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Unit information: Mathematical and Data Modelling in 2018/19

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Unit name Mathematical and Data Modelling
Unit code EMATM0037
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Giuggioli
Open unit status Not open

Basic knowledge gained in mathematics undergraduate courses on vector calculus and partial differential equations and numerical skills. For those students entering the programme that do not have that background, it means that they will be trained through the courses Partial Differential Equations and Numerical Methods in Matlab offered as mandatory in TB1 for the MSc (or equivalent background for and understanding of the relevant topics of those courses are waived by the programme director based on prior learning).



School/department Department of Engineering Mathematics
Faculty Faculty of Engineering


This unit will ground students in team-based mathematical modelling and problem solving applied to real world problems. The unit will be divided into 2 six week quarters. At the start of each quarter, the students are split into teams of 3-6 and present a sequence of real-world problems, one for each team. During the quarter, the students will be trained in the problem solving approach, and work on and be guided towards and through particular mathematical/computational solution methodologies by the supervising academic. At the end of the quarter, each group of students will present their results and submit a written technical report.


To give students a thorough grounding in mathematical modelling and problem solving applied to real world engineering / applied science problems. The course will cover both model-centric and data-centric paradigms

Intended learning outcomes

1. Have mathematically modelled a range of real world problems drawn from engineering, economics, and the physical, chemical and biological sciences.

2. Have experience of finding, reading and interpreting technical information.

3. Understand the mathematical modelling cycle, of model, analysis, prediction/interpretation, and iterative refinement.

4. Understand the differences between and relative merits of model-centric and data-centric paradigms.

5. Be able to identify and draw upon a range of appropriate mathematical and computational methodologies when presented with new and unfamiliar problems.

6. Have practised teamwork and time management.

7. Have learnt how to present and interpret mathematical results to/for a non-mathematical engineering audience.

8. Achieve advanced level of writing in their technical reports.

Teaching details

Methods of Teaching Computer laboratory sessions.
Contact Hours Per Week 3
Student Input 1 hour lecture, 2 hours of tutorials, 28 hours of demonstrations and practical classes, 69 hours of guided independent study including assessment.

Assessment Details

100% coursework

This unit will be assessed by two equal coursework assignments, one for each problem worked on. Each of the two five-week parts will be assessed by:

Group technical report (100%) – learning outcomes 1-8 (10 page text report, excluding Appendices and figures).

Formative assessments on presentation skills occur throughout the course during the lab sessions.

Reading and References

There is no standard set of textbooks for this course. Each problem presented will typically be accompanied by a couple of references. However, students will be encouraged to use the library and internet to discover any missing technical information not included in the problem presentation