# Courses

### 2023 Fall STAT 2 001 LEC 001 - Introduction to Statistics

Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Interval estimation. Some standard significance tests.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 454 | 453 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

22953 | LAB 2 | TuTh 9:00am - 9:59am | Evans 334 | 4 | 28/27/0 |

22954 | LAB 2 | TuTh 10:00am - 10:59am | Evans 334 | 4 | 28/28/0 |

22955 | LAB 2 | TuTh 11:00am - 11:59am | Evans 334 | 4 | 29/29/0 |

22992 | LAB 2 | TuTh 11:00am - 11:59am | Evans 344 | 4 | 28/28/0 |

22993 | LAB 2 | TuTh 10:00am - 10:59am | Evans 332 | 4 | 28/28/0 |

22994 | LAB 2 | TuTh 12:00pm - 12:59pm | Evans 344 | 4 | 28/28/0 |

25760 | LAB 2 | TuTh 4:00pm - 4:59pm | Evans 344 | 4 | 28/28/0 |

25761 | LAB 2 | TuTh 5:00pm - 5:59pm | Evans 344 | 4 | 29/29/0 |

26744 | LAB 2 | TuTh 4:00pm - 4:59pm | Evans 332 | 4 | 28/28/0 |

26745 | LAB 2 | TuTh 5:00pm - 5:59pm | Evans 332 | 4 | 29/29/0 |

22995 | LAB 2 | TuTh 1:00pm - 1:59pm | Evans 344 | 4 | 29/29/0 |

22996 | LAB 2 | TuTh 2:00pm - 2:59pm | Evans 344 | 4 | 28/27/0 |

22997 | LAB 2 | TuTh 3:00pm - 3:59pm | Evans 344 | 4 | 28/28/0 |

23005 | LAB 2 | TuTh 1:00pm - 1:59pm | Evans 6 | 4 | 28/28/0 |

23006 | LAB 2 | TuTh 1:00pm - 1:59pm | Evans 2 | 4 | 28/28/0 |

23007 | LAB 2 | TuTh 2:00pm - 2:59pm | Evans 2 | 4 | 28/28/0 |

23008 | LAB 2 | TuTh 3:00pm - 3:59pm | Evans 2 | 4 | 28/28/0 |

22944 | LAB 2 | TuTh 9:00am - 9:59am | Evans 6 | 4 | 28/28/0 |

22945 | LAB 2 | TuTh 10:00am - 10:59am | Evans 85 | 4 | 28/28/0 |

22947 | LAB 2 | TuTh 11:00am - 11:59am | Evans 85 | 4 | 28/28/0 |

22948 | LAB 2 | TuTh 12:00pm - 12:59pm | Evans 2 | 4 | 28/28/0 |

22960 | LAB 2 | TuTh 8:00am - 8:59am | Evans 344 | 4 | 28/29/0 |

26671 | LAB 2 | TuTh 5:00pm - 5:59pm | Evans 71 | 4 | 28/28/0 |

32571 | LAB 2 | TuTh 11:00am - 11:59am | Evans 332 | 4 | 28/28/0 |

32572 | LAB 2 | TuTh 2:00pm - 2:59pm | Evans 332 | 4 | 28/28/0 |

22951 | LAB 2 | TuTh 9:00am - 9:59am | Evans 344 | 4 | 29/29/0 |

22952 | LAB 2 | TuTh 10:00am - 10:59am | Evans 344 | 4 | 29/29/0 |

### 2023 Fall STAT C8 001 LEC 001 - Foundations of Data Science

Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? 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. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24699 | LAB 8 | 4 | 0/0/0 |

### 2023 Fall STAT 20 001 LEC 001 - Introduction to Probability and Statistics

For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 122 | 127 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

22965 | LAB 20 | WeFr 9:00am - 9:59am | Wheeler 212 | 4 | 122/127/0 |

### 2023 Fall STAT 20 002 LEC 002 - Introduction to Probability and Statistics

For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 103 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

22961 | LAB 20 | TuTh 10:00am - 10:59am | Moffitt Library 145 | 4 | 96/103/0 |

### 2023 Fall STAT 20 004 LEC 004 - Introduction to Probability and Statistics

For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 104 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

26795 | LAB 20 | TuTh 12:00pm - 12:59pm | Moffitt Library 145 | 4 | 96/104/0 |

### 2023 Fall STAT 20 005 LEC 005 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 106 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

26797 | LAB 20 | WeFr 1:00pm - 1:59pm | Moffitt Library 145 | 4 | 96/106/0 |

### 2023 Fall STAT 20 006 LEC 006 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 100 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

26799 | LAB 20 | TuTh 2:00pm - 2:59pm | Moffitt Library 145 | 4 | 96/100/0 |

### 2023 Fall STAT 20 007 LEC 007 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 102 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

26801 | LAB 20 | WeFr 3:00pm - 3:59pm | Moffitt Library 145 | 4 | 96/102/0 |

### 2023 Fall STAT 20 008 LEC 008 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 97 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

26803 | LAB 20 | TuTh 4:00pm - 4:59pm | Moffitt Library 145 | 4 | 96/97/0 |

### 2023 Fall STAT 20 003 LEC 003 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 104 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

31549 | LAB 20 | WeFr 11:00am - 11:59am | Moffitt Library 145 | 4 | 96/104/0 |

### 2023 Fall STAT 33A 001 LEC 001 - Introduction to Programming in R

An introduction to the R statistical software for students with minimal prior experience with programming. This course prepares students for data analysis with R. The focus is on the computational model that underlies the R language with the goal of providing a foundation for coding. 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. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 90 | 84 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24260 | LAB 33 | We 9:00am - 9:59am | Evans 334 | 1 | 22/22/0 |

24261 | LAB 33 | We 10:00am - 10:59am | Evans 334 | 1 | 22/19/0 |

24262 | LAB 33 | We 2:00pm - 2:59pm | Evans 340 | 1 | 23/21/0 |

24263 | LAB 33 | We 3:00pm - 3:59pm | Evans 340 | 1 | 23/22/0 |

### 2023 Fall STAT 33B 001 LEC 001 - Introduction to Advanced Programming in R

The course is designed primarily for those who are already familiar with programming in another language, such as python, and want to understand how R works, and for those who already know the basics of R programming and want to gain a more in-depth understanding of the language in order to improve their coding. The focus is on the underlying paradigms in R, such as functional programming, atomic vectors, complex data structures, environments, and object systems. 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.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 90 | 88 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24265 | LAB 33 | Mo 9:00am - 9:59am | Evans 334 | 1 | 21/20/0 |

24536 | LAB 33 | Mo 10:00am - 10:59am | Evans 334 | 1 | 22/20/0 |

25529 | LAB 33 | Mo 12:00pm - 12:59pm | Evans 340 | 1 | 22/22/0 |

25530 | LAB 33 | Mo 1:00pm - 1:59pm | Evans 340 | 1 | 25/26/0 |

### 2023 Fall STAT C102 001 LEC 001 - Data, Inference, and Decisions

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. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24871 | LAB 102 | Mo 1:00pm - 1:59pm | Social Sciences Building 104 | 4 | 0/0/0 |

24760 | LAB 102 | Mo 9:00am - 9:59am | Social Sciences Building 104 | 4 | 0/0/0 |

24762 | LAB 102 | Mo 10:00am - 10:59am | Social Sciences Building 175 | 4 | 0/0/0 |

24764 | LAB 102 | Mo 11:00am - 11:59am | Social Sciences Building 175 | 4 | 0/0/0 |

24766 | LAB 102 | Mo 12:00pm - 12:59pm | Social Sciences Building 104 | 4 | 0/0/0 |

24873 | LAB 102 | Mo 2:00pm - 2:59pm | Social Sciences Building 104 | 4 | 0/0/0 |

24875 | LAB 102 | Mo 3:00pm - 3:59pm | Social Sciences Building 104 | 4 | 0/0/0 |

24877 | LAB 102 | Mo 4:00pm - 4:59pm | Social Sciences Building 104 | 4 | 0/0/0 |

25486 | LAB 102 | 4 | 0/0/0 |

### 2023 Fall STAT C131A 001 LEC 001 - Statistical Methods for Data Science

This course teaches a broad range of statistical methods that are used to solve data problems. 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. An important focus of the course is on statistical computing and reproducible statistical analysis. The course and lab include hands-on experience in analyzing real world data from the social, life, and physical sciences. The R statistical language is used.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 80 | 80 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

25175 | LAB 131 | TuTh 10:00am - 10:59am | Evans 340 | 4 | 40/40/0 |

25176 | LAB 131 | TuTh 3:00pm - 3:59pm | Evans 340 | 4 | 40/40/0 |

### 2023 Fall STAT 133 001 LEC 001 - Concepts in Computing with Data

An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 180 | 189 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23000 | LAB 133 | Th 9:00am - 10:59am | Evans 342 | 3 | 30/30/0 |

23001 | LAB 133 | Th 11:00am - 12:59pm | Evans 342 | 3 | 30/32/0 |

23002 | LAB 133 | Th 11:00am - 12:59pm | Evans 340 | 3 | 30/32/0 |

23003 | LAB 133 | Th 1:00pm - 2:59pm | Evans 340 | 3 | 30/33/0 |

25265 | LAB 133 | Th 1:00pm - 2:59pm | Evans 342 | 3 | 30/30/0 |

25266 | LAB 133 | Th 3:00pm - 4:59pm | Evans 342 | 3 | 30/32/0 |

### 2023 Fall STAT 134 001 LEC 001 - Concepts of Probability

An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 361 | 360 | 0 |

### 2023 Fall STAT 135 001 LEC 001 - Concepts of Statistics

A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, non-parametric methods, introduction to optimality, goodness-of-fit tests, analysis of variance, bootstrap and computer-intensive methods and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 127 | 126 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

22967 | LAB 135 | Fr 4:00pm - 5:59pm | Evans 344 | 4 | 35/35/0 |

23010 | LAB 135 | Fr 12:00pm - 1:59pm | Hearst Mining 310 | 4 | 35/32/0 |

23011 | LAB 135 | Fr 1:00pm - 2:59pm | Evans 9 | 4 | 0/0/0 |

22966 | LAB 135 | Fr 3:00pm - 4:59pm | Dwinelle 229 | 4 | 0/0/0 |

23483 | LAB 135 | Fr 1:00pm - 2:59pm | Dwinelle 223 | 4 | 35/26/0 |

23484 | LAB 135 | Fr 3:00pm - 4:59pm | Hearst Mining 310 | 4 | 35/33/0 |

### 2023 Fall STAT 150 001 LEC 001 - Stochastic Processes

Random walks, discrete time Markov chains, Poisson processes. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 96 | 83 | 0 |

### 2023 Fall STAT 151A 001 LEC 001 - Linear Modelling: Theory and Applications

A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 70 | 50 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

22941 | LAB 151 | Fr 9:00am - 10:59am | Evans 330 | 4 | 35/26/0 |

22942 | LAB 151 | Fr 12:00pm - 1:59pm | Evans 330 | 4 | 35/24/0 |

### 2023 Fall STAT 153 001 LEC 001 - Introduction to Time Series

An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 70 | 79 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23876 | LAB 153 | Fr 2:00pm - 3:59pm | Evans 332 | 4 | 40/39/0 |

23877 | LAB 153 | Fr 4:00pm - 5:59pm | Evans 332 | 4 | 40/40/0 |

23878 | LAB 153 | 4 | 30/0/0 | ||

23879 | LAB 153 | 4 | 30/0/0 |

### 2023 Fall STAT 154 001 LEC 001 - Modern Statistical Prediction and Machine Learning

Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 50 | 48 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

22925 | LAB 154 | We 9:00am - 10:59am | Evans 344 | 4 | 25/24/0 |

22926 | LAB 154 | We 12:00pm - 1:59pm | Evans 344 | 4 | 25/24/0 |

### 2023 Fall STAT 156 001 LEC 001 - Causal Inference

This course will focus on approaches to causal inference using the potential outcomes framework. It will also use causal diagrams at an intuitive level. The main topics are classical randomized experiments, observational studies, instrumental variables, principal stratification and mediation analysis. Applications are drawn from a variety of fields including political science, economics, sociology, public health, and medicine. This course is a mix of statistical theory and data analysis. Students will be exposed to statistical questions that are relevant to decision and policy making.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 40 | 37 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24805 | LAB 156 | Mo 9:00am - 10:59am | Evans 330 | 4 | 18/18/0 |

24806 | LAB 156 | Mo 1:00pm - 2:59pm | Evans 330 | 4 | 23/19/0 |

### 2023 Fall STAT 157 001 SEM 001 - Seminar on Topics in Probability and Statistics

Substantial student participation required. The topics to be covered each semester that the course may be offered will be announced by the middle of the preceding semester; see departmental bulletins. Recent topics include: Bayesian statistics, statistics and finance, random matrix theory, high-dimensional statistics.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 35 | 34 | 0 |

### 2023 Fall STAT 158 001 LEC 001 - Experimental Design

This course will review the statistical foundations of randomized experiments and study principles for addressing common setbacks in experimental design and analysis in practice. We will cover the notion of potential outcomes for causal inference and the Fisherian principles for experimentation (randomization, blocking, and replications). We will also cover experiments with complex structures (clustering in units, factorial design, hierarchy in treatments, sequential assignment, etc). We will also address practical complications in experiments, including noncompliance, missing data, and measurement error.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 70 | 58 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

32607 | LAB 158 | Mo 11:00am - 12:59pm | Evans 330 | 4 | 35/29/0 |

32608 | LAB 158 | Mo 3:00pm - 4:59pm | Evans 330 | 4 | 35/29/0 |

### 2023 Fall STAT 201B 001 LEC 001 - Introduction to Statistics at an Advanced Level

Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 70 | 59 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

22937 | LAB 201 | We 1:00pm - 2:59pm | Evans 332 | 4 | 35/33/0 |

22938 | LAB 201 | We 3:00pm - 4:59pm | Evans 332 | 4 | 35/26/0 |

### 2023 Fall STAT 201A 001 LEC 001 - Introduction to Probability at an Advanced Level

Distributions in probability and statistics, central limit theorem, Poisson processes, modes of convergence, transformations involving random variables.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 70 | 59 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

22934 | LAB 201 | Mo 12:00pm - 1:59pm | Evans 332 | 4 | 35/28/0 |

22935 | LAB 201 | Mo 2:00pm - 3:59pm | Evans 332 | 4 | 35/31/0 |

### 2023 Fall STAT 204 001 LEC 001 - Probability for Applications

A treatment of ideas and techniques most commonly found in the applications of probability: Gaussian and Poisson processes, limit theorems, large deviation principles, information, Markov chains and Markov chain Monte Carlo, martingales, Brownian motion and diffusion.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 25 | 19 | 0 |

### 2023 Fall STAT C205A 001 LEC 001 - Probability Theory

The course is designed as a sequence with Statistics C205B/Mathematics C218B with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 21 | 20 | 0 |

### 2023 Fall STAT 210A 001 LEC 001 - Theoretical Statistics

An introduction to mathematical statistics, covering both frequentist and Bayesian aspects of modeling, inference, and decision-making. Topics include statistical decision theory; point estimation; minimax and admissibility; Bayesian methods; exponential families; hypothesis testing; confidence intervals; small and large sample theory; and M-estimation.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 90 | 69 | 0 |

### 2023 Fall STAT 215A 001 LEC 001 - Statistical Models: Theory and Application

Applied statistics with a focus on critical thinking, reasoning skills, and techniques. Hands-on-experience with solving real data problems with high-level programming languages such as R. Emphasis on examining the assumptions behind standard statistical models and methods. Exploratory data analysis (e.g., graphical data summaries, PCAs, clustering analysis). Model formulation, fitting, and validation and testing. Linear regression and generalizations (e.g., GLMs, ridge regression, lasso).

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 30 | 21 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

22963 | LAB 215 | Fr 9:00am - 10:59am | Evans 334 | 4 | 30/21/0 |

### 2023 Fall STAT 243 001 LEC 001 - Introduction to Statistical Computing

Concepts in statistical programming and statistical computation, including programming principles, data and text manipulation, parallel processing, simulation, numerical linear algebra, and optimization.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 70 | 56 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23021 | LAB 243 | Fr 1:00pm - 2:59pm | Evans 340 | 4 | 35/34/0 |

23022 | LAB 243 | Fr 3:00pm - 4:59pm | Evans 340 | 4 | 35/22/0 |

### 2023 Fall STAT C245B 001 LEC 001 - Concepts of Statistics

A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, non-parametric methods, introduction to optimality, goodness-of-fit tests, analysis of variance, bootstrap and computer-intensive methods and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 11 | 6 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24205 | LAB 245 | We 12:00pm - 1:59pm | Berkeley Way West 1212 | 4 | 10/6/0 |

### 2023 Fall STAT 254 001 LEC 001 - Modern Statistical Prediction and Machine Learning

This course is about statistical learning methods and their use for data analysis. Upon completion, students will be able to build baseline models for real world data analysis problems, implement models using programming languages and draw conclusions from models. The course will cover principled statistical methodology for basic machine learning tasks such as regression, classification, dimension reduction and clustering. Methods discussed will include linear regression, subset selection, ridge regression, LASSO, logistic regression, kernel smoothing methods, tree based methods, bagging and boosting, neural networks, Bayesian methods, as well as inference techniques based on resampling, cross validation and sample splitting.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 20 | 12 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

32556 | LAB 254 | We 9:00am - 10:59am | Evans 344 | 4 | 10/4/0 |

32557 | LAB 254 | We 12:00pm - 1:59pm | Evans 344 | 4 | 10/8/0 |

### 2023 Fall STAT 256 001 LEC 001 - Causal Inference

This course will focus on approaches to causal inference using the potential outcomes framework. It will also use causal diagrams at an intuitive level. The main topics are classical randomized experiments, observational studies, instrumental variables, principal stratification and mediation analysis. Applications are drawn from a variety of fields including political science, economics, sociology, public health, and medicine. This course is a mix of statistical theory and data analysis. Students will be exposed to statistical questions that are relevant to decision and policy making.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 40 | 39 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24809 | LAB 256 | Mo 1:00pm - 2:59pm | Evans 330 | 4 | 20/19/0 |

24808 | LAB 256 | Mo 9:00am - 10:59am | Evans 330 | 4 | 20/20/0 |

### 2023 Fall STAT 272 001 SES 001 - Statistical Consulting

To be taken concurrently with service as a consultant in the department's drop-in consulting service. Participants will work on problems arising in the service and will discuss general ways of handling such problems. There will be working sessions with researchers in substantive fields and occasional lectures on consulting.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 12 | 6 | 0 |

### 2023 Fall STAT 278B 001 SEM 001 - Statistics Research Seminar

Special topics, by means of lectures and informational conferences.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 35 | 16 | 0 |

### 2023 Fall STAT 278B 002 SEM 002 - Statistics Research Seminar

Special topics, by means of lectures and informational conferences.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 10 | 6 | 0 |

### 2023 Fall STAT 375 001 LEC 001 - Professional Preparation: Teaching of Probability and Statistics

Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 40 | 13 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23142 | LAB 375 | 40/13/0 |

### 2023 Summer STAT 2 001 LEC 001 - Introduction to Statistics

Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Interval estimation. Some standard significance tests.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 120 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

13851 | LAB 2 | TuTh 9:00am - 10:59am | Internet/Online | 4 | 30/0/0 |

13852 | LAB 2 | TuTh 11:30am - 1:29pm | Internet/Online | 4 | 30/0/0 |

14171 | LAB 2 | TuTh 2:00pm - 3:59pm | Internet/Online | 4 | 30/0/0 |

14179 | LAB 2 | TuTh 5:00pm - 6:59pm | Internet/Online | 4 | 30/0/0 |

### 2023 Summer STAT C8 001 LEC 001 - Foundations of Data Science

Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? 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. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

13993 | LAB 8 | MoWe 3:00pm - 4:59pm | Etcheverry 3111 | 4 | 0/0/0 |

13994 | LAB 8 | MoWe 3:00pm - 4:59pm | Etcheverry 3109 | 4 | 0/0/0 |

13870 | LAB 8 | MoWe 11:00am - 12:59pm | Etcheverry 3119 | 4 | 0/0/0 |

13871 | LAB 8 | MoWe 11:00am - 12:59pm | Social Sciences Building 110 | 4 | 0/0/0 |

13880 | LAB 8 | MoWe 11:00am - 12:59pm | Social Sciences Building 122 | 4 | 0/0/0 |

