Stochastic simulation: stochastic models and simulation studies
 Data handling: Files, rearrangement and tidying, regular expressions.
 Computing with linear models
 Matrix computation in general

Entry Requirements (not applicable to Visiting Students)
Prerequisites 

Corequisites  
Prohibited Combinations  Students MUST NOT also be taking
Extended Statistical Programming (MATH11242)

Other requirements  * This course is only available to students on an Mathematics MSc or MSc Data Science (Informatics)* 
Course Delivery Information

Academic year 2024/25, Not available to visiting students (SS1)

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 )

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 
Written feedback for each assignment. 
No Exam Information 
Learning Outcomes
On completion of this course, the student will be able to:
 Write reasonably efficient, well structured and documented computer programs in R.
 Write efficient implementations of statistical methods.
 Be able to process data effectively, in particular preparing data for analysis and visualizing data.
 Show appreciation of reliable and reproducible computational methods, and the nature of a statistical analysis.
 Demonstrate expertise in commonly used statistical computing methods.

Reading List
Advanced R  Wickham (2nd ed.)  Chapman and Hall/CRC, 2014 (9780815384571)
R for Data Science  Grolemund, Wickham  O'Reilly, 2016 (9781491910399)

Additional Information
Graduate Attributes and Skills 
Not entered 
Keywords  SP,statistics,programming language 
Contacts
Course organiser  Prof Simon Wood
Tel:
Email: simon.wood@ed.ac.uk 
Course secretary  Miss Kirstie Paterson
Tel:
Email: Kirstie.Paterson@ed.ac.uk 

