Berkeley Statistics Classes
Introduction to Statistical Computing (Statistics 243)
Fall 2023, 2022, 2021, 2020, 2019, 2018, 2017, 2015, 2014, 2013, 2012, 2011: I am again teaching this graduate-level introduction to statistical computing. The course is an intro to statistical computing. Up through 2022 this was taught using R and starting in 2023 is is taught in Python. The course covers both programming concepts and statistical computing concepts. Programming concepts include data structures, flow control, functions and variable scope, regular expressions, matrix manipulations, debugging, and parallel programming. Statistical computing topics include data and text manipulation, databases, numerical linear algebra, simulation studies and Monte Carlo methods, numerical optimization, and visualization. I expect students to be comfortable with calculus, linear algebra, statistics, and probability. All materials are available from GitHub, including a complete set of course notes and accompanying set of demo code.
Bayesian Statistics (Statistics 238)
Fall 2016: I taught a new graduate-level course on Bayesian statistics. The idea is for this to be a regular part of the department course offerings. The course focuses on concepts, methodology, and computation, with some limited theory and some material covered in the context of real data applications. During the first two-thirds of the course, we will focus on the core of Bayesian statistics, with topics including Bayes' theorem, general principles (likelihood, exchangeability, de Finetti's theorem), prior distributions, simple models, hierarchical models, methods of inference (exact, approximations, Monte Carlo strategies), Markov chain Monte Carlo (MCMC) and other computation strategies, model diagnostics and model selection. The second part of the course will introduce the Bayesian approach to a range of important statistical models and situations including GLMs and GLMMs, high-dimensional data and multiple testing, meta-analysis, nonparametrics, missing data, and causal inference.
Statistical Inferences for Social and Life Scientists (Statistics 131A)
Spring 2011, Fall 2010: I taught an intro course covering both probability and statistics, as well as introducing R. The audience was primarily biology, environmental science and public health majors.
Concepts of Statistics (Statistics 135)
Spring 2010: I taught this course, a first course on statistical theory for advanced undergraduates. We used DeGroot and Schervish, Probability and Statistics, 3rd edition.
Linear Modelling: Theory and Applications (Statistics 151A)
Fall 2009: I taught this course for advanced undergraduates covering linear models (regression and ANOVA) and generalized linear models. I used John Fox's Applied Regression Analysis and Generalized Linear Models. Material available on bSpace to UCB affiliates. Unfortunately I don't know how to provide more general access.
Harvard Biostatistics Classes
Bayesian Methodology in Biostatistics (Biostatistics 249)
Spring 2009: I again taught Bio249. See the course website for information.
Fall 2007: I taught Bio249, the first offering of the course in several years. The course website is accessible to the public (hopefully).
Introduction to R (Biostatistics 503)
This is a winter session (5 sessions, 3 hours each) course, introducing R. No background in R or statistics is assumed, but a small amount of programming experience (e.g., SAS, C) may be helpful as well as a basic background in statistics (e.g., knowledge of regression). The course has been taught by a series of postdocs/research associates: Jess Mar (DFCI Biostatistics), Aedin Culhane (DFCI Biostatistics), Subha Guha (HSPH Biostatistics) and Jarek Harezlak (HSPH Biostatistics).
The course was offered in 2006, 2007, 2008, and 2009. The 2009 course website is accessible to the public (hopefully).
Introduction to Geographical Information Systems Using ArcGIS (Biostatistics 504)
This is an introductory, 5-session (3.5 hours each) winter session course on GIS, taught by Sumeeta Srinivasan of FAS and the Center for Geographic Analysis.
The course was offered in 2007, 2008, and 2009. The 2009 course website is accessible to the public (hopefully).
Spatial Statistics for Public Health and Social Inquiry (Biostatistics 283/Statistics 155)
Spring 2007: Rima Izem (Harvard Statistics) and I co-taught a full semester spatial statistics class aimed at non-statistics graduate students and advanced undergraduates. Our aim was to provide an applied introduction to spatial statistics, following the template of Biostatistics 226, Applied Longitudinal Analysis. The course website is accessible to the public (hopefully).
Unfortunately there are no plans at the present to offer this course again.
Spatial Statistics for Health Research (Biostatistics 284)
Fall 2004: I co-taught a half-semester course on spatial statistics in public health research with Louise Ryan and Yi Li. I taught the third of the course dealing with point data. This included three lectures, one lab, and one day discussing code and papers on hierarchical modelling.
The lectures drew on material from a number of sources, giving an introduction to exploratory spatial analysis, covariance modelling and fitting, controlling for spatial correlation in regression analyses, kriging from classical and Bayesian perspectives, other methods for spatial smoothing including thin plate spline methods and radial basis functions, spatial and hierarchical modelling, and spatial design. This material has been superceded by the material in Biostatistics 283 (see above).
Last updated: November 2023.