The context for the Predictive Analytics module is management in complex, interconnected, data-driven environments.
Forecasting is a fundamental business skill. Forecasts of the future are used in all areas of business, from operations and finance to marketing and entrepreneurship. Predictive analytics is about using data to forecast uncertain quantities and events.
Predictive Analytics introduces students to three key topics in predictive analytics: time series, regression, and ensembling, and develops students’ ability to think like a data scientist.
The module builds on ideas and tools introduced in MSING054 Mathematical Foundations of Business Analytics and MSING055 Programming for Business Analytics, including R, statistical software used by the world’s leading data scientists.
During the module, students will work with example data sets to experience the stages of the data science process: they will visualise data, propose models that might fit the data, choose a best-fit model, use that model to make predictions, and test those predictions against new realisations.
Cases that illustrate the applications of data science to business problems will be used, as well as in-class forecasting competitions.
The aims of the Predictive Analytics module are:
- To develop a rigorous understanding of how data is used to support the practice of management and strong data-based reasoning and computational thinking skills.
To introduce students to predictive analytics techniques used by organisations to forecast uncertain quantities and events.
Upon successful completion of the module, students will be able to:
- Understand how organisations use predictive analytics to forecast uncertain quantities and events.
- Understand the stages of the data science process.
- Understand how to use time series, regression, and ensembling techniques to develop business forecasts.
- Characterise and critically assess the quality of data sets and their limitations in the context of data-driven decision-making.
- Understand how to source, cleanse and manage data sets.
- Use selected data analytics tools to manipulate and visualise data.
Use selected data analytics tools to develop predictive analytics models.
60% individual coursework; 40% group coursework