Overview

Indicative content includes:

• Data Marts and Warehouses and how they manage data collections.
• Big Data and the 4 Vs (Volume, Variety, Velocity and Veracity).
• Data Quality, the meaning and methods to raise it.
• Data Mining, extracting meaning from data.
• Analysis Tools and processes used to describe and evaluate data collections.

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 construct 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

• Construct an appropriate data warehouse for a given data set.

• Critically discuss the various methods for storage large or Big Data data sets.

• Demonstrate skills to suitably sanitise a given data set.

• Explain the definitions and examples of the various types of analytical data.