13881 | LAB 8 | MoWe 11:00am - 12:59pm | Requested General Assignment | 4 | 0/0/0 |

13882 | LAB 8 | MoWe 1:00pm - 2:59pm | Etcheverry 3119 | 4 | 0/0/0 |

13883 | LAB 8 | MoWe 1:00pm - 2:59pm | Social Sciences Building 110 | 4 | 0/0/0 |

13887 | LAB 8 | MoWe 1:00pm - 2:59pm | Etcheverry 3111 | 4 | 0/0/0 |

13888 | LAB 8 | MoWe 1:00pm - 2:59pm | Etcheverry 3109 | 4 | 0/0/0 |

13896 | LAB 8 | MoWe 1:00pm - 2:59pm | Etcheverry 3105 | 4 | 0/0/0 |

13897 | LAB 8 | MoWe 3:00pm - 4:59pm | Hearst Field Annex B5 | 4 | 0/0/0 |

13992 | LAB 8 | MoWe 3:00pm - 4:59pm | Social Sciences Building 110 | 4 | 0/0/0 |

13995 | LAB 8 | MoWe 5:00pm - 6:59pm | Etcheverry 3109 | 4 | 0/0/0 |

13996 | LAB 8 | MoWe 5:00pm - 6:59pm | Etcheverry 3107 | 4 | 0/0/0 |

### 2023 Summer STAT 20 001 LEC 001 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 90 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

13854 | LAB 20 | TuWeTh 1:00pm - 1:59pm | Anthro/Art Practice Bldg 160 | 4 | 31/0/0 |

13855 | LAB 20 | MoWeTh 2:00pm - 2:59pm | 4 | 30/0/0 | |

13999 | LAB 20 | MoWeTh 3:00pm - 3:59pm | 4 | 30/0/0 |

### 2023 Summer STAT 21 001 LEC 001 - Introductory Probability and Statistics for Business

Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 277 | 0 | 0 |

### 2023 Summer STAT C100 001 LEC 001 - Principles & Techniques of Data Science

In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. This class will focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. 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.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

13971 | LAB 100 | MoTuWeThFr 4:30pm - 5:29pm | Internet/Online | 4 | 0/0/0 |

13958 | LAB 100 | TuTh 1:00pm - 1:59pm | Etcheverry 3111 | 4 | 0/0/0 |

13960 | LAB 100 | TuTh 2:00pm - 2:59pm | Hearst Field Annex B5 | 4 | 0/0/0 |

13953 | LAB 100 | TuTh 4:00pm - 4:59pm | Hearst Field Annex B5 | 4 | 0/0/0 |

13955 | LAB 100 | TuTh 4:00pm - 4:59pm | Etcheverry 3111 | 4 | 0/0/0 |

13957 | LAB 100 | TuTh 4:00pm - 4:59pm | Etcheverry 3109 | 4 | 0/0/0 |

### 2023 Summer STAT 134 001 LEC 001 - Concepts of Probability

An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 100 | 0 | 0 |

### 2023 Summer STAT 135 001 LEC 001 - Concepts of Statistics

A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, non-parametric methods, introduction to optimality, goodness-of-fit tests, analysis of variance, bootstrap and computer-intensive methods and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 60 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

15456 | LAB 135 | TuWeTh 3:30pm - 4:30pm | 4 | 30/0/0 | |

15457 | LAB 135 | TuWeTh 4:30pm - 5:30pm | 4 | 30/0/0 |

### 2023 Summer STAT 155 001 LEC 001 - Game Theory

General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 60 | 0 | 0 |

### 2023 Spring STAT 2 001 LEC 001 - Introduction to Statistics

Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Interval estimation. Some standard significance tests.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 451 | 454 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23268 | LAB 2 | MoWe 8:00am - 8:59am | Evans 2 | 4 | 28/28/0 |

23269 | LAB 2 | MoWe 9:00am - 9:59am | Evans 70 | 4 | 29/29/0 |

23270 | LAB 2 | MoWe 10:00am - 10:59am | Cheit C335 | 4 | 28/28/0 |

23271 | LAB 2 | MoWe 10:00am - 10:59am | Wheeler 106 | 4 | 28/29/0 |

23272 | LAB 2 | MoWe 11:00am - 11:59am | Latimer 105 | 4 | 29/30/0 |

23273 | LAB 2 | MoWe 11:00am - 11:59am | Cheit C335 | 4 | 28/28/0 |

23274 | LAB 2 | MoWe 1:00pm - 1:59pm | Evans 85 | 4 | 28/27/0 |

23275 | LAB 2 | MoWe 2:00pm - 2:59pm | Evans 75 | 4 | 28/28/0 |

26524 | LAB 2 | MoWe 2:00pm - 2:59pm | Wheeler 24 | 4 | 28/28/0 |

26525 | LAB 2 | MoWe 3:00pm - 3:59pm | Evans 81 | 4 | 28/28/0 |

26526 | LAB 2 | MoWe 9:00am - 9:59am | Evans 334 | 4 | 28/28/0 |

26527 | LAB 2 | MoWe 5:00pm - 5:59pm | Evans 75 | 4 | 28/28/0 |

26528 | LAB 2 | MoWe 2:00pm - 2:59pm | Evans 344 | 4 | 28/28/0 |

27027 | LAB 2 | MoWe 3:00pm - 3:59pm | Evans 344 | 4 | 28/29/0 |

32910 | LAB 2 | MoWe 4:00pm - 4:59pm | Evans 334 | 4 | 29/29/0 |

32911 | LAB 2 | MoWe 4:00pm - 4:59pm | Evans 344 | 4 | 28/29/0 |

### 2023 Spring STAT C8 001 LEC 001 - Foundations of Data Science

Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? 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. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23452 | LAB 8 | We 12:00pm - 1:59pm | 4 | 0/0/0 | |

