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A blurb about Modern Regression:
The material in this course concentrates on methods for the analysis of data. The emphasis is on description, validation, and interpretation. Topics include exploratory data analysis, statistical computing, and regression analysis. Real-world examples will be drawn from engineering and the various physical and social sciences. Students will do projects and write reports.
This is "Advanced Data Analysis" but I think it's the same as Topics:
This is a project course in data analysis. Students work in teams on a semester-long data analysis problem. Past projects have been drawn from current research in neuroscience, genetics, finance and psychology. Data analysis requires the application and extension of statistical methods and computing skills learned in 36-401 [Modern Regression]. A key objective of the course is to expose students to the variety of challeges faced by the data analyst. Students research the scientific background of their problem, consult with subject-area scientists, and communicate their methods and results both in writing and in class presentations. At the end of the semester, each team presents a poster of their project at the "Meeting of the Minds" undergraduate research symposium.
Intro to Prob Models:
An introductory-level course in stochastic processes. Topics typically include Poisson processes, Markov chains, birth and death processes, random walks, recurrent events, and renewal theory. Examples are drawn from reliability theory, queuing theory, inventory theory, and various applications in the social and physical sciences.
That last one sounds like it's some of the stuff in the physics book.
The material in this course concentrates on methods for the analysis of data. The emphasis is on description, validation, and interpretation. Topics include exploratory data analysis, statistical computing, and regression analysis. Real-world examples will be drawn from engineering and the various physical and social sciences. Students will do projects and write reports.
This is "Advanced Data Analysis" but I think it's the same as Topics:
This is a project course in data analysis. Students work in teams on a semester-long data analysis problem. Past projects have been drawn from current research in neuroscience, genetics, finance and psychology. Data analysis requires the application and extension of statistical methods and computing skills learned in 36-401 [Modern Regression]. A key objective of the course is to expose students to the variety of challeges faced by the data analyst. Students research the scientific background of their problem, consult with subject-area scientists, and communicate their methods and results both in writing and in class presentations. At the end of the semester, each team presents a poster of their project at the "Meeting of the Minds" undergraduate research symposium.
Intro to Prob Models:
An introductory-level course in stochastic processes. Topics typically include Poisson processes, Markov chains, birth and death processes, random walks, recurrent events, and renewal theory. Examples are drawn from reliability theory, queuing theory, inventory theory, and various applications in the social and physical sciences.
That last one sounds like it's some of the stuff in the physics book.
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