Overview

Indicative content includes:

• Data pre-processing – Looking at how data mining techniques are involved with cleaning data, improving the quality of data and the preparation of data, to make it meaningful.
• Data mining – Looking at how machine learning techniques, such as supervised machine learning and unsupervised machine learning are used to develop programs without the need for instructions.
• Data Analysis – Writing a bespoke programme to analyse a given data set using industry standard packages (e.g. NumPy or SciPy).
• Data Evaluation – Interpreting your data to test your programme outcomes.

Assessment Strategy

-threshold -Equivalent to 40%.Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

-good -Equivalent to the range 60%-69%.Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

-excellent -Equivalent to the range 70%+.Assemble critically evaluated, relevant areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

Learning Outcomes

• Employ appropriate analytical tools to conduct data analysis.

• Evaluate methods and techniques used in data analytics.

• Evaluate results from the use of data analytical tools.

• Illustrate the core concepts and process of data analytics.