23453 | LAB 8 | We 12:00pm - 1:59pm | 4 | 0/0/0 | |

23454 | LAB 8 | We 12:00pm - 1:59pm | 4 | 0/0/0 | |

23455 | LAB 8 | We 12:00pm - 1:59pm | 4 | 0/0/0 | |

23456 | LAB 8 | We 2:00pm - 3:59pm | 4 | 0/0/0 | |

23457 | LAB 8 | We 2:00pm - 3:59pm | 4 | 0/0/0 | |

23458 | LAB 8 | We 2:00pm - 3:59pm | 4 | 0/0/0 | |

23459 | LAB 8 | We 2:00pm - 3:59pm | 4 | 0/0/0 | |

23460 | LAB 8 | We 4:00pm - 5:59pm | 4 | 0/0/0 | |

23461 | LAB 8 | We 4:00pm - 5:59pm | 4 | 0/0/0 | |

23462 | LAB 8 | We 4:00pm - 5:59pm | 4 | 0/0/0 | |

23463 | LAB 8 | We 4:00pm - 5:59pm | 4 | 0/0/0 | |

23464 | LAB 8 | We 6:00pm - 7:59pm | 4 | 0/0/0 | |

23465 | LAB 8 | We 6:00pm - 7:59pm | 4 | 0/0/0 | |

23466 | LAB 8 | We 6:00pm - 7:59pm | 4 | 0/0/0 | |

23467 | LAB 8 | We 6:00pm - 7:59pm | 4 | 0/0/0 | |

23468 | LAB 8 | Th 8:00am - 9:59am | 4 | 0/0/0 | |

23469 | LAB 8 | Th 8:00am - 9:59am | 4 | 0/0/0 | |

23470 | LAB 8 | Th 8:00am - 9:59am | 4 | 0/0/0 | |

23471 | LAB 8 | Th 8:00am - 9:59am | 4 | 0/0/0 | |

23817 | LAB 8 | Th 10:00am - 11:59am | 4 | 0/0/0 | |

24009 | LAB 8 | Th 12:00pm - 1:59pm | 4 | 0/0/0 | |

24045 | LAB 8 | Th 12:00pm - 1:59pm | 4 | 0/0/0 | |

24047 | LAB 8 | Th 12:00pm - 1:59pm | 4 | 0/0/0 | |

24048 | LAB 8 | Th 12:00pm - 1:59pm | 4 | 0/0/0 | |

24049 | LAB 8 | Th 2:00pm - 3:59pm | 4 | 0/0/0 | |

24050 | LAB 8 | Th 2:00pm - 3:59pm | 4 | 0/0/0 | |

24051 | LAB 8 | Th 2:00pm - 3:59pm | 4 | 0/0/0 | |

24052 | LAB 8 | Th 2:00pm - 3:59pm | 4 | 0/0/0 | |

23814 | LAB 8 | Th 10:00am - 11:59am | 4 | 0/0/0 | |

24252 | LAB 8 | Th 6:00pm - 7:59pm | 4 | 0/0/0 | |

23815 | LAB 8 | Th 10:00am - 11:59am | 4 | 0/0/0 | |

24220 | LAB 8 | Th 4:00pm - 5:59pm | Internet/Online | 4 | 0/0/0 |

24525 | LAB 8 | Fr 12:00pm - 1:59pm | 4 | 0/0/0 | |

23816 | LAB 8 | Th 10:00am - 11:59am | 4 | 0/0/0 | |

24221 | LAB 8 | Th 4:00pm - 5:59pm | Internet/Online | 4 | 0/0/0 |

24526 | LAB 8 | Fr 2:00pm - 3:59pm | 4 | 0/0/0 | |

24222 | LAB 8 | Th 4:00pm - 5:59pm | Internet/Online | 4 | 0/0/0 |

24527 | LAB 8 | Fr 12:00pm - 1:59pm | 4 | 0/0/0 | |

24223 | LAB 8 | Th 4:00pm - 5:59pm | Wheeler 104 | 4 | 0/0/0 |

24615 | LAB 8 | Fr 2:00pm - 3:59pm | 4 | 0/0/0 | |

24616 | LAB 8 | Fr 12:00pm - 1:59pm | 4 | 0/0/0 | |

24617 | LAB 8 | Fr 12:00pm - 1:59pm | 4 | 0/0/0 | |

24224 | LAB 8 | Th 6:00pm - 7:59pm | Internet/Online | 4 | 0/0/0 |

24618 | LAB 8 | Th 2:00pm - 3:59pm | 4 | 0/0/0 | |

24619 | LAB 8 | Fr 8:00am - 9:59am | 4 | 0/0/0 | |

24620 | LAB 8 | Fr 2:00pm - 3:59pm | 4 | 0/0/0 | |

24621 | LAB 8 | Fr 2:00pm - 3:59pm | 4 | 0/0/0 | |

24622 | LAB 8 | Fr 12:00pm - 1:59pm | 4 | 0/0/0 | |

24225 | LAB 8 | Th 6:00pm - 7:59pm | Internet/Online | 4 | 0/0/0 |

25040 | LAB 8 | Mo 12:00pm - 12:59pm | Etcheverry 3105 | 4 | 0/0/0 |

### 2023 Spring STAT 20 001 LEC 001 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 122 | 122 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23277 | LAB 20 | WeFr 9:00am - 9:59am | Wheeler 212 | 4 | 122/122/0 |

### 2023 Spring STAT 20 002 LEC 002 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 86 | 86 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

27150 | LAB 20 | WeFr 9:00am - 9:59am | GSPP 150 | 4 | 86/86/0 |

### 2023 Spring STAT 20 003 LEC 003 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 98 | 98 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

27352 | LAB 20 | WeFr 11:00am - 11:59am | Moffitt Library 145 | 4 | 100/98/0 |

### 2023 Spring STAT 20 004 LEC 004 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 104 | 104 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

31489 | LAB 20 | WeFr 1:00pm - 1:59pm | Moffitt Library 145 | 4 | 104/104/0 |

### 2023 Spring STAT 20 005 LEC 005 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 98 | 98 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

31490 | LAB 20 | WeFr 3:00pm - 3:59pm | Moffitt Library 145 | 4 | 98/98/0 |

### 2023 Spring STAT 20 007 LEC 007 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 120 | 120 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

31492 | LAB 20 | WeFr 6:00pm - 6:59pm | Wheeler 212 | 4 | 120/120/0 |

### 2023 Spring STAT 20 008 LEC 008 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 99 | 99 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

31493 | LAB 20 | WeFr 9:00am - 9:59am | Moffitt Library 145 | 4 | 99/99/0 |

### 2023 Spring STAT 33A 001 LEC 001 - Introduction to Programming in R

An introduction to the R statistical software for students with minimal prior experience with programming. This course prepares students for data analysis with R. The focus is on the computational model that underlies the R language with the goal of providing a foundation for coding. 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. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 100 | 87 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24866 | LAB 33 | We 11:00am - 11:59am | Evans 342 | 1 | 25/22/0 |

24867 | LAB 33 | We 9:00am - 9:59am | Evans 342 | 1 | 25/20/0 |

24868 | LAB 33 | We 1:00pm - 1:59pm | Evans 342 | 1 | 25/21/0 |

24869 | LAB 33 | We 2:00pm - 2:59pm | Evans 342 | 1 | 25/24/0 |

### 2023 Spring STAT C100 001 LEC 001 - Principles & Techniques of Data Science

In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. This class will focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. 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.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

