Module Catalogue

MAS2806 : Scientific Computation with Python

  • Offered for Year: 2024/25
  • Available for Study Abroad and Exchange students, subject to proof of pre-requisite knowledge.
  • Module Leader(s): Dr William Rushworth
  • Owning School: Mathematics, Statistics and Physics
  • Teaching Location: Newcastle City Campus
Semesters

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

Aims

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.

Outline Of Syllabus

⦁       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.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion211:0021:00Completion of in course assessment
Scheduled Learning And Teaching ActivitiesLecture111:0011:00Lectures
Scheduled Learning And Teaching ActivitiesLecture122:0024:00Computer Practicals
Guided Independent StudyIndependent study441:0044:00Preparation time for lectures, background reading, coursework review
Total100:00
Jointly Taught With
Code Title
PHY2039Scientific Computation with Python
Teaching Rationale And Relationship

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.

Assessment Methods

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

Exams
Description Length Semester When Set Percentage Comment
Digital Examination1201A70Digital Examination - In person
Exam Pairings
Module Code Module Title Semester Comment
Scientific Computation with Python1N/A
Other Assessment
Description Semester When Set Percentage Comment
Prob solv exercises1M15Problem-solving exercises assessment
Prob solv exercises1M15Problem-solving exercises assessment
Assessment Rationale And Relationship

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.

Reading Lists

Timetable