Students will be exposed to statistical questions that are relevant to decision and policy making. Fall and/or spring: 15 weeks - 3 hours of lecture, 2 hours of discussion, and 1 hour of supplement per week, Probability for Data Science: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Stat 140 is a probability course for Data 8 graduates who have also had a year of calculus and wish to go deeper into data science. Research term project. Theoretical Statistics: Read More [+], Prerequisites: Statistics 210A and a graduate level probability course; a good understanding of various notions of stochastic convergence, Terms offered: Spring 2021, Fall 2015, Fall 2012 Advanced Topics in Learning and Decision Making: Read More [+], Advanced Topics in Learning and Decision Making: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 A deficient grade in STAT33B may be removed by taking STAT133. Credit Restrictions: Students will receive no credit for STAT21 after completing STAT20, STATW21, STAT 25, STAT 2X, STAT 21X, STAT S21, STAT 5, STAT2, or STAT N21. painting through robert margaret bible isbn older horse paperback french dorothy recent Summer: 8 weeks - 6 hours of lecture and 4 hours of laboratory per week, Formerly known as: Computer Science C8/Statistics C8/Information C8, Also listed as: COMPSCIC8/DATAC8/INFOC8, Foundations of Data Science: Read Less [-], Terms offered: Fall 2022, Summer 2022 8 Week Session, Spring 2022 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 Professional Preparation: Teaching of Probability and Statistics: Read More [+], Prerequisites: Graduate standing and appointment as a graduate student instructor, Fall and/or spring: 15 weeks - 2 hours of lecture and 4 hours of laboratory per week, Subject/Course Level: Statistics/Professional course for teachers or prospective teachers, Professional Preparation: Teaching of Probability and Statistics: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Corequisite or Prerequisite: Foundations of Data Science (COMPSCIC8 / DATAC8 / INFOC8 / STATC8). implement the relevant methods using R. Repeat rules: Course may be repeated for credit without restriction. Credit Restrictions: Students will receive no credit for Statistics 259 after taking Statistics 159. Analysis of Time Series: Read More [+], Terms offered: Spring 2008, Spring 2006, Spring 2005 Introduction to Statistical Computing: Read Less [-], Terms offered: Spring 2011, Spring 2010, Spring 2009 Special topics in probability and statistics offered according to student demand and faculty availability. Berkeley seminars are offered in all campus departments, and topics vary from department to department and semester to semester. 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. Comprehension of broad concepts is the main goal, but practical implementation in R is also emphasized. Understand in depth and make use of principles of numerical linear algebra, optimization, and simulation for statistics-related research. The Statistics of Causal Inference in the Social Science: Quantitative Methodology in the Social Sciences Seminar. Some standard significance tests. 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. STAT133 recommended, Linear Modelling: Theory and Applications: Read Less [-], Terms offered: Spring 2020, Spring 2019, Spring 2018 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. Algorithms in statistical computing: random number generation, generating other distributions, random sampling and permutations. Strongly recommended corequisite: Statistics 33A or Statistics 133, Statistical Methods for Data Science: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Student Learning Outcomes: Become familiar with concepts and tools for reproducible research and good scientific computing practices. Introduction to Advanced Programming in R. , and object systems. Random variables, discrete and continuous families of distributions. Topics include: probability, conditioning, and independence; random variables; distributions and joint distributions; expectation, variance, tail bounds; Central Limit Theorem; symmetries in random permutations; prior and posterior distributions; probabilistic models; bias-variance tradeoff; testing hypotheses; correlation and the regression model. This is the second course, which focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. Advanced Topics in Probability and Stochastic Process: Advanced Topics in Probability and Stochastic Processes. Quantitative Methodology in the Social Sciences Seminar: Read Less [-], Terms offered: Fall 2018, Fall 2017, Fall 2016 The courses also discusses statistical computing resources, with emphasis on the R language and environment (www.r-project.org). Reproducible and Collaborative Statistical Data Science: Terms offered: Spring 2015, Fall 2014, Fall 2010, Terms offered: Fall 2021, Fall 2020, Spring 2017, Supervised Independent Study and Research, Terms offered: Fall 2019, Fall 2018, Spring 2017. Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis. Use of numerical computation, graphics, simulation, and computer algebra. Linear Modelling: Theory and Applications: Read More [+], Prerequisites: STAT 102 or STAT135. Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree. Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read More [+], Prerequisites: Statistics 200A or equivalent (may be taken concurrently), Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read Less [-], Terms offered: Fall 2017, Fall 2015, Fall 2013 Stochastic Analysis with Applications to Mathematical Finance: Read More [+], Prerequisites: 205A or consent of instructor, Stochastic Analysis with Applications to Mathematical Finance: Read Less [-], Prerequisites: Statistics 201B or Statistics 210A. Credit Restrictions: Students will receive no credit for STATC140 after completing STAT134. Individual Study for Master's Candidates: Read More [+]. Introduction to Statistics at an Advanced Level: Read Less [-], Terms offered: Fall 2019, Spring 2017, Spring 2015 Credit Restrictions: Students will receive no credit for STAT33A after completing STAT33B, or STAT133. Statistics 133, 134, and 135 recommended, Statistical Models: Theory and Application: Read Less [-], Terms offered: Spring 2022, Spring 2021, Spring 2020 Terms offered: Fall 2022, Spring 2022, Fall 2021 Biostatistical Methods: Survival Analysis and Causality: Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine. Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. 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 Linear regression and generalizations (e.g., GLMs, ridge regression, lasso). Upon completion, the final grade will be applied to both parts of the series. The course will cover introductory topics in linear algebra, starting with the basics; discrete probability and how prob- ability can be used to understand high-dimensional vector spaces; matrices and graphs as popular mathematical structures with which to model data (e.g., as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc. Nonparametric and Robust Methods: Read More [+], Prerequisites: A year of upper division probability and statistics, Nonparametric and Robust Methods: Read Less [-], Terms offered: Fall 2021, Fall 2020, Fall 2019 Terms offered: Fall 2022, Spring 2022, Fall 2021 Classification regression, clustering, dimensionality, reduction, and density estimation. Statistical Methods for Data Science: Read More [+], Prerequisites: Statistics/Computer Science/Information C8 or Statistics 20; and Mathematics 1A, Mathematics 16A, or Mathematics 10A/10B. Statistics Colloquium: Read More [+], Fall and/or spring: 15 weeks - 1-2 hours of colloquium per week. Support Berkeleys commitment to excellence and opportunity! 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. ); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc. Conditional expectation, independence, laws of large numbers. Introductory Probability and Statistics for Business: Read More [+]. The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences. Probability and Mathematical Statistics in Data Science: Terms offered: Spring 2022, Spring 2021, Spring 2020. properties of social network data, etc. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models. Corequisite: Mathematics 54, Electrical Engineering 16A, Statistics 89A, Mathematics 110 or equivalent linear algebra. Topics in Theoretical Statistics: Read More [+], Formerly known as: 216A-216B and 217A-217B, Topics in Theoretical Statistics: Read Less [-], Terms offered: Spring 2016 Repeat rules: Course may be repeated for credit when topic changes. In-depth computational implementation using Markov chain Monte Carlo and other techniques. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models. Masters of Statistics Capstone Project: Read More [+], Prerequisites: Statistics 201A-201B, 243. Other topics covered include the principles of design, randomization, ANOVA, response surface methodoloy, and computer experiments. Population and variables. Offered through the Student Learning Center. Prerequisite or corequisite: Foundations of Data Science (COMPSCIC8 / INFOC8 / STATC8), Linear Algebra for Data Science: Read Less [-], Terms offered: Fall 2015 As a member of the UC Berkeley community, I act with honesty, integrity, and respect for others.. 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. Join the online learning revolution! Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog. Final exam not required. Individual and/or group meetings with faculty. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra. Grading/Final exam status: Offered for pass/not pass grade only. Fall and/or spring: 15 weeks - 3 hours of lecture per week, Summer: 8 weeks - 7.5 hours of lecture per week, Introductory Probability and Statistics for Business: Read Less [-], Terms offered: Summer 2021 8 Week Session, Summer 2020 8 Week Session, Summer 2019 8 Week Session
