Postgraduate Course: Predictive Analytics and Modelling of Data (CMSE11428)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course provide students with the fundamentals of supervised and unsupervised learning models to predict real-world business applications.
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| 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, 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
Weekly lectures and hands-on programming exercises in Python which enables students to implement the methodologies covered in class.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 10,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
164 )
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| Assessment (Further Info) |
Written Exam
40 %,
Coursework
60 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
60% coursework (group) - assesses all course Learning Outcomes
40% exam (individual) - assesses all course Learning Outcomes
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| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on the assessments within agreed deadlines. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S1 (December) | Predictive Analytics and Modelling of Data - Exam | 2:120 | |
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 predictions
- Formulate managerial guidelines and make recommendations
- Communicate predictions effectively and efficiently to a critical audience
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Reading List
Applied Predictive Modelling, Springer, Max Kuhn
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Additional Information
| Graduate Attributes and Skills |
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Convey meaning and message through a wide range of communication tools, including digital technology and social media; to understand how to use these tools to communicate in ways that sustain positive and responsible relationships.
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Cognitive Skills
After completing this course, students should be able to:
Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to
quality.
Knowledge and Understanding
After completing this course, students should be able to:
Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Antonia Gieschen
Tel:
Email: Antonia.Gieschen@ed.ac.uk |
Course secretary | Mr Ewan Henderson
Tel:
Email: ehende2@ed.ac.uk |
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