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

Course Outline
School	Business School	College	College of Humanities and Social Science
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Not available to visiting students
SCQF Credits	15	ECTS Credits	7.5
Summary	This 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 studentsż 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 2017/18, 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 Coursework Term projects 60% weighting Presentations 10% weighting -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 Data		2:00

Learning Outcomes
On completion of this course, the student will be able to: Discuss the concept and methods of prediction analytics using the proper terminology Identify and properly state research problems related to prediction analytics in different business settings Critically discuss alternative prediction approaches and methods, and choose the right prediction models for a prediction exercise, implement them, and prepare forecasts Formulate managerial guidelines and make recommendations Communicate forecasts effectively and efficiently to a critical audience