# Courses

### 2020 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 |
---|---|---|---|

O | 150 | 148 | 0 |

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

23776 | LAB 133 | 3 | 0/0/0 | ||

23777 | LAB 133 | 3 | 0/0/0 | ||

23778 | LAB 133 | 3 | 0/0/0 | ||

23779 | LAB 133 | 3 | 0/0/0 | ||

23782 | LAB 133 | We 11:00am - 12:59pm | Internet/Online | 3 | 44/44/0 |

23783 | LAB 133 | We 1:00pm - 2:59pm | Internet/Online | 3 | 40/30/0 |

23784 | LAB 133 | We 3:00pm - 4:59pm | Internet/Online | 3 | 40/35/0 |

23786 | LAB 133 | Th 9:00am - 10:59am | Internet/Online | 3 | 40/39/0 |

23787 | LAB 133 | 3 | 0/0/0 | ||

23788 | LAB 133 | 3 | 0/0/0 |

### 2020 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 | 300 | 277 | 0 |

### 2020 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 | 180 | 122 | 0 |

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

23734 | LAB 135 | Fr 2:00pm - 2:59pm | Internet/Online | 4 | 30/24/0 |

23735 | LAB 135 | Fr 2:00pm - 2:59pm | Internet/Online | 4 | 30/17/0 |

23796 | LAB 135 | Fr 10:00am - 10:59am | Internet/Online | 4 | 30/19/0 |

23797 | LAB 135 | Fr 10:00am - 10:59am | Internet/Online | 4 | 30/21/0 |

24645 | LAB 135 | Fr 12:00pm - 12:59pm | Internet/Online | 4 | 30/21/0 |

24646 | LAB 135 | Fr 12:00pm - 12:59pm | Internet/Online | 4 | 30/20/0 |

### 2020 Fall STAT 140 001 LEC 001 - Probability for Data Science

An introduction to probability, emphasizing the combined use of mathematics and programming to solve problems. Random variables, discrete and continuous families of distributions. Bounds and approximations. Dependence, conditioning, Bayes methods. Convergence, Markov chains. Least squares prediction. Random permutations, symmetry, order statistics. Use of numerical computation, graphics, simulation, and computer algebra.