Students will be exposed to statistical questions that are relevant to decision and policy making. Fall and/or spring: 15 weeks - 3 hours of lecture, 2 hours of discussion, and 1 hour of supplement per week, Probability for Data Science: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Stat 140 is a probability course for Data 8 graduates who have also had a year of calculus and wish to go deeper into data science. Research term project. Theoretical Statistics: Read More [+], Prerequisites: Statistics 210A and a graduate level probability course; a good understanding of various notions of stochastic convergence, Terms offered: Spring 2021, Fall 2015, Fall 2012 Advanced Topics in Learning and Decision Making: Read More [+], Advanced Topics in Learning and Decision Making: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 A deficient grade in STAT33B may be removed by taking STAT133. Credit Restrictions: Students will receive no credit for STAT21 after completing STAT20, STATW21, STAT 25, STAT 2X, STAT 21X, STAT S21, STAT 5, STAT2, or STAT N21. painting through robert margaret bible isbn older horse paperback french dorothy recent Summer: 8 weeks - 6 hours of lecture and 4 hours of laboratory per week, Formerly known as: Computer Science C8/Statistics C8/Information C8, Also listed as: COMPSCIC8/DATAC8/INFOC8, Foundations of Data Science: Read Less [-], Terms offered: Fall 2022, Summer 2022 8 Week Session, Spring 2022 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 Professional Preparation: Teaching of Probability and Statistics: Read More [+], Prerequisites: Graduate standing and appointment as a graduate student instructor, Fall and/or spring: 15 weeks - 2 hours of lecture and 4 hours of laboratory per week, Subject/Course Level: Statistics/Professional course for teachers or prospective teachers, Professional Preparation: Teaching of Probability and Statistics: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Corequisite or Prerequisite: Foundations of Data Science (COMPSCIC8 / DATAC8 / INFOC8 / STATC8). implement the relevant methods using R. Repeat rules: Course may be repeated for credit without restriction. Credit Restrictions: Students will receive no credit for Statistics 259 after taking Statistics 159. Analysis of Time Series: Read More [+], Terms offered: Spring 2008, Spring 2006, Spring 2005 Introduction to Statistical Computing: Read Less [-], Terms offered: Spring 2011, Spring 2010, Spring 2009 Special topics in probability and statistics offered according to student demand and faculty availability. Berkeley seminars are offered in all campus departments, and topics vary from department to department and semester to semester. 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. Comprehension of broad concepts is the main goal, but practical implementation in R is also emphasized. Understand in depth and make use of principles of numerical linear algebra, optimization, and simulation for statistics-related research. The Statistics of Causal Inference in the Social Science: Quantitative Methodology in the Social Sciences Seminar. Some standard significance tests. 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. STAT133 recommended, Linear Modelling: Theory and Applications: Read Less [-], Terms offered: Spring 2020, Spring 2019, Spring 2018 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. Algorithms in statistical computing: random number generation, generating other distributions, random sampling and permutations. Strongly recommended corequisite: Statistics 33A or Statistics 133, Statistical Methods for Data Science: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Student Learning Outcomes: Become familiar with concepts and tools for reproducible research and good scientific computing practices. Introduction to Advanced Programming in R. , and object systems. Random variables, discrete and continuous families of distributions. Topics include: probability, conditioning, and independence; random variables; distributions and joint distributions; expectation, variance, tail bounds; Central Limit Theorem; symmetries in random permutations; prior and posterior distributions; probabilistic models; bias-variance tradeoff; testing hypotheses; correlation and the regression model. This is the second course, which focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. Advanced Topics in Probability and Stochastic Process: Advanced Topics in Probability and Stochastic Processes. Quantitative Methodology in the Social Sciences Seminar: Read Less [-], Terms offered: Fall 2018, Fall 2017, Fall 2016 The courses also discusses statistical computing resources, with emphasis on the R language and environment (www.r-project.org). Reproducible and Collaborative Statistical Data Science: Terms offered: Spring 2015, Fall 2014, Fall 2010, Terms offered: Fall 2021, Fall 2020, Spring 2017, Supervised Independent Study and Research, Terms offered: Fall 2019, Fall 2018, Spring 2017. Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis. Use of numerical computation, graphics, simulation, and computer algebra. Linear Modelling: Theory and Applications: Read More [+], Prerequisites: STAT 102 or STAT135. Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree. Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read More [+], Prerequisites: Statistics 200A or equivalent (may be taken concurrently), Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read Less [-], Terms offered: Fall 2017, Fall 2015, Fall 2013 Stochastic Analysis with Applications to Mathematical Finance: Read More [+], Prerequisites: 205A or consent of instructor, Stochastic Analysis with Applications to Mathematical Finance: Read Less [-], Prerequisites: Statistics 201B or Statistics 210A. Credit Restrictions: Students will receive no credit for STATC140 after completing STAT134. Individual Study for Master's Candidates: Read More [+]. Introduction to Statistics at an Advanced Level: Read Less [-], Terms offered: Fall 2019, Spring 2017, Spring 2015 Credit Restrictions: Students will receive no credit for STAT33A after completing STAT33B, or STAT133. Statistics 133, 134, and 135 recommended, Statistical Models: Theory and Application: Read Less [-], Terms offered: Spring 2022, Spring 2021, Spring 2020 Terms offered: Fall 2022, Spring 2022, Fall 2021 Biostatistical Methods: Survival Analysis and Causality: Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine. Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. 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 Linear regression and generalizations (e.g., GLMs, ridge regression, lasso). Upon completion, the final grade will be applied to both parts of the series. The course will cover introductory topics in linear algebra, starting with the basics; discrete probability and how prob- ability can be used to understand high-dimensional vector spaces; matrices and graphs as popular mathematical structures with which to model data (e.g., as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc. Nonparametric and Robust Methods: Read More [+], Prerequisites: A year of upper division probability and statistics, Nonparametric and Robust Methods: Read Less [-], Terms offered: Fall 2021, Fall 2020, Fall 2019 Terms offered: Fall 2022, Spring 2022, Fall 2021 Classification regression, clustering, dimensionality, reduction, and density estimation. Statistical Methods for Data Science: Read More [+], Prerequisites: Statistics/Computer Science/Information C8 or Statistics 20; and Mathematics 1A, Mathematics 16A, or Mathematics 10A/10B. Statistics Colloquium: Read More [+], Fall and/or spring: 15 weeks - 1-2 hours of colloquium per week. Support Berkeleys commitment to excellence and opportunity! 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. ); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc. Conditional expectation, independence, laws of large numbers. Introductory Probability and Statistics for Business: Read More [+]. The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences. Probability and Mathematical Statistics in Data Science: Terms offered: Spring 2022, Spring 2021, Spring 2020. properties of social network data, etc. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models. Corequisite: Mathematics 54, Electrical Engineering 16A, Statistics 89A, Mathematics 110 or equivalent linear algebra. Topics in Theoretical Statistics: Read More [+], Formerly known as: 216A-216B and 217A-217B, Topics in Theoretical Statistics: Read Less [-], Terms offered: Spring 2016 Repeat rules: Course may be repeated for credit when topic changes. In-depth computational implementation using Markov chain Monte Carlo and other techniques. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models. Masters of Statistics Capstone Project: Read More [+], Prerequisites: Statistics 201A-201B, 243. Other topics covered include the principles of design, randomization, ANOVA, response surface methodoloy, and computer experiments. Population and variables. Offered through the Student Learning Center. Prerequisite or corequisite: Foundations of Data Science (COMPSCIC8 / INFOC8 / STATC8), Linear Algebra for Data Science: Read Less [-], Terms offered: Fall 2015 As a member of the UC Berkeley community, I act with honesty, integrity, and respect for others.. 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. Join the online learning revolution! Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog. Final exam not required. Individual and/or group meetings with faculty. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra. Grading/Final exam status: Offered for pass/not pass grade only. Fall and/or spring: 15 weeks - 3 hours of lecture per week, Summer: 8 weeks - 7.5 hours of lecture per week, Introductory Probability and Statistics for Business: Read Less [-], Terms offered: Summer 2021 8 Week Session, Summer 2020 8 Week Session, Summer 2019 8 Week Session
