- Faculty of Engineering Sciences
- Teaching department
- UCL School of Management
- Credit value
- Module is only available to students on the following programmes: - MSc Business Analytics - School of Management Affiliates
Alternative credit options
There are no alternative credit options available for this module.
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 MSIN0096 Statistical Foundations of Business Analytics and MSIN0143 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.
Module deliveries for 2020/21 academic year
Intended teaching term: Term 2 Postgraduate (FHEQ Level 7)
Teaching and assessment
- Mode of study
- Methods of assessment
60% Coursework (Individual) (2,000 words)40% Coursework (Group) (2,000 words)
- Mark scheme
- Numeric Marks
- Number of students on module in previous year
- Module leader
- Mr Alastair Moore
This module description was last updated on 5th March 2020.