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

O | 335 | 332 | 0 |

### 2020 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 | 92 | 78 | 0 |

### 2020 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 | 100 | 64 | 0 |

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

23687 | LAB 151 | Fr 9:00am - 10:59am | Internet/Online | 4 | 35/31/0 |

23688 | LAB 151 | Fr 11:00am - 12:59pm | Internet/Online | 4 | 35/33/0 |

25344 | LAB 151 | Fr 1:00pm - 2:59pm | Internet/Online | 4 | 0/0/0 |

25345 | LAB 151 | Fr 3:00pm - 4:59pm | Internet/Online | 4 | 0/0/0 |

### 2020 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 |
---|---|---|---|

O | 140 | 127 | 0 |

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

25390 | LAB 153 | Fr 9:00am - 10:59am | Internet/Online | 4 | 35/31/0 |

25391 | LAB 153 | Fr 11:00am - 12:59pm | Internet/Online | 4 | 35/34/0 |

25392 | LAB 153 | Fr 1:00pm - 2:59pm | Internet/Online | 4 | 35/29/0 |

25393 | LAB 153 | Fr 3:00pm - 4:59pm | Internet/Online | 4 | 35/33/0 |

### 2020 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 | 70 | 52 | 0 |

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

23669 | LAB 154 | Mo 10:00am - 11:59am | Internet/Online | 4 | 35/24/0 |

23670 | LAB 154 | Mo 12:00pm - 1:59pm | Internet/Online | 4 | 35/27/0 |

25564 | LAB 154 | Mo 2:00pm - 3:59pm | Internet/Online | 4 | 1/1/0 |

26476 | LAB 154 | Mo 2:00pm - 3:59pm | Internet/Online | 4 | 0/0/0 |

### 2020 Fall 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 | 62 | 0 |

### 2020 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 | 35 | 19 | 0 |

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

33495 | LAB 156 | Tu 2:00pm - 3:59pm | Internet/Online | 4 | 18/11/0 |

33496 | LAB 156 | Tu 4:00pm - 5:59pm | Internet/Online | 4 | 18/8/0 |

### 2020 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 |
---|---|---|---|

C | 15 | 15 | 0 |

### 2020 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 | 53 | 0 |

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

23680 | LAB 201 | Mo 12:00pm - 1:59pm | Evans 344 | 4 | 35/31/0 |

23681 | LAB 201 | Mo 3:00pm - 4:59pm | Evans 344 | 4 | 35/22/0 |

### 2020 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 | 52 | 0 |

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

23683 | LAB 201 | We 11:00am - 12:59pm | Evans 344 | 4 | 35/29/0 |

23684 | LAB 201 | We 1:00pm - 2:59pm | Evans 344 | 4 | 35/23/0 |

### 2020 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 | 30 | 27 | 1 |

### 2020 Fall STAT C206A 001 LEC 001 - Advanced Topics in Probability and Stochastic Process

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 | 10 | 7 | 0 |

### 2020 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 | 140 | 107 | 0 |

### 2020 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 | 42 | 37 | 0 |

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

23721 | LAB 215 | Fr 11:00am - 12:59pm | Internet/Online | 4 | 42/37/0 |

### 2020 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 | 80 | 57 | 0 |

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

23822 | LAB 243 | Fr 12:00pm - 1:59pm | Internet/Online | 4 | 40/33/0 |

23823 | LAB 243 | Fr 2:00pm - 3:59pm | Internet/Online | 4 | 40/24/0 |

34333 | LAB 243 | 4 | 1/0/0 |

### 2020 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 | 36 | 30 | 0 |

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

33498 | LAB 256 | Tu 2:00pm - 3:59pm | Internet/Online | 4 | 18/13/0 |

33499 | LAB 256 | Tu 4:00pm - 5:59pm | Internet/Online | 4 | 18/17/0 |

### 2020 Fall 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 |
---|---|---|---|

O | 19 | 18 | 0 |

### 2020 Fall STAT 260 002 LEC 002 - Topics in Probability and Statistics

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

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

O | 6 | 0 | 0 |

### 2020 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 | 1 | 0 |

### 2020 Fall STAT 278B 001 SEM 001 - Statistics Research Seminar

Special topics, by means of lectures and informational conferences.

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

O | 35 | 13 | 0 |

### 2020 Fall STAT 278B 002 SEM 002 - Statistics Research Seminar

Special topics, by means of lectures and informational conferences.

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

O | 10 | 7 | 0 |

### 2020 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 | 30 | 20 | 0 |

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

24035 | LAB 375 | 30/20/0 |

### 2020 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. Binomial distribution. Interval estimation. Some standard significance tests.

