Course Location

Internet/Online

Course Units

4

Course number

2

Course description

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

Instructor(s)

Adam R Lucas

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

C | 448 | 466 | 0 |

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

19121 | LAB 2 | TuTh 9:00am - 9:59am | Evans 332 | 4 | 29/29/0 |

19122 | LAB 2 | TuTh 10:00am - 10:59am | Evans 332 | 4 | 28/28/0 |

19123 | LAB 2 | TuTh 10:00am - 10:59am | Evans 334 | 4 | 29/28/0 |

19124 | LAB 2 | TuTh 11:00am - 11:59am | Evans 332 | 4 | 30/30/0 |

19125 | LAB 2 | TuTh 11:00am - 11:59am | Evans 334 | 4 | 31/31/0 |

19126 | LAB 2 | TuTh 12:00pm - 12:59pm | Evans 332 | 4 | 31/29/0 |

19127 | LAB 2 | TuTh 12:00pm - 12:59pm | Evans 334 | 4 | 30/29/0 |

19128 | LAB 2 | TuTh 1:00pm - 1:59pm | Evans 332 | 4 | 30/30/0 |

21539 | LAB 2 | TuTh 1:00pm - 1:59pm | Evans 334 | 4 | 29/29/0 |

21540 | LAB 2 | TuTh 2:00pm - 2:59pm | Evans 332 | 4 | 30/30/0 |

21541 | LAB 2 | TuTh 2:00pm - 2:59pm | Evans 334 | 4 | 30/30/0 |

21542 | LAB 2 | TuTh 3:00pm - 3:59pm | Evans 332 | 4 | 28/27/0 |

21543 | LAB 2 | TuTh 3:00pm - 3:59pm | Evans 334 | 4 | 30/29/0 |

21818 | LAB 2 | TuTh 4:00pm - 4:59pm | Evans 332 | 4 | 29/28/0 |

22748 | LAB 2 | TuTh 4:00pm - 4:59pm | Evans 334 | 4 | 29/29/0 |

22749 | LAB 2 | TuTh 5:00pm - 5:59pm | Evans 332 | 4 | 31/30/0 |

Course Times

MoTuTh 9:00am - 9:59am

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

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.

Instructor(s)

Jeremy Sanchez

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

C | 96 | 99 | 0 |

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

19130 | LAB 20 | TuTh 10:00am - 10:59am | Moffitt Library 145 | 4 | 96/99/0 |

Course Times

MoWeFr 10:00am - 10:59am

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

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.

Instructor(s)

Andrea Massari

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

O | 96 | 94 | 0 |

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

21882 | LAB 20 | WeFr 11:00am - 11:59am | Moffitt Library 145 | 4 | 96/94/0 |

Course Times

MoWeFr 12:00pm - 12:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

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.

Instructor(s)

Andrea Massari

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

O | 96 | 95 | 0 |

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

22446 | LAB 20 | WeFr 1:00pm - 1:59pm | Moffitt Library 145 | 4 | 96/95/0 |

Course Times

MoTuTh 1:00pm - 1:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Jeremy Sanchez

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

C | 96 | 96 | 0 |

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

22447 | LAB 20 | TuTh 2:00pm - 2:59pm | Moffitt Library 145 | 4 | 96/96/0 |

Course Times

MoWeFr 2:00pm - 2:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Shobhana Murali Stoyanov

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

O | 96 | 95 | 0 |

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

22448 | LAB 20 | WeFr 3:00pm - 3:59pm | Moffitt Library 145 | 4 | 96/95/0 |

Course Times

MoTuTh 3:00pm - 3:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Andrew Bray

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

C | 96 | 93 | 0 |

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

22449 | LAB 20 | TuTh 4:00pm - 4:59pm | Moffitt Library 145 | 4 | 96/93/0 |

Course Times

MoWeFr 4:00pm - 4:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Andrea Massari

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

O | 96 | 90 | 0 |

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

22450 | LAB 20 | WeFr 5:00pm - 5:59pm | Moffitt Library 145 | 4 | 96/90/0 |

Course Times

MoTuTh 11:00am - 11:59am

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Sam Pimentel

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

C | 96 | 96 | 0 |

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

21931 | LAB 20 | TuTh 12:00pm - 12:59pm | Moffitt Library 145 | 4 | 96/96/0 |

Course Times

Mo 1:00pm - 1:59pm

Course Location

Anthro/Art Practice Bldg 160

Course Units

1

Course number

33A

Course description

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.

