Skip to main content

Unit information: Mathematical and Data Modelling 3 in 2015/16

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Mathematical and Data Modelling 3
Unit code EMAT32200
Credit points 20
Level of study H/6
Teaching block(s) Teaching Block 4 (weeks 1-24)
Unit director Professor. Eddie Wilson
Open unit status Not open

EMAT22220 Mathematical and Data Modelling 2, EMAT20200 Engineering Mathematics 2, EMAT20920 Numerical Methods with Matlab.



School/department Department of Engineering Mathematics
Faculty Faculty of Engineering

Description including Unit Aims

This unit will build on mathematical modelling and case studies 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 4 six week quarters. At the start of each quarter, we will split the students into teams of 4-6 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

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

Computer laboratory sessions.

Assessment Information

Assessment details:

100% coursework

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

  • Group technical report (75%) – learning outcomes 1-8
  • Group presentation (5%) – contributes to learning outcome 7
  • Peer assessment (20%) – contributes to learning outcome 6

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.