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. 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. |
| Course description |
Not entered
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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 |
Course Delivery Information
|
| Academic year 2014/15, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
150
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 24,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
101 )
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| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Assessment of this course is through an exam (weighted 80%) and a project (weighted 20%). |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | Data Mining | 2:00 | |
Learning Outcomes
A. Knowledge and understanding of
* the value and application of data mining for business and customer relationship management;
* the variety of methods constituting data mining including data analysis, statistical methods, machine learning and model validation techniques;
* the foundations of modelling approaches such as linear regression, linear classifiers, decision tree models and clustering.
B. Intellectual skills
Students will be able:
* 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.
C. Practical and transferable skills
Students will:
* 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.
D. Transferable skills
By the end of the course students will be expected to:
* be able to communicate technically complex issues coherently and precisely;
* have acquired lifelong learning skills and personal development so as to be able to work with self-direction.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | DM |
Contacts
| Course organiser | Dr Daniel Black
Tel: (0131 6)51 1491
Email: Dan.Black@ed.ac.uk |
Course secretary | Miss Ashley Harper
Tel: (0131 6)51 5671
Email: Ashley.Harper@ed.ac.uk |
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