Your programme is made up of credits, the total differs on programme to programme.
Semester 1 Credit Value: | 10 |
ECTS Credits: | 5.0 |
European Credit Transfer System | |
To reinforce the computing in Python studied at Stage 1, and to move towards expectations of more independent programming. To introduce a wider range of mathematical techniques within Python, including methods that will be useful towards future project work.
Module Summary
Computing methods are of great use in a wide range of applications applied mathematics. This module builds on the methods introduced at Stage 1, introducing additional techniques, some of increasing mathematical and computational sophistication. In implementing these methods, students will attain increasing competence with mathematical computing, and an increasing ability to use such methods independently, towards project-orientated goals.
⦁ Advanced plotting, including surfaces, vector fields and trajectories.
⦁ Curve fitting (e.g. least squares fitting of known function to data).
⦁ Root finding (e.g. Newton-Raphson and Python solvers).
⦁ Numerical derivatives through finite difference, and related techniques of numerical integration.
⦁ Numerical solution of ordinary differential equations and applications to dynamical systems.
⦁ Use Python for matrix manipulation, linear algebra and related techniques.
Category | Activity | Number | Length | Student Hours | Comment |
---|---|---|---|---|---|
Guided Independent Study | Assessment preparation and completion | 21 | 1:00 | 21:00 | Completion of in course assessment |
Scheduled Learning And Teaching Activities | Lecture | 11 | 1:00 | 11:00 | Lectures |
Scheduled Learning And Teaching Activities | Lecture | 12 | 2:00 | 24:00 | Computer Practicals |
Guided Independent Study | Independent study | 44 | 1:00 | 44:00 | Preparation time for lectures, background reading, coursework review |
Total | 100:00 |
Code | Title |
---|---|
PHY2039 | Scientific Computation with Python |
The teaching methods are appropriate to allow students to develop a wide range of skills, from understanding basic concepts and facts to higher-order thinking. 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 develop the students’ abilities at applying the theory to solving problems.
The format of resits will be determined by the Board of Examiners
Description | Length | Semester | When Set | Percentage | Comment |
---|---|---|---|---|---|
Digital Examination | 120 | 1 | A | 70 | Digital Examination - In person |
Module Code | Module Title | Semester | Comment |
---|---|---|---|
Scientific Computation with Python | 1 | N/A |
Description | Semester | When Set | Percentage | Comment |
---|---|---|---|---|
Prob solv exercises | 1 | M | 10 | Problem-solving exercises assessment |
Prob solv exercises | 1 | M | 20 | Problem-solving exercises assessment |
A substantial examination is appropriate for the assessment of the material in this module. The format of the examination will enable students to reliably demonstrate their own knowledge, understanding and application of learning outcomes. The assurance of academic integrity forms a necessary part of programme accreditation.
The coursework assignments allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; these assessments have a secondary formative purpose as well as a primary summative purpose.