Overview

Indicative content includes:

• An introduction to data science.
• How to represent and import data into Python for manipulation.
• How to use Python libraries such as Numpy and Pandas to analyse and manipulate data.
• How to use the Python Matplotlib library for data visualisation.

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

• Apply key principles and methods from Data Science.

• Build non-trivial programs that import and manipulate data in Python.

• Build programs that analyse and manipulate data using Python libraries such as Numpy and Pandas.

• Build programs that use the a Python library, such as Matplotlib, for data visualisation.