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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2019/2020

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Mathematics in Action A (MATH11180)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 5 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryNB. This course is delivered *biennially* with the next instance being in 2019-20. It is anticipated that it would then be delivered every other session thereafter.

Mathematics is central to much of everyday life: it underpins digital communications, internet searches, medical imaging, computer animations, weather and climate predictions and many more technological advances. At the heart of this impact lies the capability of mathematics to model complex systems, to process information and to provide solutions. This course will introduce the key steps that lead from the formulation of mathematical models to the development and implementation of numerical or analytical solutions. The course will give the learner a hands-on experience of the practical use of mathematics and empower them to apply their mathematical knowledge to real-world problems.

This outcome will be achieved in the context of a specific theme of contemporary interest which varies from year to year. Examples of possible themes include: Mathematics of Climate, Epidemics, Data Science, Mutations and Cancer, and Complexity.
Course description The course will be assessed continuously through regular assignments and one project. The numerical computations required will be carried out using Matlab.

This course has a counterpart Mathematics in Action B (MATH11181) which runs in alternate years. These courses are designed so that they can be taken in any order (they are not pre-requisites for one another), and both can be taken, as the syllabus for each in consecutive year will be different.

The theme will vary each session and might include :

- Data Visualization
- Epidemics
- Data science
- Mutations and cancer
- Complexity and extended systems
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Honours Differential Equations (MATH10066) AND Honours Complex Variables (MATH10067) AND Computing and Numerics (MATH08065) AND ( Probability (MATH08066) OR Probability with Applications (MATH08067))
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting 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
Academic year 2019/20, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 16, Dissertation/Project Supervision Hours 2, Supervised Practical/Workshop/Studio Hours 10, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 70 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Coursework : 100%
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Formulate mathematical models of simple systems.
  2. Develop methods of solution, analytical and numerical, for such models.
  3. Understand the main mathematical tool introduced within the theme, and apply it to problems related to the theme.
  4. Examine critically the assumptions underlying the relevant mathematical models and methods.
  5. Write structured reports on models, methods and results.
Reading List
This will vary according to the session's theme.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsMiAA
Contacts
Course organiserDr Tibor Antal
Tel: (0131 6)51 7672
Email: Tibor.Antal@ed.ac.uk
Course secretaryMr Martin Delaney
Tel: (0131 6)50 6427
Email: Martin.Delaney@ed.ac.uk
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