Data analytics is an area of increasing importance and interest to organisations. Data analytics techniques offer huge potential in the creation of new knowledge products and services and the enhancement of existing products and services. Rather than focus on the details of specific data analytics techniques, this module addresses the application of data analytics techniques (from simple descriptive analytics techniques to more complex predictive analytics techniques) to real business problems.
Introduction to data mining and applications of data analytics
- Data, Information, Knowledge
- Modelling an activity
- Framing a business model
- Developing a model
- Deploying a model
- Communicating results
- Case studies
Data Analytics Life Cycle
- Stages of a data analytics project
- Outputs of each stage
- Roles and responsibilities of people involved in data analytics
Data Management
- Introduction to Data Management
- Role of organisations and stakeholders
- Data governance and data security
- Meta-data management
Data Analytics Techniques
- Predictive modelling techniques (e.g. regression, nearest neighbour, decision trees, neural networks, support vector machines)
- Pattern discovery (e.g. association rule mining, clustering algorithms)
- Evaluation of data analysis techniques
- Visualisation of data insight results
- Legal & ethical issues in data analytics
- Tools Data analysis tools
Data modelling tools
The module is designed to be delivered within a blended learning model, employing mixed modes (online and face to face) of learning, teaching and assessment.
This module will employ teaching methods and learning situations in the traditional roles such as lectures, seminars and tutorials, as well as more innovative, student-based learning methods such as problem solving in groups for both theoretical and practical situations.
Students will be encouraged to be proactive in their approach to learning through the use of case studies and simulation exercises, working independently and in groups. In some cases students will be expected to use computer-based learning material to supplement studies.
The practical element of the module will be supported through the medium of supervised and independent practical sessions. Students will be able to explore the characteristics, advantages and limitations of approaches learnt through their application to suitable case studies and simulation exercises. Where appropriate, students will provide feedback from group research through cascading the knowledge to peers and through presentations. In-class discussions review of leading research papers in each topic covered will also contribute towards the practical content.
Most appropriate distribution methods will be used to distribute materials to students, between students and from students, e.g. a VLE, blogs, Twitter, a forum.
Students will be expected to develop independence in, and responsibility for their own learning.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |