Data Science

This course unit has two objectives. The first is to introduce the student to a range of fundamental, non-trivial algotithms, and to the techniques required to analyse their correctness and running-time.

The second is to present a conceptual framework for analysing the intrinsic complexity of computational problems, which abstracts away from details of particular algorithms.

• Demonstrate awareness of the “Data Science Process” by describing qualitatively how it would apply in a given situation. 
• Demonstrate awareness of need for data cleaning descriptively and by doing elementary data cleaning and preparation in the laboratory.  
• Demonstrate ability to measure and express uncertainty from a set of data and quantities derived from that data. 
• Demonstrate ability to choose and build appropriate models of different datasets. 
• Demonstrate ability to evaluate the quality of a model of a dataset. 
• Demonstrate the ability compare different models of a dataset and models of different dataset in order to draw statistically sound conclusions about hypotheses or claims from the data. 
• Demonstrate ability to use python tools to: read and write data sets to and from files, produce descriptive statistics and draw conclusions from these, produce graphical visualisation and draw conclusions, perform basic statistical tests including the difference between means, and perform a simple machine learning experiment by building an email spam filter using a naive Bayes classifier.

Lectures and coursework reported via Jupyter notebooks in Python.

To be determined

Please note: AI forms part of the assessment method for this course unit. The unit coursework requires students to implement, train, and evaluate machine learning models, learning techniques such as Naive Bayes and Regression (Category D: AI-integrated). For more information, please see: AI in Teaching & Learning Policy.