Postgraduate Course: Data Mining (CMSE11118)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 15 |
ECTS Credits | 7.5 |
| Summary | 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. |
| Course description |
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.
Syllabus
Overview of data mining
Data visualisation and pre-processing
Data mining in practice
Models and patterns
Introduction to data mining using SPSS and other software
Classification trees
Predictive modelling
Descriptive modelling
Classification models
Clustering
Student Learning Experience
Students will participate in lectures where they will be introduced to the principles and techniques involved in data mining, engage in discussion inside and outside of the classroom, complete practical exercises which will reinforce the ideas discussed in lectures, engage in a coursework group project on applications of data mining techniques to common business problems, perform independent reading and research and critically reflect on their own learning experiences.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Business Statistics and Forecasting (CMSE11080)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For Business School PG students only, or by special permission of the School. 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 2017/18, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
150
(
Lecture Hours 16,
Supervised Practical/Workshop/Studio Hours 5,
Summative Assessment Hours 2,
Revision Session Hours 2,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
122 )
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| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Assessment of this course is through an exam (weighted 80%) and a project (weighted 20%).
The coursework will be a group project based on practical applications of data mining techniques to business problems. The work will be presented as a poster with a short oral presentation. Further details will be distributed on Monday 22nd February.
The coursework must be submitted by 4pm on Thursday 18th March. An electronic copy of the poster and any supporting material must be submitted electronically via TURNITIN on Learn.
The degree exam will be in the April/May diet of examinations (with the exact date being set by the University during the second semester). |
| Feedback |
Feedback on formative assessed work will be provided within 15 working days of submission, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which has been made clear to students at the start of the academic year.
Students will gain feedback on their understanding of the material when they perform computer lab exercises. Students may ask questions in Lectures to assess their knowledge. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | Data Mining | 2:00 | |
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 disuss 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 linear regression, linear classifiers, decision tree models and clustering.
- Communicate technically complex issues coherently and precisely.
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Reading List
Han, J., Kamber, M. and Pei, J (2012) Data Mining: Concepts and Techniques. Morgan Kaufmann.
Hand, D., Mannila, H. and Smyth, P. (2001) Principles of Data Mining. MIT Press: Massachusets.
It covers a large amount of ground and does not focus too much on the Computer Science side of Data Mining. It does include a certain amount of the mathematics underpinning Data Mining; however, students should not be too daunted by this as we will be working through the relevant material in lectures. I would recommend examining the Hub copy of this text before purchasing as you may consider it to be too theoretical to be of immediate use.
Witten, I. H. and Frank, E. (2005) Data Mining: Practical machine learning tools and techniques (2nd ed.) Margan Kaufmann: USA.
Duda, R. O., Hart, P. E. and Stork, D. G. (2001) Pattern Classification (2nd ed.) Wiley-Interscience: USA.
Duda et al, provides a large amount of information on clustering techniques and so is useful for one of the lectures. |
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 mathematical specifications of several commonly used 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. |
| Keywords | Mark-DM |
Contacts
| Course organiser | Dr Galina Andreeva
Tel: (0131 6)51 3293
Email: |
Course secretary | Miss Ashley Harper
Tel: (0131 6)51 5671
Email: |
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