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

O | 300 | 298 | 0 |

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

21860 | LAB 2 | TuTh 8:00am - 8:59am | Evans 332 | 4 | 24/23/0 |

21861 | LAB 2 | TuTh 9:00am - 9:59am | Evans 332 | 4 | 25/25/0 |

21862 | LAB 2 | TuTh 10:00am - 10:59am | Evans 332 | 4 | 25/26/0 |

21863 | LAB 2 | TuTh 11:00am - 11:59am | Evans 332 | 4 | 24/24/0 |

21864 | LAB 2 | TuTh 12:00pm - 12:59pm | Wheeler 124 | 4 | 25/25/0 |

21865 | LAB 2 | TuTh 1:00pm - 1:59pm | Wheeler 106 | 4 | 26/26/0 |

21866 | LAB 2 | TuTh 2:00pm - 2:59pm | Evans 332 | 4 | 25/25/0 |

21867 | LAB 2 | TuTh 3:00pm - 3:59pm | Evans 332 | 4 | 25/25/0 |

21868 | LAB 2 | TuTh 8:00am - 8:59am | Evans 334 | 4 | 25/24/0 |

21869 | LAB 2 | TuTh 2:00pm - 2:59pm | Evans 330 | 4 | 25/25/0 |

21870 | LAB 2 | TuTh 3:00pm - 3:59pm | Evans 330 | 4 | 25/25/0 |

21871 | LAB 2 | TuTh 5:00pm - 5:59pm | Evans 334 | 4 | 25/25/0 |

### 2020 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 | 1350 | 1350 | 0 |

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

22141 | LAB 8 | We 12:00pm - 1:59pm | Evans 458 | 4 | 30/0/0 |

22142 | LAB 8 | We 12:00pm - 1:59pm | Cory 105 | 4 | 30/0/0 |

22143 | LAB 8 | We 12:00pm - 1:59pm | Evans B6 | 4 | 30/0/0 |

22144 | LAB 8 | We 12:00pm - 1:59pm | Sutardja Dai 254 | 4 | 30/0/0 |

22145 | LAB 8 | We 2:00pm - 3:59pm | Evans 458 | 4 | 30/0/0 |

22146 | LAB 8 | We 2:00pm - 3:59pm | Cory 105 | 4 | 30/0/0 |

22147 | LAB 8 | We 2:00pm - 3:59pm | Evans B6 | 4 | 30/0/0 |

22148 | LAB 8 | We 2:00pm - 3:59pm | Sutardja Dai 254 | 4 | 30/0/0 |

22149 | LAB 8 | We 4:00pm - 5:59pm | Evans 458 | 4 | 30/0/0 |

22150 | LAB 8 | We 4:00pm - 5:59pm | Cory 105 | 4 | 30/0/0 |

22151 | LAB 8 | We 4:00pm - 5:59pm | Evans B6 | 4 | 30/0/0 |

22152 | LAB 8 | We 4:00pm - 5:59pm | Sutardja Dai 254 | 4 | 30/0/0 |

22153 | LAB 8 | We 6:00pm - 7:59pm | Evans 458 | 4 | 30/0/0 |

22154 | LAB 8 | We 6:00pm - 7:59pm | Cory 105 | 4 | 30/0/0 |

22155 | LAB 8 | We 6:00pm - 7:59pm | Evans B6 | 4 | 30/0/0 |

22156 | LAB 8 | We 6:00pm - 7:59pm | Sutardja Dai 254 | 4 | 30/0/0 |

22157 | LAB 8 | Th 8:00am - 9:59am | Evans 458 | 4 | 30/0/0 |

22158 | LAB 8 | Th 8:00am - 9:59am | Cory 105 | 4 | 30/0/0 |

22159 | LAB 8 | Th 8:00am - 9:59am | Evans B6 | 4 | 30/0/0 |

22160 | LAB 8 | Th 8:00am - 9:59am | Sutardja Dai 254 | 4 | 30/0/0 |

22667 | LAB 8 | Th 10:00am - 11:59am | Evans 458 | 4 | 30/0/0 |

