Compulsory modules

Data Mining is a process of extracting information and patterns from the data, and provide insight and understanding to inform decision making. This module introduces key concepts of data mining process and techniques including data pre-processing, feature engineering, clustering and classification.
Students will gain practical experience in the overall data mining process, and in using different techniques to identify patterns and extract information from publicly available data. They will also learn to present their findings effectively in the form of a report.

The aims of this module are to:

  • Introduce the facets of research from broad research philosophy to detailed data collection.
  • Develop critical analysis skills across a range of different sources.
  • Introduce ethical thinking into the development of an appropriate research methodology.

Optional modules (choose three)

This module aims to introduce students to big data analytics and data visualisation tools and techniques that are widely used for business intelligence and other real-world applications. The module will enable students to solve a variety of complex data centred problems using computer software visualisation tools such as Tableau, Google Data Studio, and Microsoft Power BI.

Students will be equipped with the knowledge and experience needed to communicate complex concepts to a non-technical audience using interactive graphs and charts in the form of dashboards and worksheets to gain data insights.

Students will also learn about the importance of appropriate and responsible data use in government, healthcare and other sectors.

This module aims to provide students with knowledge and experience in modern machine learning techniques suitable for solving various AI challenges using real-world datasets. With an understanding of conventional machine learning and deep learning concepts and techniques, students will gain the ability to pre-prepare data, design machine learning models, and evaluate these models using suitable evaluation measures.

This module introduces the use of statistical models for data summary and prediction using the R or Python programming language and R packages.