Unit information: Mathematical and Data Modelling 3 in 2020/21

Unit name	Mathematical and Data Modelling 3
Unit code	EMAT30005
Credit points	30
Level of study	H/6
Teaching block(s)	Teaching Block 4 (weeks 1-24)
Unit director	Professor. Eddie Wilson
Open unit status	Not open
Pre-requisites	EMAT22220 Mathematical and Data Modelling 2 EMAT20200 Engineering Mathematics 2 EMAT20920 Numerical Methods with Matlab
Co-requisites	None
School/department	School of Engineering Mathematics and Technology
Faculty	Faculty of Engineering

Description including Unit Aims

This unit will build on the Mathematical and Data Modelling 1 and 2 units in the first two years of the engineering mathematics degree programmes and complete our students' thorough grounding in team-based mathematical modelling and problem solving applied to real world problems.

The unit will be divided into 3 project phases each taking 7 term-time weeks. At the start of each phase real world projects will be presented to the students mostly drawn from external stakeholders. Students will then be allocated to groups of 4-6 to work on each project.

During each project 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 academics. At the end of the phase each group of students will present their results and submit a written technical report.

Aims:

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

At the end of the course students will:

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. Have experience of writing substantial technical reports

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions including online group meetings, and presentations. The core activity will be in the form of group modelling projects on topics of interest to academia, business, or industry.

Assessment Information

3 x Summative Assessment, 100% - Coursework. This will assess all ILOs.

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.