A semester-long study of topics in Statistics. Topics and emphases will vary according to the instructor. This course may be repeated for credit with different topics. See the details in the schedule ...

Applied Statistics is the implementation of statistical methods, techniques, and theories to real-world problems and situations in several fields, such as science, engineering, business, medicine, ...

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 ...

Foundations of Algebra emphasizes computing with whole numbers and fractions, decimals, and integers; solving word problems with whole numbers, fractions, decimals and integers; solving basic linear ...

*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.

Contents: The R programming language is used for statistical data analysis and visualization. R and the R Studio development environment are a globally used tool set for statistical analysis and data ...

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

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 ...