Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

This is an introductory course on statistics and how it can help us answer the kind of questions that arise when we want to better understand the world. We will use real-world examples from the social ...

*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.

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

STATISTICS is a subject which has grown very rapidly during the last twenty years, and it is therefore inevitable that the textbook should tend to depart more and more from the traditional form. The ...

Basic statistical concepts presented with emphasis on their relevance to biological and medical investigations. Evaluation is through problem sets, quizzes embedded within asynchronous videos, use of ...

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 ...

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 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 ...