25088 | LAB 100 | Tu 7:00pm - 7:59pm | Evans 342 | 4 | 0/0/0 |

23801 | LAB 100 | Tu 4:00pm - 4:59pm | 4 | 0/0/0 | |

23802 | LAB 100 | Tu 4:00pm - 4:59pm | 4 | 0/0/0 | |

23803 | LAB 100 | We 10:00am - 10:59am | 4 | 0/0/0 | |

23804 | LAB 100 | We 10:00am - 10:59am | 4 | 0/0/0 | |

24028 | LAB 100 | We 11:00am - 11:59am | 4 | 0/0/0 | |

24029 | LAB 100 | We 11:00am - 11:59am | 4 | 0/0/0 | |

24030 | LAB 100 | 4 | 0/0/0 | ||

24031 | LAB 100 | 4 | 0/0/0 | ||

24032 | LAB 100 | 4 | 1/0/0 | ||

24033 | LAB 100 | 4 | 0/0/0 | ||

24034 | LAB 100 | 4 | 0/0/0 | ||

24036 | LAB 100 | 4 | 0/0/0 | ||

24037 | LAB 100 | 4 | 0/0/0 | ||

24038 | LAB 100 | Tu 1:00pm - 1:59pm | 4 | 0/0/0 | |

24039 | LAB 100 | Tu 1:00pm - 1:59pm | 4 | 0/0/0 | |

24040 | LAB 100 | Tu 1:00pm - 1:59pm | 4 | 0/0/0 | |

24041 | LAB 100 | Tu 1:00pm - 1:59pm | 4 | 0/0/0 | |

24042 | LAB 100 | Tu 2:00pm - 2:59pm | 4 | 0/0/0 | |

24043 | LAB 100 | Tu 2:00pm - 2:59pm | 4 | 0/0/0 | |

24044 | LAB 100 | 4 | 0/0/0 | ||

24554 | LAB 100 | Tu 2:00pm - 2:59pm | 4 | 0/0/0 | |

24556 | LAB 100 | Tu 2:00pm - 2:59pm | 4 | 0/0/0 | |

24558 | LAB 100 | Tu 3:00pm - 3:59pm | 4 | 0/0/0 | |

24669 | LAB 100 | Tu 4:00pm - 4:59pm | 4 | 0/0/0 | |

25072 | LAB 100 | We 9:00am - 9:59am | 4 | 0/0/0 | |

25074 | LAB 100 | We 10:00am - 10:59am | 4 | 0/0/0 | |

25076 | LAB 100 | We 11:00am - 11:59am | 4 | 0/0/0 | |

25078 | LAB 100 | We 9:00am - 9:59am | 4 | 0/0/0 | |

25080 | LAB 100 | Tu 5:00pm - 5:59pm | 4 | 0/0/0 | |

25082 | LAB 100 | Tu 6:00pm - 6:59pm | 4 | 0/0/0 | |

25094 | LAB 100 | We 8:00pm - 8:59pm | 4 | 0/0/0 | |

25096 | LAB 100 | We 8:00pm - 8:59pm | 4 | 0/0/0 | |

25090 | LAB 100 | Tu 7:00pm - 7:59pm | Wheeler 150 | 4 | 0/0/0 |

25092 | LAB 100 | We 8:00pm - 8:59pm | 4 | 0/0/0 | |

24035 | LAB 100 | 4 | 0/0/0 | ||

25084 | LAB 100 | Tu 7:00pm - 7:59pm | Valley Life Sciences 2050 | 4 | 0/0/0 |

25086 | LAB 100 | Tu 7:00pm - 7:59pm | Evans 342 | 4 | 0/0/0 |

24560 | LAB 100 | Tu 3:00pm - 3:59pm | Sutardja Dai 254 | 4 | 0/0/0 |

24562 | LAB 100 | Tu 3:00pm - 3:59pm | Cory 105 | 4 | 0/0/0 |

24564 | LAB 100 | Tu 3:00pm - 3:59pm | 4 | 0/0/0 | |

24566 | LAB 100 | Tu 4:00pm - 4:59pm | 4 | 0/0/0 |

### 2023 Spring STAT C102 001 LEC 001 - Data, Inference, and Decisions

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. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

25499 | LAB 102 | 4 | 0/0/0 |

### 2023 Spring STAT C131A 001 LEC 001 - Statistical Methods for Data Science

This course teaches a broad range of statistical methods that are used to solve data problems. 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. An important focus of the course is on statistical computing and reproducible statistical analysis. The course and lab include hands-on experience in analyzing real world data from the social, life, and physical sciences. The R statistical language is used.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 65 | 65 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

25832 | LAB 131 | MoWe 10:00am - 10:59am | Evans 334 | 4 | 32/32/0 |

25833 | LAB 131 | MoWe 3:00pm - 3:59pm | Evans 334 | 4 | 33/33/0 |

### 2023 Spring STAT 133 001 LEC 001 - Concepts in Computing with Data

An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 180 | 182 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23282 | LAB 133 | Th 9:00am - 10:59am | Evans 340 | 3 | 30/30/0 |

23283 | LAB 133 | Th 10:00am - 11:59am | Evans 334 | 3 | 30/29/0 |

23284 | LAB 133 | Th 11:00am - 12:59pm | Evans 340 | 3 | 31/30/0 |

23285 | LAB 133 | Th 1:00pm - 2:59pm | Evans 340 | 3 | 30/30/0 |

23286 | LAB 133 | Th 2:00pm - 3:59pm | Evans 334 | 3 | 30/30/0 |

23287 | LAB 133 | Th 3:00pm - 4:59pm | Evans 340 | 3 | 30/33/0 |

### 2023 Spring STAT 134 001 LEC 001 - Concepts of Probability

An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 360 | 349 | 0 |

### 2023 Spring STAT 135 001 LEC 001 - Concepts of Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 180 | 130 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23301 | LAB 135 | Fr 12:00pm - 1:59pm | Dwinelle 79 | 4 | 30/24/0 |

23302 | LAB 135 | Fr 12:00pm - 1:59pm | Stanley 179 | 4 | 30/24/0 |

23303 | LAB 135 | Fr 2:00pm - 3:59pm | Wheeler 30 | 4 | 30/25/0 |

23304 | LAB 135 | Fr 2:00pm - 3:59pm | Stanley 179 | 4 | 30/15/0 |

23305 | LAB 135 | Fr 4:00pm - 5:59pm | Wheeler 30 | 4 | 30/20/0 |

23300 | LAB 135 | Fr 3:00pm - 4:59pm | Evans 334 | 4 | 30/22/0 |

### 2023 Spring STAT C140 001 LEC 001 - Probability for Data Science

An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

### 2023 Spring STAT 150 001 LEC 001 - Stochastic Processes

Random walks, discrete time Markov chains, Poisson processes. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 100 | 77 | 0 |

### 2023 Spring STAT 151A 001 LEC 001 - Linear Modelling: Theory and Applications

A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 70 | 68 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

31689 | LAB 151 | We 1:00pm - 2:59pm | Evans 340 | 4 | 35/35/0 |

31690 | LAB 151 | We 3:00pm - 4:59pm | Evans 342 | 4 | 35/33/0 |

### 2023 Spring STAT 153 001 LEC 001 - Introduction to Time Series

An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 70 | 68 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23310 | LAB 153 | Fr 4:00pm - 5:59pm | Wheeler 200 | 4 | 35/0/0 |

