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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2018/2019

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

Postgraduate Course: Predictive Analytics and Modelling of Data (CMSE11357)

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 option course for the MSc in Business Analytics programme. The course will provide students with the foundations of predictive analytics to respond to the job market needs and shall cover concepts, applications, modelling/prediction and analysis techniques.
Course description This course aims at training students in the field of predictive analytics to respond to the job market needs using a variety of methodologies. Students' journey shall be a quest to distinguish the "true" signal from a universe of "noise" through the lenses of predictive analytics. To be more specific, this course covers the typical methodological steps of a prediction exercise, statistical modelling, probabilistic modelling, stochastic modelling, and artificial intelligence methodologies for prediction of both continuous and discrete variables with applications in business and economics. It also covers practical issues in predictive analytics and how to address them.
The objective of this course is to enhance students understanding of the importance of adopting a series of sound methodological steps in a prediction exercise and to provide them with an artillery of modelling and prediction techniques along with hands-on experience in using them. 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 prediction of continuous and discrete variables, 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 30 %, Coursework 70 %, Practical Exam 0 %
Additional Information (Assessment) Examination
Final exam 30% weighting - assesses LO1, LO2, and LO3.
Coursework
Term projects 60% weighting - assess LO1, LO2, LO3, and LO4.
Presentations 10% weighting - assess LO5.

-Term projects (60% of the mark including a peer assessment component worth 10%) in which students will have to undertake a prediction exercise including problem statement and related relevant research questions, prediction model building, preparation and assessment of forecasts, report on findings, formulation of recommendations and managerial guidelines.
- Presentations (10% of the final mark) involving communication of viable forecasts and the methods used to obtain them to demonstrate their ability to address real prediction problems and to convince their line managers or sponsors to base their plans on the proposed forecasts
-Exam(s) (30% of the final mark)
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Predictive Analytics and Modelling of Data1:30
Learning Outcomes
On completion of this course, the student will be able to:
  1. Discuss the concept and methods of prediction analytics using the proper terminology
  2. Identify and properly state research problems related to prediction analytics in different business settings
  3. Critically discuss alternative prediction approaches and methods, and choose the right prediction models for a prediction exercise, implement them, and prepare forecasts
  4. Formulate managerial guidelines and make recommendations
  5. Communicate forecasts effectively and efficiently to a critical audience
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsBA-PAMD
Contacts
Course organiserDr Matthias Bogaert
Tel:
Email: Matthias.Bogaert@ed.ac.uk
Course secretaryMiss Lauren Millson
Tel: (0131 6)51 3013
Email: Lauren.Millson@ed.ac.uk
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