22668 | LAB 8 | Th 10:00am - 11:59am | Cory 105 | 4 | 30/0/0 |

22669 | LAB 8 | Th 10:00am - 11:59am | Evans B6 | 4 | 30/0/0 |

22670 | LAB 8 | Th 10:00am - 11:59am | Sutardja Dai 254 | 4 | 30/0/0 |

22982 | LAB 8 | Th 12:00pm - 1:59pm | Evans 458 | 4 | 30/0/0 |

23030 | LAB 8 | Th 12:00pm - 1:59pm | Cory 105 | 4 | 30/0/0 |

23033 | LAB 8 | Th 12:00pm - 1:59pm | Evans B6 | 4 | 30/0/0 |

23034 | LAB 8 | Th 12:00pm - 1:59pm | Sutardja Dai 254 | 4 | 30/0/0 |

23035 | LAB 8 | Th 2:00pm - 3:59pm | Evans 458 | 4 | 30/0/0 |

23036 | LAB 8 | Th 2:00pm - 3:59pm | Cory 105 | 4 | 30/0/0 |

23037 | LAB 8 | Th 2:00pm - 3:59pm | Evans B6 | 4 | 30/0/0 |

23038 | LAB 8 | Th 2:00pm - 3:59pm | Sutardja Dai 254 | 4 | 30/0/0 |

23350 | LAB 8 | Th 4:00pm - 5:59pm | Evans 458 | 4 | 30/0/0 |

23351 | LAB 8 | Th 4:00pm - 5:59pm | Cory 105 | 4 | 30/0/0 |

23352 | LAB 8 | Th 4:00pm - 5:59pm | Evans B6 | 4 | 30/0/0 |

23353 | LAB 8 | Th 4:00pm - 5:59pm | Sutardja Dai 254 | 4 | 30/0/0 |

23354 | LAB 8 | Th 6:00pm - 7:59pm | Evans 458 | 4 | 30/0/0 |

23355 | LAB 8 | Th 6:00pm - 7:59pm | Cory 105 | 4 | 30/0/0 |

23397 | LAB 8 | Th 6:00pm - 7:59pm | Evans B6 | 4 | 30/0/0 |

24278 | LAB 8 | Fr 12:00pm - 1:59pm | Sutardja Dai 254 | 4 | 30/0/0 |

24279 | LAB 8 | Fr 2:00pm - 3:59pm | Evans 458 | 4 | 30/0/0 |

24280 | LAB 8 | Fr 12:00pm - 1:59pm | Evans B6 | 4 | 30/0/0 |

24560 | LAB 8 | Fr 2:00pm - 3:59pm | Sutardja Dai 254 | 4 | 30/0/0 |

24561 | LAB 8 | Fr 12:00pm - 1:59pm | Evans 458 | 4 | 30/0/0 |

24562 | LAB 8 | Fr 12:00pm - 1:59pm | Cory 105 | 4 | 30/0/0 |

24563 | LAB 8 | Th 2:00pm - 3:59pm | 4 | 30/0/0 | |

24564 | LAB 8 | Fr 8:00am - 9:59am | 4 | 30/0/0 | |

24565 | LAB 8 | Fr 2:00pm - 3:59pm | 4 | 30/0/0 | |

24566 | LAB 8 | Fr 2:00pm - 3:59pm | 4 | 30/0/0 | |

24567 | LAB 8 | Fr 12:00pm - 1:59pm | 4 | 30/0/0 | |

32816 | LAB 8 | 4 | 1350/1350/0 |

### 2020 Spring 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 |
---|---|---|---|

O | 350 | 349 | 0 |

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

21873 | LAB 20 | MoWe 8:00am - 8:59am | Dwinelle 234 | 4 | 25/24/0 |

21874 | LAB 20 | MoWe 8:00am - 8:59am | Cory 285 | 4 | 25/25/0 |

21875 | LAB 20 | MoWe 9:00am - 9:59am | Evans 334 | 4 | 25/25/0 |

21876 | LAB 20 | MoWe 10:00am - 10:59am | Hildebrand B51 | 4 | 25/25/0 |

21877 | LAB 20 | MoWe 10:00am - 10:59am | Hildebrand B56 | 4 | 25/25/0 |

21878 | LAB 20 | MoWe 11:00am - 11:59am | Evans 334 | 4 | 25/25/0 |

