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 |
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:
Weekly lectures and hands-on programming exercises in Python which enables students to implement the methodologies covered in class.
Tutorial/seminar hours represent the minimum total live hours - online or in-person - a student can expect to receive on this course. These hours may be delivered in tutorial/seminar, lecture, workshop or other interactive whole class or small group format. These live hours may be supplemented by pre-recorded lecture material for students to engage with asynchronously.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2021/22, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 1 |
| Course Start Date |
20/09/2021 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 20,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
174 )
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| Additional Information (Learning and Teaching) |
Seminar/Tutorial hrs are the min total live hrs, online or in-person, students can expect to receive
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| Assessment (Further Info) |
Written Exam
40 %,
Coursework
60 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
60% coursework (individual) - assesses all course Learning Outcomes
40% exam (individual) - assesses all course Learning Outcomes
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| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S1 (December) | CMSE11428 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 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
Resource List:
https://eu01.alma.exlibrisgroup.com/leganto/public/44UOE_INST/lists/26181455450002466?auth=SAML |
Additional Information
| Graduate Attributes and Skills |
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 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 codes 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. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr André Santos
Tel: (0131 6)50 8074
Email: Andre.Santos@ed.ac.uk |
Course secretary | Ms Emily Davis
Tel: (0131 6)51 7112
Email: Emily.Davis@ed.ac.uk |
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