THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2017/2018

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Statistical Computing (MATH10093)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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).
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Several Variable Calculus and Differential Equations (MATH08063) AND ( Statistics (Year 2) (MATH08051) OR Statistics (Yr 3) (MATH09022)) AND ( Linear Statistical Modelling (MATH10005) AND Likelihood (MATH10004)) OR Statistical Methodology (MATH10095)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting 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
Academic year 2017/18, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 8, Supervised Practical/Workshop/Studio Hours 16, 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%
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. apply the basic concepts of computer programming.
  2. understand some computer intensive methods for statistical inference.
  3. write efficient statistical functions and have the ability to debug such functions using the computer package R.
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
KeywordsSComp,Statistics,Computing
Contacts
Course organiserProf Finn Lindgren
Tel: (0131 6)50 5769
Email: Finn.Lindgren@ed.ac.uk
Course secretaryMs Hannah Burley
Tel: (0131 6)50 4885
Email: Hannah.Burley@ed.ac.uk
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