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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Data Mining (CMSE11118)

Course Outline
SchoolBusiness School CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits15 ECTS Credits7.5
SummaryThis 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites 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
High Demand Course? Yes
Course Delivery Information
Academic year 2018/19, Available to all students (SV1) 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 )
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%).

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
Main Exam Diet S2 (April/May)Data Mining2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Critically evaluate the value and application of data mining for business and customer relationship management.
  2. Critically disuss the variety of methods constituting data mining including data analysis, statistical methods, machine learning and model validation techniques.
  3. Understand and apply the foundations of modelling approaches such as linear regression, linear classifiers, decision tree models and clustering.
  4. Communicate technically complex issues coherently and precisely.
Reading List
Han, J., Kamber, M. and Pei, J (2012) Data Mining: Concepts and Techniques. Morgan Kaufmann.

The main text for this course.

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.

Witten and Frank provide an alternate source of explanations for some of the material.

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.
Course organiserDr Aakil Caunhye
Course secretaryMs Emily Davis
Tel: (0131 6)51 7112
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