Instructor(s)

Gaston Sanchez Trujillo

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

O | 100 | 79 | 0 |

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

20424 | LAB 33 | We 10:00am - 10:59am | Evans 342 | 1 | 25/20/0 |

20425 | LAB 33 | We 9:00am - 9:59am | Evans 342 | 1 | 25/18/0 |

20426 | LAB 33 | We 1:00pm - 1:59pm | Evans 342 | 1 | 25/24/0 |

20427 | LAB 33 | We 2:00pm - 2:59pm | Evans 342 | 1 | 25/17/0 |

Course Times

We 2:00pm - 2:59pm

Course Location

Cory 277

Course Units

1

Course number

33B

Course description

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.

Instructor(s)

Gaston Sanchez Trujillo

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

O | 100 | 96 | 0 |

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

20429 | LAB 33 | Mo 10:00am - 10:59am | Evans 342 | 1 | 25/21/0 |

21089 | LAB 33 | Mo 9:00am - 9:59am | Evans 342 | 1 | 25/24/0 |

21142 | LAB 33 | Mo 12:00pm - 12:59pm | Evans 342 | 1 | 25/25/0 |

21544 | LAB 33 | Mo 1:00pm - 1:59pm | Evans 342 | 1 | 25/26/0 |

Course Times

TuTh 11:00am - 12:29pm

Course Location

Wheeler 150

Course Units

4

Course number

C100

Course description

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.

Instructor(s)

