Undergraduate Course: Modelling and Computing (MATH08084)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course is a first course in mathematical modelling and computing. Students will develop aptitude in modelling by casting problems into mathematical form, and explore different approaches to solve them, with a particular focus on computational methods.
Alongside fundamental programming skills, students will develop their professional skills in applying mathematics to formalise and solve real-world problems, developing code collaboratively, and communicating the results of mathematical and numerical analyses, |
| Course description |
This course introduces the way an applied mathematician might go about modelling problems or systems that they are interested in. This includes being explicit about what sorts of assumptions or approaches might be used, how these can be described mathematically, and ultimately applying appropriate techniques to find solutions. Different numerical methods are introduced, with a discussion of the efficiency, convergence, and accuracy trade-offs that usually arise.
The course also equips students with robust foundations in programming, with an emphasis on effective production of computer code in a professional style, using a relevant toolset. The language used will be Python.
Assessment will include two group projects, each exploring a mathematical modelling problem by applying the methods seen in the course. There will also be an individual guided conversation at the end of the Semester to assess fundamental programming skills.
Summary of student experience: You will develop, without any assumed prior knowledge, competency in Python programming, including the use of a professional toolset, providing vital skills for your future mathematical study and also for many careers. The programming and scientific computing aspects will be motivated by, and applied to, a range of mathematical problems, including models using differential equations, as well as optimization problems. Alongside these practical skills and applications, you will develop your mathematical knowledge, for example, developing the techniques necessary to compute exact solutions to simple differential equations, which can be used to validate your numerical implementations. Following on from the prerequisite Year 1 courses, you will further enhance your communication and groupwork skills.
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Information for Visiting Students
| Pre-requisites | Visiting students are advised to check that they have studied the material covered in the syllabus of each prerequisite course before enrolling. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2026/27, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 11,
Supervised Practical/Workshop/Studio Hours 33,
Summative Assessment Hours 0.5,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
151 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework: 100%
Examination: 0%
The 'guided conversation' will be 0%, assessed as pass/fail, and must be passed to pass the course. |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Produce, appraise, and troubleshoot short programs using Python, employing suitable tools and writing well-structured code to a professional standard.
- Explain the purpose, logic, advantages, and drawbacks of some fundamental computational methods used in applied mathematics.
- Use mathematics and programming as tools to model some simple systems, state and justify modelling assumptions, perform numerical experiments and critically analyse results.
- Synthesise and communicate knowledge on mathematical and computational modelling topics both in writing and in oral discussions.
- Contribute, as a dependable group member, to the collaborative exploration of the mathematical topic.
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Reading List
S. Linge and H.P. Langtangen, Programming for Computations, Python, Springer, 2016
P.R. Turner, T. Arildsen, and K. Kavanagh, Applied Scientific Computing with Python, Springer, 2018. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Computing,Mathematical Modelling,Applications of Mathematics |
Contacts
| Course organiser | Dr Charlotte Desvages
Tel: (0131 6)50 5051
Email: Charlotte.Desvages@ed.ac.uk |
Course secretary | Mr Martin Delaney
Tel: (0131 6)50 6427
Email: Martin.Delaney@ed.ac.uk |
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