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.
Outline Content
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 2023/24, 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: Feedback will be provided throughout the course.
Summative: Feedback will be provided on the assessment within agreed deadlines. |
| 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 |
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. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Aakil Caunhye
Tel:
Email: |
Course secretary | Miss Jen Wood
Tel: (0131 6)50 8335
Email: |
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