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.
Content outline:
- 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
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| Academic year 2022/23, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
88 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% coursework (individual) - assesses all course Learning Outcomes
|
| Feedback |
Formative: TBC
Summative: TBC |
| No Exam Information |
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 |
Cognitive Skills:
After completing this course, students should be able to:
- to critically discuss and explain the benefits and limitations of different data mining techniques;
- to present and describe the principles of several advanced data mining techniques.
Subject Specific Skills:
After completing this course, students should be able to:
- develop the ability to define a data mining problem, evaluate methodologies and propose solutions;
- learn how to interpret and validate the result of an application of data mining;
- be able to use a software package to implement data mining solutions, including data analysis, modelling and validation;
- develop computing skills required for data mining;
- learn how to present data mining results and communicate technical issues coherently.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Aakil Caunhye
Tel:
Email: |
Course secretary | Ms Emily Davis
Tel: (0131 6)51 7112
Email: |
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