Predictive Analytics module (BU52710)

Learn the techniques and skills for predictive modelling, covering regression, classification, model evaluation and machine learning to support business decisions

Credits

Module code

BU52710

Level

Semester

Semester 2

School

School of Business

Discipline

Economics

The module starts with the statistical foundations: distributions, inference, and regression. The models developed in the module include ordinary least squares, logistic models, and supervised machine learning models such as Decision Trees and ensemble models. The module also investigates the factors involved when having to choose among competing models.

R (or a similar tool) is used throughout, giving you hands-on experience with analytical software used in industry. Assessment is split between a 2,500-word coursework report (50%) and a two-hour lab based examination (50%).

What you will learn

In this module, you will:

Understand the fundamental principles and techniques of predictive analytics
Apply regression, time series, and supervised learning methods to business datasets
Evaluate and compare model performance using appropriate measures of forecast accuracy
Use R or similar analytical software to process and analyse large datasets
Communicate complex analytical insights clearly to technical and non-technical audiences

By the end of this module, you will be able to:

Develop a deep understanding of fundamental principles and techniques in predictive analytics
Understand the practical applications of predictive analytics across industries
Use advanced analytical tools such as R to process, analyse, and present insights from large datasets
Evaluate different data models and machine learning algorithms for forecasting and classification
Communicate complex analytical insights clearly to stakeholders