**Description**: A hands-on introduction to probability, statistical inference, regression, Markov chains, queuing theory. Use of an interactive statistical graphics environment such as R.

**Objectives**

- Students understand basic probability theory
- Students understand basic statistical inference
- Students can model problems using probability and statistical tools

**Course Learning Outcomes**: See course objectives.

**Program Learning Outcomes**

- a. Students can apply knowledge of computing and mathematics appropriate to the discipline
- j. An ability to use and apply current technical concepts and practices in the core information technologies. [BA IT only]

**Prerequisites**: 241 and 311, or consent.

**Textbook(s)**: An Introduction to Stochastic Modeling (3rd edition). Howard M. Taylor and Samuel Karlin. Academic Press. isbn 0-12-684887-4.

**Grading**

- Homeworks Assignments (50%)
- Midterm Exam (25%)
- Final Exam (25%)

**Schedule**: Week 1: Introduction and Math refresher
Week 2-3: Probability theory
Week 4-5: Basic statistics
Week 6-7: Bayesian reasoning
Week 8-10: Linear regression, regularization, ridge regression, LASSO
Week 11: Beyond linear regression models
Week 12-14: Advanced statistics
Week 15: Final class project