Joseph E. Gonzalez, Narges Norouzi

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

C | 0 | 0 | 0 |

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

19775 | LAB 100 | MoWeFr 11:00am - 11:59am | Physics Building 251 | 4 | 0/0/0 |

19776 | LAB 100 | Tu 1:00pm - 1:59pm | 4 | 0/0/0 | |

19777 | LAB 100 | Tu 1:00pm - 1:59pm | 4 | 0/0/0 | |

19557 | LAB 100 | Tu 4:00pm - 4:59pm | 4 | 0/0/0 | |

19558 | LAB 100 | Tu 4:00pm - 4:59pm | 4 | 0/0/0 | |

19559 | LAB 100 | We 10:00am - 10:59am | 4 | 0/0/0 | |

19560 | LAB 100 | We 10:00am - 10:59am | 4 | 0/0/0 | |

19766 | LAB 100 | We 11:00am - 11:59am | 4 | 0/0/0 | |

19767 | LAB 100 | We 11:00am - 11:59am | 4 | 0/0/0 | |

19768 | LAB 100 | 4 | 0/0/0 | ||

19769 | LAB 100 | 4 | 0/0/0 | ||

19770 | LAB 100 | 4 | 1/0/0 | ||

19771 | LAB 100 | 4 | 0/0/0 | ||

19772 | LAB 100 | 4 | 0/0/0 | ||

19773 | LAB 100 | 4 | 0/0/0 | ||

19774 | LAB 100 | 4 | 0/0/0 | ||

19778 | LAB 100 | Tu 1:00pm - 1:59pm | 4 | 0/0/0 | |

19779 | LAB 100 | Tu 1:00pm - 1:59pm | 4 | 0/0/0 | |

19780 | LAB 100 | Tu 2:00pm - 2:59pm | 4 | 0/0/0 | |

19781 | LAB 100 | Tu 2:00pm - 2:59pm | 4 | 0/0/0 | |

19782 | LAB 100 | 4 | 0/0/0 | ||

20194 | LAB 100 | Tu 2:00pm - 2:59pm | 4 | 0/0/0 | |

20196 | LAB 100 | Tu 2:00pm - 2:59pm | 4 | 0/0/0 | |

20198 | LAB 100 | Tu 3:00pm - 3:59pm | 4 | 0/0/0 | |

20200 | LAB 100 | Tu 3:00pm - 3:59pm | 4 | 0/0/0 | |

20202 | LAB 100 | Tu 3:00pm - 3:59pm | 4 | 0/0/0 | |

20204 | LAB 100 | Tu 3:00pm - 3:59pm | 4 | 0/0/0 | |

20206 | LAB 100 | Tu 4:00pm - 4:59pm | 4 | 0/0/0 | |

20284 | LAB 100 | Tu 4:00pm - 4:59pm | 4 | 0/0/0 | |

20577 | LAB 100 | We 9:00am - 9:59am | 4 | 0/0/0 | |

20579 | LAB 100 | We 10:00am - 10:59am | 4 | 0/0/0 | |

20581 | LAB 100 | We 11:00am - 11:59am | 4 | 0/0/0 | |

20583 | LAB 100 | We 9:00am - 9:59am | 4 | 0/0/0 | |

20585 | LAB 100 | Tu 5:00pm - 5:59pm | 4 | 0/0/0 | |

20587 | LAB 100 | Tu 6:00pm - 6:59pm | 4 | 0/0/0 | |

20589 | LAB 100 | Tu 7:00pm - 7:59pm | 4 | 0/0/0 | |

20591 | LAB 100 | Tu 7:00pm - 7:59pm | 4 | 0/0/0 | |

20593 | LAB 100 | Tu 7:00pm - 7:59pm | 4 | 0/0/0 | |

20595 | LAB 100 | Tu 7:00pm - 7:59pm | 4 | 0/0/0 | |

20597 | LAB 100 | We 8:00pm - 8:59pm | 4 | 0/0/0 | |

20599 | LAB 100 | We 8:00pm - 8:59pm | 4 | 0/0/0 | |

20601 | LAB 100 | We 8:00pm - 8:59pm | 4 | 0/0/0 |

Course Times

TuTh 12:30pm - 1:59pm

Course Location

Li Ka Shing 245

Course Units

4

Course number

C102

Course description

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.

Instructor(s)

Ramesh Sridharan, Alexander Strang

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

C | 0 | 0 | 0 |

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

20880 | LAB 102 | 4 | 0/0/0 |

Course Times

MoWeFr 9:00am - 9:59am

Course Location

Stanley 105

Course Units

3

Course number

133

Course description

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. This course uses R as its primary computing language; details are determined by the instructor.

Instructor(s)

Gaston Sanchez Trujillo

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

C | 194 | 193 | 0 |

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

19133 | LAB 133 | Th 9:00am - 10:59am | Evans 340 | 3 | 31/32/0 |

19134 | LAB 133 | Th 9:00am - 10:59am | Evans 342 | 3 | 31/31/0 |

19135 | LAB 133 | Th 11:00am - 12:59pm | Evans 340 | 3 | 32/32/0 |

19136 | LAB 133 | Th 11:00am - 12:59pm | Evans 342 | 3 | 32/29/0 |

19137 | LAB 133 | Th 1:00pm - 2:59pm | Evans 340 | 3 | 35/35/0 |

19138 | LAB 133 | Th 4:00pm - 5:59pm | Evans 330 | 3 | 34/34/0 |

Course Times

TuTh 6:30pm - 7:59pm

Course Location

Dwinelle 155

Course Units

4

Course number

134

Course description

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.

Instructor(s)

Vadim Gorin

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

O | 420 | 415 | 0 |

Course Times

MoWeFr 10:00am - 10:59am

Course Location

Valley Life Sciences 2040

Course Units

4

Course number

135

Course description

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.

Instructor(s)

Adam R Lucas

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

O | 141 | 125 | 0 |

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

19151 | LAB 135 | Fr 12:00pm - 1:59pm | Evans 344 | 4 | 35/35/0 |

19152 | LAB 135 | Fr 12:00pm - 1:59pm | Evans 342 | 4 | 35/26/0 |

19153 | LAB 135 | Fr 2:00pm - 3:59pm | Evans 344 | 4 | 36/34/0 |

19154 | LAB 135 | Fr 3:00pm - 4:59pm | Evans 342 | 4 | 35/30/0 |

Course Times

TuTh 2:00pm - 3:29pm

Course Location

Valley Life Sciences 2050

Course Units

4

Course number

C140

Course description

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.

Instructor(s)

Ani Adhikari

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

C | 0 | 0 | 0 |

Course Times

MoWeFr 1:00pm - 1:59pm

Course Location

Stanley 106

Course Units

3

Course number

150

Course description

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.

Instructor(s)

Benson C Au, Ella Veronika Hiesmayr

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

O | 114 | 90 | 0 |

Course Times

TuTh 9:30am - 10:59am

Course Location

Etcheverry 3108

Course Units

4

Course number

151A

Course description

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. This course uses either R or Python as its primary computing language, as determined by the instructor.

Instructor(s)

Ryan Giordano

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

O | 70 | 53 | 0 |

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

22574 | LAB 151 | We 9:00am - 10:59am | Evans 334 | 4 | 35/29/0 |

22575 | LAB 151 | We 1:00pm - 2:59pm | Evans 334 | 4 | 35/24/0 |

Course Times

MoWeFr 2:00pm - 2:59pm

Course Location

Stanley 106

Course Units

4

Course number

153

Course description

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. This course uses either R or Python as its primary computing language, as determined by the instructor.

Instructor(s)

Alice Cima

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

C | 70 | 78 | 0 |

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

19158 | LAB 153 | Fr 12:00pm - 1:59pm | Evans 334 | 4 | 35/39/0 |

19159 | LAB 153 | Fr 3:00pm - 4:59pm | Evans 334 | 4 | 35/39/0 |

Course Times

TuTh 11:00am - 12:29pm

Course Location

Social Sciences Building 20

Course Units

4

Course number

154

Course description

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. This course uses Python as its primary computing language; details are determined by the instructor.

