This course provides students with firsthand experience at solving real-world problems in a team environment. Student teams will be asked to solve a problem faced by a company or not-for-profit organization and to exploit all their knowledge and skills in order to develop a satisfactory solution for the problem. The course provides students with a challenge similar to the ones faced by practitioners in industry and requires creative thinking and application of mathematical skills to develop a satisfactory solution. The course concludes with presentation of an analysis of the problem and the proposed solution. Students will be mentored and guided by course lecturers throughout the semester.
- Semester 1 - 2015
1. Have developed skills to scope, structure, and deliver on projects;
2. Have been able to approach real world business problems with quantitative techniques;
3. Have learned to communicate, verbally and orally, to project stakeholders; and
4. Have been exposed to the realities of business processes and decision making.
Students will be supported to identify an unstructured business problem and will be asked to propose a solution to that business problem. To be able to do so, students will have to:
- familiarise themselves with the problem and produce a detailed problem statement
- scope a project to address key aspects of the problem, and prepare a project proposal
- gather and analyse data relevant to the problem (statistical analysis)
- investigate whether the problem or similar problems have been addressed before (literature search)
- formulate an appropriate model (mathematical modelling)
- develop a solution approach to solve the model (algorithm development)
- conduct a computational study, interpret the results, and draw conclusions
- prepare a final report on their results.
The course will include provision of material on principles and practices of mathematical consulting, on working in teams, on preparation of reports, and presentation of quantitative studies. The course will not be delivered in lecture format, but via direct supervision of student teams.
Successful completion of at least 4 mathematics or statistics courses at 2000/3000 level, at least 2 of which must be at 3000 level.
Integrated Learning Session
Face to Face On Campus 3 hour(s) per Week for Full Term
Contact hours include introductory lectures, individual student and team guidance, and presentations.