This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...

*Note: This course description is only applicable for the Computer Science Post-Baccalaureate program. Additionally, students must always refer to course syllabus for the most up to date information.

This campus-based module is led by Stephen Walters. It runs in the Autumn semester and is worth 15 credits. This module introduces students to the basic concepts and techniques of medical statistics, ...

This course introduces students to statistics and quantitative information. The course surveys probability theory, hypothesis testing, descriptive statistics and visualizations, and inferential ...

The purpose of the course is to introduce the statistical methods that are critical in the performance analysis and selection of information systems and networks. It includes fundamental topics as ...

The field of data analytics is developing rapidly. With the rise of ever larger and more specialised datasets, it’s essential to understand how to collect, handle, evaluate and interpret data to ...

Introduction to Big Data (DT102) provides the fundamentals needed to allow organizations to turn data into actionable information. Starting with an overview of big data, which covers key enterprise ...

Introduction to handling and analysis of digital geospatial data. Operation of GIS software, including collection, processing and understanding of data, production of maps and geospatial projection ...

Course planning information Course notes Access to a Computer with Excel is required. Expected prior learning Students must have a good grounding in basic maths. It is strongly recommended that ...

This course aims to introduce participants to the methodology of systematic reviews and meta-analysis. It is taught by a team of systematic reviewers, research synthesis methodologists, information ...