Postgraduate Course: Data Mining 2 (CMSE11460)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course is designed to give students an overview of advanced data mining techniques, with a focus on its use and value along with a taxonomy of these data mining techniques. |
| Course description |
This course is designed to give students an overview of advanced data mining, with a focus on its use and value along with a taxonomy of data mining techniques.
The course provides students with an appreciation of the uses of data mining software in solving business decision problems. Students will gain knowledge of theoretical background to several of the commonly used data mining techniques and will learn about the application of data mining, as well as acquiring practical skills in the use of data mining algorithms. The course intends to focus in large part on the principles behind different advanced data mining techniques as well as their practical aspects, rather than the underlying rigorous mathematics and algorithmic details of the techniques.
Outline Content
- Model Selection and Regularisation
- Nonlinear Models
- Decision Trees
- Support Vector Machines
- Unsupervised Learning
Student Learning Experience
Students will cover the underlying principles of advanced data mining techniques and focus on their practical implementation.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Data Mining 1 (CMSE11459)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically evaluate the value and application of data mining for business and customer relationship management.
- Critically discuss the variety of methods constituting data mining including data analysis, statistical methods, machine learning and model validation techniques.
- Understand and apply the foundations of modelling approaches such as decision trees and support vector machines.
- Communicate technically complex issues coherently and precisely.
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Reading List
| Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2013) An Introduction to Statistical Learning with Applications in R. |
Additional Information
| Graduate Attributes and Skills |
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
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.
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.
Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore and solve them responsibly.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Stavros Stavroglou
Tel: (0131 6)51 1603
Email: Stavros.Stavroglou@ed.ac.uk |
Course secretary | Miss Jen Wood
Tel: (0131 6)50 8335
Email: J.Wood@ed.ac.uk |
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