23309 | LAB 153 | Fr 1:00pm - 2:59pm | Hearst Gym 242 | 4 | 35/0/0 |

23307 | LAB 153 | Fr 12:00pm - 1:59pm | Mulford 240 | 4 | 35/33/0 |

23308 | LAB 153 | Fr 3:00pm - 4:59pm | Evans 342 | 4 | 35/35/0 |

### 2023 Spring STAT 154 001 LEC 001 - Modern Statistical Prediction and Machine Learning

Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 41 | 36 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23312 | LAB 154 | Mo 12:00pm - 1:59pm | Evans 342 | 4 | 18/18/0 |

24513 | LAB 154 | Mo 3:00pm - 4:59pm | Evans 342 | 4 | 23/18/0 |

### 2023 Spring STAT 155 001 LEC 001 - Game Theory

General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 100 | 76 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

33789 | LAB 201 | Mo 3:00pm - 4:59pm | Evans 330 | 4 | 30/15/0 |

### 2023 Spring STAT 157 001 SEM 001 - Seminar on Topics in Probability and Statistics

Substantial student participation required. The topics to be covered each semester that the course may be offered will be announced by the middle of the preceding semester; see departmental bulletins. Recent topics include: Bayesian statistics, statistics and finance, random matrix theory, high-dimensional statistics.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 81 | 81 | 0 |

### 2023 Spring STAT 158 001 LEC 001 - Experimental Design

This course will review the statistical foundations of randomized experiments and study principles for addressing common setbacks in experimental design and analysis in practice. We will cover the notion of potential outcomes for causal inference and the Fisherian principles for experimentation (randomization, blocking, and replications). We will also cover experiments with complex structures (clustering in units, factorial design, hierarchy in treatments, sequential assignment, etc). We will also address practical complications in experiments, including noncompliance, missing data, and measurement error.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 35 | 23 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

33448 | LAB 158 | We 9:00am - 10:59am | Evans 344 | 4 | 17/12/0 |

33449 | LAB 158 | We 12:00pm - 1:59pm | Evans 344 | 4 | 17/11/0 |

32934 | LAB 158 | We 9:00am - 10:59am | Evans 344 | 4 | 18/0/0 |

32935 | LAB 158 | We 12:00pm - 1:59pm | Evans 344 | 4 | 18/0/0 |

### 2023 Spring STAT 159 001 LEC 001 - Reproducible and Collaborative Statistical Data Science

A project-based introduction to statistical data analysis. Through case studies, computer laboratories, and a term project, students will learn practical techniques and tools for producing statistically sound and appropriate, reproducible, and verifiable computational answers to scientific questions. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git, Python, and LaTeX.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 66 | 64 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

25671 | LAB 159 | Fr 9:00am - 10:59am | Evans 340 | 4 | 36/35/0 |

25672 | LAB 159 | Fr 12:00pm - 1:59pm | Evans 340 | 4 | 30/29/0 |

### 2023 Spring STAT C200C 001 LEC 001 - Principles and Techniques of Data Science

Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. 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. Research term project.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24569 | LAB 200 | 4 | 0/0/0 |

### 2023 Spring STAT C205B 001 LEC 001 - Probability Theory

The course is designed as a sequence with with Statistics C205A/Mathematics C218A with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 10 | 8 | 0 |

### 2023 Spring STAT C206B 001 LEC 001 - Advanced Topics in Probability and Stochastic Processes

The topics of this course change each semester, and multiple sections may be offered. Advanced topics in probability offered according to students demand and faculty availability.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 15 | 10 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

33580 | LAB 151 | Fr 1:00pm - 2:59pm | Evans 334 | 4 | 0/0/0 |

33581 | LAB 151 | Fr 3:00pm - 4:59pm | Evans 334 | 4 | 0/0/0 |

### 2023 Spring STAT 210B 001 LEC 001 - Theoretical Statistics

Introduction to modern theory of statistics; empirical processes, influence functions, M-estimation, U and V statistics and associated stochastic decompositions; non-parametric function estimation and associated minimax theory; semiparametric models; Monte Carlo methods and bootstrap methods; distributionfree and equivariant procedures; topics in machine learning. Topics covered may vary with instructor.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 45 | 39 | 0 |

### 2023 Spring STAT 215B 001 LEC 001 - Statistical Models: Theory and Application

Course builds on 215A in developing critical thinking skills and the techniques of advanced applied statistics. Particular topics vary with instructor. Examples of possible topics include planning and design of experiments, ANOVA and random effects models, splines, classification, spatial statistics, categorical data analysis, survival analysis, and multivariate analysis.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 25 | 14 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

23323 | LAB 215 | Fr 9:00am - 10:59am | Evans 344 | 4 | 25/14/0 |

### 2023 Spring STAT 222 001 SEM 001 - Masters of Statistics Capstone Project

The capstone project is part of the masters degree program in statistics. Students engage in professionally-oriented group research under the supervision of a research advisor. The research synthesizes the statistical, computational, economic, and social issues involved in solving complex real-world problems.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 50 | 47 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24199 | LAB 222 | Th 4:00pm - 4:59pm | Mulford 240 | 4 | 25/24/0 |

25807 | LAB 222 | Th 5:00pm - 5:59pm | Mulford 240 | 4 | 25/23/0 |

### 2023 Spring STAT 230A 001 LEC 001 - Linear Models

Theory of least squares estimation, interval estimation, and tests under the general linear fixed effects model with normally distributed errors. Large sample theory for non-normal linear models. Two and higher way layouts, residual analysis. Effects of departures from the underlying assumptions. Robust alternatives to least squares.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 50 | 47 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

25136 | LAB 230 | Fr 3:00pm - 4:59pm | Evans 344 | 4 | 26/23/0 |

23326 | LAB 230 | Fr 12:00pm - 1:59pm | Evans 344 | 4 | 24/24/0 |

### 2023 Spring STAT 232 001 LEC 001 - Experimental Design

This course will review the statistical foundations of randomized experiments and study principles for addressing common setbacks in experimental design and analysis in practice. We will cover the notion of potential outcomes for causal inference and the Fisherian principles for experimentation (randomization, blocking, and replications). We will also cover experiments with complex structures (clustering in units, factorial design, hierarchy in treatments, sequential assignment, etc). We will also address practical complications in experiments, including noncompliance, missing data, and measurement error.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 35 | 26 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

33444 | LAB 232 | We 12:00pm - 1:59pm | Evans 344 | 4 | 18/14/0 |

27281 | LAB 232 | We 9:00am - 10:59am | Evans 344 | 4 | 18/12/0 |

### 2023 Spring STAT 240 001 LEC 001 - Nonparametric and Robust Methods

Standard nonparametric tests and confidence intervals for continuous and categorical data; nonparametric estimation of quantiles; robust estimation of location and scale parameters. Efficiency comparison with the classical procedures.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 20 | 8 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

32932 | LAB 240 | We 1:00pm - 2:59pm | Evans 334 | 4 | 20/8/0 |

### 2023 Spring STAT C241B 001 LEC 001 - Advanced Topics in Learning and Decision Making

In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. This class will focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. 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.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 50 | 28 | 0 |

