Module Catalogue

MAS2602 : Computing for Mathematics and Statistics (Inactive)

  • Inactive for Year: 2021/22
  • Module Leader(s): Dr Chris Graham
  • Lecturer: Dr Lee Fawcett
  • Owning School: Mathematics, Statistics and Physics
  • Teaching Location: Newcastle City Campus
Semester 1 Credit Value: 10
ECTS Credits: 5.0


To reinforce the computing in Python/R studied within MAS1801/1802, and to move towards expectations of more independent programming. To introduce a wider range of mathematical/statistical topics/techniques within Python & R, including methods that will be useful towards future project work. To introduce some basic ideas of algorithm complexity and computational complexity.

Module summary
Computing methods are of great use in a wide range of applications of pure and applied mathematics and statistics. This module builds on the methods introduced in MAS1801 and MAS1802, introducing additional techniques, some of increasing mathematical and computational sophistication. In implementing these methods, students will attain increasing competence with mathematical/statistical computing, and an increasing ability to use such methods independently, towards project-orientated goals.

Outline Of Syllabus

[Primarily using Python]
Plotting of vector fields (quiver plots) and trajectories (streamlines). Curve fitting (e.g. least squares fitting of known function to data). Root finding (Newton-Raphson and built-in Python solvers). Numerical derivatives through finite difference, and related techniques of numerical integration and numerical solution of ODEs.
[Primarily using R]
Simulations from univariate distributions (extending beyond those in MAS1802). Simulations from bivariate and trivariate distributions, including use of conditional distributions. Transformations of random variables. Sampling distributions. Illustrations of properties of hypothesis tests and confidence intervals.
[Using either Python or R]
Introduction to algorithm complexity and computational complexity. Illustration via sorting algorithms.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture111:0011:00Formal lectures
Guided Independent StudyAssessment preparation and completion210:0020:00Revision for tests
Scheduled Learning And Teaching ActivitiesLecture21:002:00Revision lectures
Scheduled Learning And Teaching ActivitiesPractical102:0020:00Computer practicals
Scheduled Learning And Teaching ActivitiesPractical21:202:40Computer-based tests
Scheduled Learning And Teaching ActivitiesDrop-in/surgery140:102:20Office hours
Guided Independent StudyIndependent study115:0015:00Preparation for final project
Guided Independent StudyIndependent study127:0027:00Studying, practising and gaining understanding of course material
Teaching Rationale And Relationship

Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Practicals are used to help the students to develop their programming skills but also afford an opportunity to develop the students’ abilities at applying the theory to solving problems. Office hours (two per week) provide an opportunity for more direct contact between individual students and the lecturer: a typical student might spend a total of one or two hours over the course of the module, either individually or as part of a group.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Written exercise1M20Project
Computer assessment1M40PC test 1 (80 mins, in-class, open book)
Computer assessment1M40PC test 2 (80 mins, in-class, open book)
Assessment Rationale And Relationship

The project allows the students to develop their problem solving techniques and to practise the methods learnt in the module. They also allows the assessment of the computational skills acquired by the students. The PC tests allow the students to assess their progress with the material. They both allow feedback to the students and so act as formative as well as summative assessment.

Reading Lists