berkeley statistics courses

The Statistics of Causal Inference in the Social Science. This course teaches a broad range of statistical methods that are used to solve data problems. A seminar on successful research designs and a forum for students to discuss the research methods needed in their own work, supplemented by lectures on relevant statistical and computational topics such as matching methods, instrumental variables, regression discontinuity, and Bayesian, maximum likelihood and robust estimation. Advanced Topics in Learning and Decision Making: Terms offered: Spring 2011, Spring 2010, Spring 2009, Introduction to Modern Biostatistical Theory and Practice, Terms offered: Spring 2022, Spring 2021, Fall 2019, , asymptotic linearity/normality, the delta method, bootstrapping, machine learning, targeted maximum likelihood estimation. Data 8), there is considerable demand for follow-on courses that build on the skills acquired in that class. Special topics, by means of lectures and informational conferences. Genomics is one of the fundamental areas of research in the biological sciences and is rapidly becoming one of the most important application areas in statistics. Topics include data types and structures, such as vectors, data frames and lists; the REPL evaluation model; function calls, argument matching, and environments; writing simple functions and control flow. Markov chains. Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. Statistics Research Seminar: Read More [+], Fall and/or spring: 15 weeks - 0 hours of seminar per week, Statistics Research Seminar: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Biostatistical Methods: Survival Analysis and Causality: Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine. Use a variety of approaches to problem solving Professional Preparation: Teaching of Probability and Statistics: Individual Study for Master's Candidates: Individual Study for Doctoral Candidates: Berkeley Berkeley Academic Guide: Academic Guide 2022-23. A project-based introduction to statistical data analysis. ); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc. The first course in this two-semester sequence is Public Health C240E/Statistics C245E. Comprehension of broad concepts is the main goal, but practical implementation in R is also emphasized. Statistics 133, 134, and 135 recommended, Statistical Models: Theory and Application: Read Less [-], Terms offered: Spring 2022, Spring 2021, Spring 2020 frame data science questions relevant to longitudinal studies as the estimation of statistical parameters generated from regression, Quantitative Methodology in the Social Sciences Seminar: Terms offered: Fall 2018, Fall 2017, Fall 2016, Terms offered: Spring 2021, Fall 2017, Fall 2016, Terms offered: Fall 2021, Fall 2020, Fall 2019, Advanced Topics in Learning and Decision Making, Terms offered: Spring 2022, Spring 2017, Spring 2016. The goal of this course is to better understand programming principles in general and to write better R code that capitalizes on the language's design. The R statistical language is used. The focus is on the underlying paradigms in R, such as functional programming, atomic vectors, complex data structures, environments, and object systems. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership. An introduction to time series analysis in the time domain and spectral domain. This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. experience in analyzing real world data from the social, life, and physical sciences. Grading/Final exam status: Offered for pass/not pass grade only. Terms offered: Spring 2022, Fall 2021, Spring 2021 with real data and assessing statistical assumptions. Topics in Probability and Statistics: Read More [+], Topics in Probability and Statistics: Read Less [-], Terms offered: Spring 2016, Spring 2015, Spring 2014 Directed Study for Graduate Students: Read More [+], Summer: 6 weeks - 1-16 hours of independent study per week8 weeks - 1-12 hours of independent study per week, Directed Study for Graduate Students: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. This course and Pb Hlth C240C/Stat C245C provide an introduction to computational statistics with emphasis on statistical methods and software for addressing high-dimensional inference problems that arise in current biological and medical research. Emphasis is on estimation in nonparametric models in the context of contingency tables, regression (e.g., linear, logistic), density estimation and more. The Statistics of Causal Inference in the Social Science: Quantitative Methodology in the Social Sciences Seminar. An introduction to linear algebra for data science. A deficient grade in STAT20 may be removed by taking STATW21, STAT21, or STAT N21. