SCS2207 Probability Theory and Statistical Computing

Students are introduced to the concepts and methods of statistics, including variability, randomness, and probability. A statistical software program is used to facilitate the analysis of data sets and the understanding of statistical concepts, and to carry out simulation of experiments. Many jobs or professions require that objective decisions be made based on statistical data; students are taught how to collect, analyze, and interpret data correctly. Students are also shown how to clearly and accurately present data to others. The course also introduces students to a range of computational techniques that are important to statistics. The topics covered include numerical linear algebra, numerical optimization, graphical techniques, numerical approximations, numerical integration and Monte Carlo methods. Use of statistical packages (Splus, SAS) are also illustrated.

Course Objectives

• To review statistical theory and inference, to introduce computer science students to statistical thinking and to gain proficiency in the correct application of various statistical tools to data modeling and analysis.

Course Outcomes
After completing this course, the student should be able to:

• Distinguish between quantitative and categorical data and know which graphical and tabular techniques to apply to each
• Discuss issues associated with collecting and interpreting data from sample surveys and polls.
• Distinguish between an experiment and an observational study
• Describe what is meant by the central limit theorem, and understand its relevance to statistical inference
• Determine appropriate sample sizes for estimating an unknown population proportion or mean