Overview

Indicative content includes:

• Data analysis methods and algorithms.
• OSEMN (Obtain, Scrun, Explore, Model, iNterpret) cycle.
• Methods for reporting data science experiments.
• Applying acquired knowledge and skills to a student-selected data set or sets.

Assessment Strategy

-threshold -Equivalent to 50%.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, relevent 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 data science techniques with a data-set.

• Evaluate the effacacy of experiments conducted.

• Report results of experiments analysing data.