CM2203: Informatics

School Cardiff School of Computer Science and Informatics
Department Code COMSC
Module Code CM2203
External Subject Code 100370
Number of Credits 10
Level L5
Language of Delivery English
Module Leader Dr Sylwia Polberg
Semester Spring Semester
Academic Year 2022/3

Outline Description of Module

The aim of this module is to provide the student with and understanding of the role data mining and data quality techniques play in our lives. The students will develop a basic toolbox allowing them to use methods for learning and evaluation information. This will be paired with a consideration of the ethical implications surrounding gathering and using information in an automated manner.

 

On completion of the module a student should be able to

  1. Execute and evaluate various techniques in knowledge discovery and data mining
  2. Analyse and critically evaluate methods for assuring quality of information
  3. Appraise the ethical implications and societal risks associated with data mining and data quality assurance

How the module will be delivered

Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include:

  • on-line resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes),
  • on-line interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings)
  • face to face small group sessions (e.g. help classes, feedback sessions)

Skills that will be practised and developed

Please refer to the learning outcomes.

How the module will be assessed

The module will be assessed with three portfolios consiting of a blend of tasks, which can include online exercises, coded tasks, short essays, independent inquiry, etc.

Assessment Breakdown

Type % Title Duration(hrs)
Portfolio 40 Informatics Portfolio N/A
Portfolio 30 Informatics Portfolio N/A
Portfolio 30 Informatics Portfolio N/A

Syllabus content

Similarity measures

Knowledge discovery process

Data mining

Classification

Clustering

Association rule learning

Data quality dimensions, activities and methodologies

Ethical considerations concerning gathering and using information


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