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra. Quantitative Methodology in the Social Sciences Seminar: Read More [+], Terms offered: Spring 2021, Fall 2017, Fall 2016 Directed Study for Undergraduates: Read More [+], Fall and/or spring: 15 weeks - 1-3 hours of directed group study per week, Summer: 6 weeks - 2.5-7.5 hours of directed group study per week8 weeks - 1.5-5.5 hours of directed group study per week, Directed Study for Undergraduates: Read Less [-], Terms offered: Fall 2019, Fall 2018, Spring 2017 Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week, Summer: 8 weeks - 6 hours of lecture, 2 hours of discussion, and 2 hours of laboratory per week, Formerly known as: Statistics C100/Computer Science C100, Principles & Techniques of Data Science: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Topics include group comparisons and ANOVA, standard parametric statistical models, multivariate data visualization, multiple linear regression, logistic regression and classification, regression trees and random forests. Research term project. Fall and/or spring: 15 weeks - 3 hours of web-based lecture per week, Summer: 8 weeks - 7.5 hours of web-based lecture per week, Terms offered: Spring 2021, Fall 2016, Fall 2003 The statistical and computational methods are motivated by and illustrated on data structures that arise in current high-dimensional inference problems in biology and medicine. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Credit Restrictions: Students will receive no credit for DATAC102 after completing STAT 102, or DATA 102. Most time is spent on 2 approaches: mixed models based upon explicit (latent variable) maximum likelihood estimation of the sources of the dependence, versus empirical estimating equation approaches (generalized estimating equations). Standard designs studied include factorial designs, block designs, latin square designs, and repeated measures designs. Recommended prerequisite: Mathematics 55 or equivalent exposure to counting arguments, Summer: 10 weeks - 4.5 hours of lecture and 3 hours of laboratory per week, Modern Statistical Prediction and Machine Learning: Read Less [-], Terms offered: Fall 2022, Spring 2022, Summer 2021 8 Week Session Fall and/or spring: 15 weeks - 1 hour of lecture and 1 hour of laboratory per week, Summer: 6 weeks - 2 hours of lecture and 3 hours of laboratory per week, Introduction to Programming in R: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Credit Restrictions: Students will receive no credit for Statistics 200A-200B after completing Statistics 201A-201B. Students will be exposed to statistical questions that are relevant to decision and policy making. Data, Inference, and Decisions: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Topics will vary semester to semester. BerkeleyX offers interactive online classes and MOOCs from the worlds best universities. Topics in Theoretical Statistics: Read More [+], Formerly known as: 216A-216B and 217A-217B, Topics in Theoretical Statistics: Read Less [-], Terms offered: Spring 2016 Terms offered: Fall 2022, Spring 2022, Fall 2021 Introduction to Advanced Programming in R: Read More [+], Prerequisites: Compsci 61A or equivalent programming background. Terms offered: Fall 2022, Spring 2022, Fall 2021 Applications are drawn from political science, economics, sociology, and public health. Quantitative/Statistical Research Methods in Social Sciences: Individual Study Leading to Higher Degrees. A deficient grade in DATAC102 may be removed by taking STAT 102, STAT 102, or DATA 102. Markov decision processes and partially observable Markov decision processes. Grading: Letter grade. derive consistent statistical inference in the presence of correlated, repeated measures data using likelihood-based mixed models and estimating equation approaches (generalized estimating equations; GEE), Terms offered: Fall 2022, Spring 2022, Fall 2021 Introduction to Programming in R: Read More [+]. The selection of topics may vary from year to year. The Statistics of Causal Inference in the Social Science: Read More [+], Prerequisites: At least one graduate matrix based multivariate regression course in addition to introductory statistics and probability, Fall and/or spring: 15 weeks - 3-3 hours of lecture and 1-2 hours of discussion per week. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing. Simple random, stratified, cluster, and double sampling. Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II, Terms offered: Fall 2017, Fall 2015, Fall 2013. , statistical models, graphical procedures, designing an R package, object-oriented programming, inter-system interfaces. Ensemble methods. Advanced Topics in Probability and Stochastic Process: Advanced Topics in Probability and Stochastic Processes. Repeat rules: Course may be repeated for credit without restriction. The course and lab include hands-on experience in analyzing real world data from the social, life, and physical sciences. Offered through the Student Learning Center. Conditional expectations, martingales and martingale convergence theorems. The PDF will include all information unique to this page. Random permutations, symmetry, order statistics. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. Terms offered: Spring 2022, Fall 2021, Spring 2021 Statistical Genomics: Read More [+], Prerequisites: Statistics 200A and 200B or equivalent (may be taken concurrently). Repeat rules: Course may be repeated for credit with instructor consent. Introduction to Advanced Programming in R: Read Less [-], Terms offered: Fall 2008, Fall 2007 The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. The R statistical language is used. Stationary processes. Student Learning Outcomes: Understand the difference between math and simulation, and appreciate the power of both Advanced topics in probability offered according to students demand and faculty availability.

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