*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.

Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness; tests of simple and composite hypotheses, linear models, and multiple regression ...

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

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, ...

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 module will introduce students to basic concepts and techniques such as hypothesis testing and confidence interval estimation in statistics. Students will learn some simple statistical methods and ...