Module Title:   Data Mining

Module Credit:   10

Module Code:   CM-0425M

Academic Year:   2015/6

Teaching Period:   Semester 2

Module Occurrence:   A

Module Level:   FHEQ Level 7

Module Type:   Standard module

Provider:   Sch of Computing Informatics & Media (not current)

Related Department/Subject Area:   SCIM (Dept of Computer Science)

Principal Co-ordinator:   Dr D Neagu

Additional Tutor(s):   Mr M J Ridley

Prerequisite(s):   None

Corequisite(s):   None

Aims:
To develop a thorough understanding of the theory and practice of advanced data processing techniques. This will include data storage and data manipulation issues, data analysis, data processing and data mining issues.

Learning Teaching & Assessment Strategy:
A combination of lectures, seminars, tutorials, lab sessions and directed study. Concepts, principles and theories explored in formal lectures, practised and demonstrated in laboratory classes and practised and discussed in tutorials. Practical skills developed in laboratory sessions. Oral feedback is given during labs and tutorials. Coursework will assess the application of practical skills to the knowledge base of the module and the wider learning outcomes expressed in the descriptor. Supplementary coursework will involve repairing deficiencies in the original submission.

Lectures:   12.00          Directed Study:   70.00           
Seminars/Tutorials:   6.00          Other:   0.00           
Laboratory/Practical:   12.00          Formal Exams:   0.00          Total:   100.00

On successful completion of this module you will be able to...

demonstrate a knowledge and understanding of the theory and practice of data mining approaches.

On successful completion of this module you will be able to...

analyse data, critically evaluate possible solutions, design and implement data mining algorithms in specific case studies.

On successful completion of this module you will be able to...

critically analyse, evaluate and present in writing case studies of data mining benchmarks.

  Coursework   100%
 
  Report (2000 words or equivalent): overview, analysis and evaluation of data mining techniques for a selected topic

Outline Syllabus:
Data Mining. Data, Information and Knowledge. Data Preparation (Statistical Evaluation, Data Selection, Data Cleaning, Transformation of Data). Data Mining Algorithms (Association, Classification, Clustering, Time Series Analysis). Data Mining Methodologies. Using Data Mining in the analysis of scientific data.

Reading List:

Database systems : a practical approach to design, implementation, and management
By Connolly, Thomas M., Begg, Carolyn E.
Harlow : Addison-Wesley, 2009.
Edition: 5th ed.
Classmark: L 681.3.07 CON

Data mining : practical machine learning tools and techniques
By Witten, I. H. (Ian H.), Frank, Eibe.
San Francisco, Calif. : Morgan Kaufmann ; 2005.
Edition: 2nd ed.
Series: The Morgan Kaufmann series in data management systems.
L 681.3.072 WIT

Version No:  1