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

Postgraduate Course: Data Mining 1 (CMSE11459)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThis 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 Academic 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
Resampling Methods

Student Learning Experience
Students will over the underlying principles of fundamental data mining techniques and focus on their practical implementation.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2020/21, Not available to visiting students (SS1) Quota:  None
Course Start Block 3 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 10, Seminar/Tutorial Hours 4, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 84 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Coursework -
Assesses LO1, LO2, LO3, LO4 (100%)

This will be a group work with individual submissions. Students will be required to propose a topic as a group, with every member working on a different research question within that topic.
Feedback Students will receive the mark and the feedback on their work within 15 working days after their submission.
No Exam Information
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 discuss 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 regression and classification.
  4. Communicate technically complex issues coherently and precisely.
Reading List
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2013) An Introduction to Statistical Learning with Applications in R.

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