21879 | LAB 20 | MoWe 12:00pm - 12:59pm | Evans 9 | 4 | 25/25/0 |

21880 | LAB 20 | MoWe 12:00pm - 12:59pm | Evans 334 | 4 | 25/25/0 |

21881 | LAB 20 | MoWe 1:00pm - 1:59pm | Evans 332 | 4 | 25/25/0 |

21882 | LAB 20 | MoWe 1:00pm - 1:59pm | Evans 334 | 4 | 25/25/0 |

21883 | LAB 20 | MoWe 2:00pm - 2:59pm | Evans 332 | 4 | 25/25/0 |

21884 | LAB 20 | MoWe 2:00pm - 2:59pm | Evans 334 | 4 | 25/25/0 |

21885 | LAB 20 | MoWe 3:00pm - 3:59pm | Evans 332 | 4 | 25/25/0 |

21886 | LAB 20 | MoWe 3:00pm - 3:59pm | Evans 334 | 4 | 25/25/0 |

### 2020 Spring 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 |
---|---|---|---|

O | 158 | 157 | 0 |

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

32519 | LAB 20 | TuTh 8:00am - 8:59am | Evans 330 | 4 | 26/24/0 |

32520 | LAB 20 | TuTh 9:00am - 9:59am | Evans 330 | 4 | 28/27/0 |

32521 | LAB 20 | TuTh 10:00am - 10:59am | Evans 330 | 4 | 26/25/0 |

32522 | LAB 20 | TuTh 11:00am - 11:59am | Evans 330 | 4 | 27/27/0 |

32523 | LAB 20 | TuTh 12:00pm - 12:59pm | Evans 330 | 4 | 26/26/0 |

32524 | LAB 20 | TuTh 1:00pm - 1:59pm | Evans 330 | 4 | 28/28/0 |

### 2020 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 | 140 | 127 | 0 |

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

31223 | LAB 33 | We 10:00am - 10:59am | Evans 342 | 1 | 35/30/0 |

31224 | LAB 33 | We 9:00am - 9:59am | Evans 342 | 1 | 35/34/0 |

31225 | LAB 33 | We 11:00am - 11:59am | Evans 342 | 1 | 35/31/0 |

31226 | LAB 33 | We 12:00pm - 12:59pm | Evans 342 | 1 | 35/32/0 |

### 2020 Spring 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 | 140 | 69 | 0 |

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

31231 | LAB 33 | 1 | 0/0/0 | ||

31232 | LAB 33 | Fr 10:00am - 10:59am | Evans 342 | 1 | 35/20/0 |

31233 | LAB 33 | Fr 11:00am - 11:59am | Evans 340 | 1 | 35/27/0 |

31234 | LAB 33 | Fr 12:00pm - 12:59pm | Hearst Gym 242 | 1 | 35/22/0 |

### 2020 Spring STAT 88 001 LEC 001 - Probability and Mathematical Statistics in Data Science

In this connector course we will state precisely and prove results discovered while exploring data in Data 8. 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.

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

O | 300 | 287 | 0 |

### 2020 Spring STAT 89A 001 LEC 001 - Linear Algebra for Data Science

An introduction to linear algebra for data science. 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.); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc.

