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

Postgraduate Course: Predictive Analytics and Modelling of Data (CMSE11428)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course provide students with the fundamentals of supervised and unsupervised learning models to predict real-world business applications.
Course description Academic 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, and artificial intelligence methodologies for prediction of 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 course organiser.

Student Learning Experience
Students will use an online learning platform that offers theory, mixed with coding exercises and questions which will be supplemented by weekly 2-hour sessions that serves to answer questions regarding the material and to provide recapitulations of the major concepts.

The latter part of the course (modelling with machine learning techniques) will be taught in class, supplemented with lab sessions.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary.
Course Delivery Information
Academic year 2019/20, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Seminar/Tutorial Hours 14, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 160 )
Assessment (Further Info) Written Exam 40 %, Coursework 50 %, Practical Exam 10 %
Additional Information (Assessment) Group Report (50% weighting
Assesses Learning Outcomes 1,2,3 and 4.

Individual Presentation (10% weighting)
Assesses Learning Outcome 5.

Individual Written Examination (40% weighting)
Assesses Learning Outcomes 1,2 and 3.

The group assignment consists of group work on analysing a data set. The assessment is broken down into a report with peer evaluation and a presentation. The report will be one group mark, the presentation will be graded individual. In the report the students will have to go over the relevant academic literature and describe their methodology and findings. For both the presentation and the report emphasis will be placed on the managerial relevance of their work.

The written examination will focus on theoretical and practical questions about supervised and unsupervised learning techniques covered in class. It will be a 2-hour examination and will be assessed individually.
Feedback The assessments will be marked according to the University common marking scheme. Feedback on formative assessed work will be provided in line with the Taught Assessment Regulation turnaround period, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which will be communicated to students during semester.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)CMSE11428 Predictive Analytics and Modelling of Data2:00
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 predictions
  4. Formulate managerial guidelines and make recommendations
  5. Communicate predictions effectively and efficiently to a critical audience
Reading List
Applied Predictive Modelling Springer, Max Kuhn
Additional Information
Graduate Attributes and Skills After completing this course, students should be able to:

A. Knowledge and Understanding:
1. define and understand the business problem and the predictive analytics goals;
2.describe the key steps in the predictive modelling process in order to solve the business problem;
3. Identify the proper predictive modelling techniques to solve the business problem;
4. understand and apply predictive modelling techniques;
5. critically evaluate and interpret the results of the predictive models and how they can help in solving het business problem;

B. Practice: applied knowledge, skills and understanding:
1. be familiar with relevant activities that should be executed at each stage of the predictive modelling process;
2. transform the data such that it can be used to build a predictive model;
3. select the most appropriate predictive model for a given business problem;
4. understand advantages and drawbacks for each predictive models in different business settings;
5. evaluate the predictive models using different metrics and explain these in layman's terms;

C. Communication, ICT and numeracy skills:
1. apply state-of-the-art data visualization, data transformation, and predictive modelling in the statistical programming language Python;
2. solve a predictive modelling case from scratch;
3. develop reproducible R code that can be used in decision making;

D. Generic Cognitive Skills:
1. demonstrate report writing skills;
2. demonstrate presentation skills;
3. demonstrate business understanding and problem solving skills;
4. demonstrate awareness of group dynamics.
KeywordsNot entered
Course organiserDr Johannes De Smedt
Tel: (0131 6)51 1046
Course secretaryMiss Lauren Millson
Tel: (0131 6)51 3013
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