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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Prescriptive Analytics with Mathematical Programming (CMSE11356)

Course Outline
SchoolBusiness School CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits15 ECTS Credits7.5
SummaryThis is a compulsory course for the MSc in Business Analytics programme. The course will provide students with the foundations of prescriptive analytics with emphasis on mathematical programming concepts, applications, models, and solution methods.
Course description Optimisation problems are concerned with optimising an objective function subject to a set of constraints. When deterministic optimisation problems are translated in algebraic form, we refer to them as mathematical programs. Mathematical programming, as an area within Operational Research (OR), Management Science (MS) and Business Analytics (BA), is concerned with model building and strategies and methods for solving mathematical programs. In this course, we address model building in OR/MS/BA, present a variety of typical OR/MS/BA problems and their mathematical programming formulations, provide general tips on how to model managerial situations, and discuss solution strategies and present solution methods for linear programs, non-linear programs and integer programs.
The objective of this course is to enhance students' understanding of the critical nature of building appropriate mathematical models as simplified representations of realistic managerial situations, and the role such models play in prescribing solutions to decision making problems. The course also aims at training students to critically assess mathematical programming models and solution methodologies. In addition, students will learn how to use state-of-the-art prescriptive analytics tools in the context of decision problems faced by business managers. The course provides opportunities for students to learn from each other, from practitioners in the field, and from the latest theoretical and applied research in the field. The course will require students to work in groups on realistic projects in different business settings involving prescriptive analytics, and to present their work to the rest of the class and to an external panel when the projects are supplied by industry.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2018/19, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 150 ( Lecture Hours 20, Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 117 )
Assessment (Further Info) Written Exam 0 %, Coursework 90 %, Practical Exam 10 %
Additional Information (Assessment) Coursework
Term projects 60% weighting
Presentations 10% weighting
Take Home Exam 30% weighting

- Term projects (60% of the mark including a peer assessment component worth 10%) in which students will have to undertake a prescriptive analytics exercise including problem statement, model building, solution design, report on findings, formulation of recommendations and managerial guidelines.
- Presentations (10% of the final mark) involving communication of solutions to prescriptive analytics problems and the methods used to obtain them to demonstrate their ability to address real world decision problems and to convince their line managers or sponsors to implement the proposed solutions
- Take Home Exam (30% of the final mark)
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Discuss the concept and methods of prescriptive analytics, in general, and mathematical programming, in particular, using the proper terminology
  2. Identify and properly state prescriptive analytics optimization problems in different business settings, model them, choose the right solution methodology and methods and solve them using mathematical programming techniques
  3. Interpret solutions, formulate managerial guidelines and make recommendations
  4. Critically discuss alternative prescriptive analytics approaches and methods
  5. Communicate solutions effectively and efficiently to a critical audience of non-specialists
Reading List
Additional Information
Graduate Attributes and Skills Not entered
Course organiserDr Douglas Alem
Tel: (0131 6)51 1036
Course secretaryMiss Lauren Millson
Tel: (0131 6)51 3013
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