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|
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.
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.
|Scheduled Learning And Teaching Activities||Lecture||11||1:00||11:00||Formal lectures|
|Guided Independent Study||Assessment preparation and completion||2||10:00||20:00||Revision for tests|
|Scheduled Learning And Teaching Activities||Lecture||2||1:00||2:00||Revision lectures|
|Scheduled Learning And Teaching Activities||Practical||10||2:00||20:00||Computer practicals|
|Scheduled Learning And Teaching Activities||Practical||2||1:20||2:40||Computer-based tests|
|Scheduled Learning And Teaching Activities||Drop-in/surgery||14||0:10||2:20||Office hours|
|Guided Independent Study||Independent study||1||15:00||15:00||Preparation for final project|
|Guided Independent Study||Independent study||1||27:00||27:00||Studying, 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.
The format of resits will be determined by the Board of Examiners
|Computer assessment||1||M||40||PC test 1 (80 mins, in-class, open book)|
|Computer assessment||1||M||40||PC 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.