Postgraduate Course: Data Mining 1 (CMSE11459)
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 fundamental data mining techniques, with a focus on its use and value along with a taxonomy of these fundamental data mining techniques. |
| Course description |
This course is designed to give students an overview of 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 data mining techniques as well as their practical aspects, rather than the underlying rigorous mathematics and algorithmic details of the techniques.
Content outline:
Introduction to Statistical Learning
Regression I
Regression II
Classification
Resampling Methods
Student Learning Experience:
Students will over the underlying principles of fundamental data mining techniques and focus on their practical implementation.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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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 3 (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
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| 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 regression and classification.
- 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.
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Additional Information
| Graduate Attributes and Skills |
Cognitive Skills:
After completing this course, students should be able:
- to critically discuss and explain the benefits and limitations of different data mining techniques;
- to present and describe the principles of several fundamental 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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