Module Title:   Data Mining

Module Credit:   20

Module Code:   CM-1072D

Academic Year:   2015/6

Teaching Period:   Semester 2

Module Occurrence:   A

Module Level:   FHEQ Level 7

Module Type:   Standard module

Provider:   Computer Science

Related Department/Subject Area:   School of Electrical Engineering & Computer Science

Principal Co-ordinator:   Professor Daniel Neagu

Additional Tutor(s):   -

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 description and critical evaluation of data analysis, data cleaning, data representation and data manipulation issues for mining, data processing, pattern or correlation exploration and data mining issues. Three types of relationships are addressed: classes, clusters, associations, as well as presenting data in a useful format (data visualization).

Learning Teaching & Assessment Strategy:
A combination of lectures, seminars, tutorials, lab sessions and directed study. Principles and theories will be introduced and explored in formal lectures, practised and discussed in tutorials, and applied and demonstrated in laboratory classes. Practical skills will be developed in tutorial and laboratory sessions. Oral feedback will be given during labs and tutorials. This will then be followed by further reading and practical work undertaken by the students with online support and feedback.
Coursework - overview, analysis, evaluation and presentation of data mining techniques and the results for a selected topic, will assess the application of practical skills to the knowledge base of the module and the wider learning outcomes expressed in the descriptor, addressing selection and implementation of suitable techniques such as supervised, unsupervised learning, association algorithms on a given data set and evaluation of the issues involved and results.
Supplementary coursework will involve repairing deficiencies in the original submissions.

Lectures:   24.00          Directed Study:   146.00           
Seminars/Tutorials:   6.00          Other:   0.00           
Laboratory/Practical:   24.00          Formal Exams:   0.00          Total:   200.00

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

critically analyse and evaluate the theory and practice of data analysis options, data mining approaches and results.

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

Analyse data, critically evaluate possible solutions, choose, design and implement data mining algorithms, and apply the data mining techniques to 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 or real world examples.

  Coursework   100%
 
  Report (4000 words or equivalent): on data mining techniques and the results for a selected topic.

Supplementary Assessment:
As Original

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, Text Mining). Data Mining Methodologies. Using Data Mining in the analysis of scientific and practical data.

Version No:  1