### 2023 Spring STAT 254 001 LEC 001 - Modern Statistical Prediction and Machine Learning

This course is about statistical learning methods and their use for data analysis. Upon completion, students will be able to build baseline models for real world data analysis problems, implement models using programming languages and draw conclusions from models. The course will cover principled statistical methodology for basic machine learning tasks such as regression, classification, dimension reduction and clustering. Methods discussed will include linear regression, subset selection, ridge regression, LASSO, logistic regression, kernel smoothing methods, tree based methods, bagging and boosting, neural networks, Bayesian methods, as well as inference techniques based on resampling, cross validation and sample splitting.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 29 | 28 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

33038 | LAB 254 | Mo 12:00pm - 1:59pm | Evans 342 | 4 | 17/16/0 |

33039 | LAB 254 | Mo 3:00pm - 4:59pm | Evans 342 | 4 | 12/12/0 |

### 2023 Spring STAT 259 001 LEC 001 - Reproducible and Collaborative Statistical Data Science

A project-based introduction to statistical data analysis. Through case studies, computer laboratories, and a term project, students will learn practical techniques and tools for producing statistically sound and appropriate, reproducible, and verifiable computational answers to scientific questions. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git, Python, and LaTeX.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 8 | 8 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

25674 | LAB 259 | Fr 9:00am - 10:59am | Evans 340 | 4 | 1/1/0 |

25675 | LAB 259 | Fr 12:00pm - 1:59pm | Evans 340 | 4 | 7/7/0 |

### 2023 Spring STAT 260 001 LEC 001 - Topics in Probability and Statistics

Special topics in probability and statistics offered according to student demand and faculty availability.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 7 | 7 | 0 |

### 2023 Spring STAT 278B 001 SEM 001 - Statistics Research Seminar

Special topics, by means of lectures and informational conferences.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 15 | 8 | 0 |

### 2023 Spring STAT 278B 002 SEM 002 - Statistics Research Seminar

Special topics, by means of lectures and informational conferences.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 15 | 4 | 0 |

### 2023 Spring STAT 375 001 LEC 001 - Professional Preparation: Teaching of Probability and Statistics

Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 21 | 19 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24635 | LAB 375 | 21/19/0 |

### 2023 Spring STAT 375 002 LEC 002 - Professional Preparation: Teaching of Probability and Statistics

Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 19 | 13 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

27420 | LAB 375 | 19/13/0 |

### 2022 Fall STAT C8 001 LEC 001 - Foundations of Data Science

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24963 | LAB 8 | 4 | 0/0/0 |

### 2022 Fall STAT 20 001 LEC 001 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 98 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

22970 | LAB 20 | WeFr 9:00am - 9:59am | Moffitt Library 145 | 4 | 96/98/0 |

### 2022 Fall STAT 20 002 LEC 002 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 86 | 87 | 0 |

### 2022 Fall STAT 20 004 LEC 004 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 85 | 86 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

32670 | LAB 20 | WeFr 9:00am - 9:59am | Davis 534 | 4 | 85/86/0 |

### 2022 Fall STAT 20 005 LEC 005 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 96 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

32672 | LAB 20 | WeFr 11:00am - 11:59am | Moffitt Library 145 | 4 | 96/96/0 |

### 2022 Fall STAT 20 006 LEC 006 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 99 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

32674 | LAB 20 | WeFr 1:00pm - 1:59pm | Moffitt Library 145 | 4 | 96/99/0 |

### 2022 Fall STAT 20 007 LEC 007 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 86 | 91 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

32676 | LAB 20 | WeFr 3:00pm - 3:59pm | Moffitt Library 145 | 4 | 86/91/0 |

### 2022 Fall STAT 20 008 LEC 008 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 96 | 97 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

32678 | LAB 20 | WeFr 5:00pm - 5:59pm | Moffitt Library 145 | 4 | 95/97/0 |

### 2022 Fall STAT 20 009 LEC 009 - Introduction to Probability and Statistics

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 122 | 120 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

32680 | LAB 20 | WeFr 10:00am - 10:59am | Wheeler 212 | 4 | 122/120/0 |

### 2022 Fall STAT 33A 001 LEC 001 - Introduction to Programming in R

An introduction to the R statistical software for students with minimal prior experience with programming. This course prepares students for data analysis with R. The focus is on the computational model that underlies the R language with the goal of providing a foundation for coding. 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. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 100 | 85 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24451 | LAB 33 | We 9:00am - 9:59am | Evans 342 | 1 | 25/18/0 |

24452 | LAB 33 | We 10:00am - 10:59am | Evans 342 | 1 | 25/21/0 |

24453 | LAB 33 | We 2:00pm - 2:59pm | Evans 342 | 1 | 25/21/0 |

24454 | LAB 33 | We 3:00pm - 3:59pm | Evans 342 | 1 | 25/25/0 |

### 2022 Fall STAT 33B 001 LEC 001 - Introduction to Advanced Programming in R

The course is designed primarily for those who are already familiar with programming in another language, such as python, and want to understand how R works, and for those who already know the basics of R programming and want to gain a more in-depth understanding of the language in order to improve their coding. The focus is on the underlying paradigms in R, such as functional programming, atomic vectors, complex data structures, environments, and object systems. 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.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

O | 100 | 90 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

24456 | LAB 33 | Fr 9:00am - 9:59am | Evans 342 | 1 | 25/22/0 |

24780 | LAB 33 | Fr 10:00am - 10:59am | Evans 342 | 1 | 25/25/0 |

26310 | LAB 33 | Fr 2:00pm - 2:59pm | Cory 289 | 1 | 25/23/0 |

26311 | LAB 33 | Fr 3:00pm - 3:59pm | Cory 289 | 1 | 25/20/0 |

### 2022 Fall STAT C102 001 LEC 001 - Data, Inference, and Decisions

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. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.

Status | Limit | Enrolled | Waitlist |
---|---|---|---|

C | 0 | 0 | 0 |

Class # | Section | Date And Times | Location | Units | LIM/ENR/WAIT |
---|---|---|---|---|---|

25153 | LAB 102 | Mo 2:00pm - 2:59pm | Etcheverry 3105 | 4 | 0/0/0 |

26210 | LAB 102 | TuTh 8:00am - 9:29am | Internet/Online | 4 | 0/0/0 |

25034 | LAB 102 | Mo 9:00am - 9:59am | Etcheverry 3105 | 4 | 0/0/0 |

25036 | LAB 102 | Mo 10:00am - 10:59am | Evans 9 | 4 | 0/0/0 |

25038 | LAB 102 | Mo 11:00am - 11:59am | Hearst Gym 242 | 4 | 0/0/0 |

25151 | LAB 102 | Mo 1:00pm - 1:59pm | Moffitt Library 106 | 4 | 0/0/0 |

25155 | LAB 102 | Mo 3:00pm - 3:59pm | Etcheverry 3105 | 4 | 0/0/0 |

25157 | LAB 102 | Mo 4:00pm - 4:59pm | Etcheverry 3105 | 4 | 0/0/0 |