OS3307 Modeling Practices for Computing

An applied course in modeling and understanding systems where randomness plays a significant role. Topics include basic probability and statistics, queuing models, Monte Carlo and discrete-event simulation, least squares curve fitting, and elements of statistical design of experiments. The focus will be on applications of these techniques in a computer science context.

Prerequisite

Discrete Math, Intro Programming

Lecture Hours

4

Lab Hours

1

Course Learning Outcomes

• Recognize basic principles of probability and how to apply them in a computer science context
• Demonstrate knowledge of techniques commonly used in statistical inference
• Understand the relationship between probability and statistics, and how it governs good scientific practice in data analysis
• Develop skills in applying simulation, design of experiments, and queuing models to solve problems in a computer science context