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

O | 100 | 73 | 0 |

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

23377 | LAB 89 | Mo 8:00am - 9:59am | Evans 342 | 4 | 25/14/0 |

23378 | LAB 89 | Mo 10:00am - 11:59am | Evans 342 | 4 | 25/22/0 |

### 2020 Spring STAT 102 001 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 |
---|---|---|---|

O | 160 | 159 | 0 |

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

32461 | LAB 102 | We 9:00am - 9:59am | Evans 344 | 4 | 29/28/0 |

32464 | LAB 102 | We 10:00am - 10:59am | Evans 344 | 4 | 27/25/0 |

32466 | LAB 102 | We 11:00am - 11:59am | Evans 344 | 4 | 28/27/1 |

32468 | LAB 102 | We 12:00pm - 12:59pm | Evans 344 | 4 | 27/26/0 |

32470 | LAB 102 | We 1:00pm - 1:59pm | Evans 344 | 4 | 27/26/0 |

32473 | LAB 102 | We 2:00pm - 2:59pm | Evans 344 | 4 | 28/27/0 |

32475 | LAB 102 | 4 | 0/0/0 | ||

32477 | LAB 102 | 4 | 0/0/0 |

### 2020 Spring STAT 131A 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 |
---|---|---|---|

O | 70 | 65 | 0 |

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

21892 | LAB 131 | TuTh 4:00pm - 4:59pm | Evans 332 | 4 | 38/32/0 |

21893 | LAB 131 | 4 | 0/0/0 | ||

32429 | LAB 131 | 4 | 0/0/0 | ||

32430 | LAB 131 | TuTh 5:00pm - 5:59pm | Evans 332 | 4 | 38/33/0 |

32431 | LAB 131 | 4 | 0/0/0 | ||

32432 | LAB 131 | 4 | 0/0/0 |

### 2020 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 |
---|---|---|---|

O | 250 | 212 | 0 |

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

21895 | LAB 133 | Th 9:00am - 10:59am | Evans 340 | 3 | 31/36/0 |

21896 | LAB 133 | 3 | 0/0/0 | ||

21897 | LAB 133 | Th 11:00am - 12:59pm | Evans 340 | 3 | 30/25/0 |

21898 | LAB 133 | Th 11:00am - 12:59pm | Evans 342 | 3 | 30/28/0 |

21899 | LAB 133 | Th 1:00pm - 2:59pm | Evans 340 | 3 | 30/30/0 |

21900 | LAB 133 | Th 3:00pm - 4:59pm | Evans 340 | 3 | 31/27/0 |

21901 | LAB 133 | 3 | 0/0/0 | ||

21902 | LAB 133 | Fr 11:00am - 12:59pm | Evans 342 | 3 | 30/30/0 |

23060 | LAB 133 | Fr 3:00pm - 4:59pm | Evans 340 | 3 | 30/12/0 |

23061 | LAB 133 | Fr 1:00pm - 2:59pm | Evans 342 | 3 | 30/24/0 |

### 2020 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 | 400 | 320 | 0 |

### 2020 Spring 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 | 210 | 186 | 0 |

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

21915 | LAB 135 | Fr 12:00pm - 1:59pm | Evans 330 | 4 | 35/34/0 |

21916 | LAB 135 | Fr 12:00pm - 1:59pm | Etcheverry 3109 | 4 | 35/29/0 |

21917 | LAB 135 | Fr 2:00pm - 3:59pm | Hildebrand B51 | 4 | 35/27/0 |

21918 | LAB 135 | Fr 2:00pm - 3:59pm | Evans 9 | 4 | 35/32/0 |

21919 | LAB 135 | Fr 4:00pm - 5:59pm | Evans 9 | 4 | 35/33/0 |

21920 | LAB 135 | Fr 4:00pm - 5:59pm | Evans 70 | 4 | 35/31/0 |

### 2020 Spring STAT 140 001 LEC 001 - Probability for Data Science

An introduction to probability, emphasizing the combined use of mathematics and programming to solve problems. Random variables, discrete and continuous families of distributions. Bounds and approximations. Dependence, conditioning, Bayes methods. Convergence, Markov chains. Least squares prediction. Random permutations, symmetry, order statistics. Use of numerical computation, graphics, simulation, and computer algebra.

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

O | 297 | 271 | 0 |

### 2020 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 | 73 | 72 | 0 |

### 2020 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 | 140 | 95 | 0 |

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

21922 | LAB 151 | Fr 9:00am - 10:59am | Evans 332 | 4 | 35/25/0 |

21923 | LAB 151 | Fr 11:00am - 12:59pm | Evans 332 | 4 | 35/29/0 |

22601 | LAB 151 | Fr 1:00pm - 2:59pm | Evans 332 | 4 | 35/21/0 |

22602 | LAB 151 | Fr 3:00pm - 4:59pm | Evans 332 | 4 | 35/20/0 |

### 2020 Spring STAT 152 001 LEC 001 - Sampling Surveys

Theory and practice of sampling from finite populations. Simple random, stratified, cluster, and double sampling. Sampling with unequal probabilities. Properties of various estimators including ratio, regression, and difference estimators. Error estimation for complex samples.

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

O | 70 | 53 | 0 |

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

21925 | LAB 152 | Mo 1:00pm - 2:59pm | Evans 340 | 4 | 35/30/0 |

21926 | LAB 152 | Mo 3:00pm - 4:59pm | Evans 340 | 4 | 35/23/0 |

### 2020 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 | 140 | 140 | 0 |

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

21928 | LAB 153 | Fr 9:00am - 10:59am | Evans 334 | 4 | 35/35/0 |

21929 | LAB 153 | Fr 11:00am - 12:59pm | Evans 334 | 4 | 35/35/0 |

21930 | LAB 153 | Fr 12:00pm - 1:59pm | Evans 344 | 4 | 35/35/0 |

21931 | LAB 153 | Fr 2:00pm - 3:59pm | Evans 344 | 4 | 35/35/0 |

### 2020 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 | 140 | 117 | 0 |

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

21933 | LAB 154 | Mo 9:00am - 10:59am | Evans 330 | 4 | 35/30/0 |

21934 | LAB 154 | Mo 11:00am - 12:59pm | Evans 330 | 4 | 35/33/0 |

### 2020 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 | 95 | 0 |

### 2020 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 |
---|---|---|---|

O | 15 | 13 | 0 |

### 2020 Spring STAT 158 001 LEC 001 - The Design and Analysis of Experiments

An introduction to the design and analysis of experiments. This course covers planning, conducting, and analyzing statistically designed experiments with an emphasis on hands-on experience. Standard designs studied include factorial designs, block designs, latin square designs, and repeated measures designs. Other topics covered include the principles of design, randomization, ANOVA, response surface methodoloy, and computer experiments.

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

O | 65 | 47 | 0 |

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

23299 | LAB 158 | Mo 9:00am - 10:59am | Evans 340 | 4 | 33/17/0 |

23300 | LAB 158 | Mo 11:00am - 12:59pm | Evans 340 | 4 | 32/30/0 |

### 2020 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 | 25 | 7 | 0 |

### 2020 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 | 30 | 9 | 0 |

### 2020 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 | 50 | 44 | 0 |