Instructor(s)

Song Mei

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

C | 61 | 62 | 0 |

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

19161 | LAB 154 | Mo 12:00pm - 1:59pm | Evans 334 | 4 | 31/30/0 |

20168 | LAB 154 | Mo 3:00pm - 4:59pm | Evans 334 | 4 | 31/32/0 |

Course Times

TuTh 8:00am - 9:29am

Course Location

Stanley 106

Course Units

3

Course number

155

Course description

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

Instructor(s)

Adrian Gonzalez Casanova Soberon

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

O | 100 | 80 | 0 |

Course Times

TuTh 3:30pm - 4:59pm

Course Location

Evans 340

Course Units

3

Course number

157

Course description

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.

Instructor(s)

Alexander Strang

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

C | 30 | 30 | 0 |

Course Times

MoWe 4:00pm - 4:59pm

Course Location

Anthro/Art Practice Bldg 160

Course Units

3

Course number

165

Course description

Forecasting has been used to predict elections, climate change, and the spread of COVID-19. Poor forecasts led to the 2008 financial crisis. In our daily lives, good forecasting ability can help us plan our work, be on time to events, and make informed career decisions. This practically-oriented class will provide students with tools to make good forecasts, including Fermi estimates, calibration training, base rates, scope sensitivity, and power laws. This course uses Python as its primary computing language; details are determined by the instructor.

Instructor(s)

Jacob Noah Steinhardt, Matthew Dworkin, Rahul Shah

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

C | 101 | 101 | 0 |

Course Times

TuTh 11:00am - 12:29pm

Course Location

Wheeler 150

Course Units

4

Course number

C200C

Course description

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.

Instructor(s)

Joseph E. Gonzalez, Narges Norouzi

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

C | 0 | 0 | 0 |

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

26043 | LAB 200 | 4 | 0/0/0 |

Course Times

TuTh 3:30pm - 4:59pm

Course Location

Evans 344

Course Units

4

Course number

C205B

Course description

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.

Instructor(s)

Shirshendu Ganguly, Mriganka Basu Roy Chowdhury

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

O | 15 | 7 | 0 |

Course Times

TuTh 9:30am - 10:59am

Course Location

Evans 344

Course Units

3

Course number

C206B

Course description

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.

Instructor(s)

Steven N Evans, Mriganka Basu Roy Chowdhury

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

O | 15 | 7 | 0 |

Course Times

TuTh 11:00am - 12:29pm

Course Location

Anthro/Art Practice Bldg 155

Course Units

4

Course number

210B

Course description

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.

Instructor(s)

Nikita Zhivotovskiy, Drew Thanh Nguyen

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

O | 45 | 36 | 0 |

Course Times

TuTh 2:00pm - 3:29pm

Course Location

Evans 1011

Course Units

4

Course number

215B

Course description

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.

Instructor(s)

Jon Mcauliffe

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

C | 20 | 20 | 0 |

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

25939 | LAB 215 | Fr 9:00am - 10:59am | Evans 332 | 4 | 20/20/0 |

Course Times

Tu 5:00pm - 7:59pm

Course Location

Dwinelle 88

Course Units

4

Course number

222

Course description

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.

Instructor(s)

Thomas NG Bengtsson, Libor Pospisil

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

O | 48 | 43 | 0 |

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

25999 | LAB 222 | Th 4:00pm - 4:59pm | Social Sciences Building 166 | 4 | 24/23/0 |

26134 | LAB 222 | Th 5:00pm - 5:59pm | Social Sciences Building 166 | 4 | 24/20/0 |

Course Times

MoWe 5:00pm - 6:29pm

Course Location

Wurster 102

Course Units

4

Course number

230A

Course description

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.

Instructor(s)

Peng Ding

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

O | 48 | 46 | 0 |

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

26096 | LAB 230 | Th 1:00pm - 2:59pm | Evans 330 | 4 | 27/27/0 |

25942 | LAB 230 | Th 9:00am - 10:59am | Evans 330 | 4 | 21/19/0 |

Course Times

TuTh 11:00am - 12:29pm

Course Location

Social Sciences Building 20

Course Units

4

Course number

254

Course description

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.

Instructor(s)

Song Mei

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

O | 18 | 15 | 0 |

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

26443 | LAB 254 | Mo 12:00pm - 1:59pm | Evans 334 | 4 | 9/8/0 |

26444 | LAB 254 | Mo 3:00pm - 4:59pm | Evans 334 | 4 | 9/7/0 |

Course Times

Tu 11:00am - 1:59pm

Course Location

Evans 344

Course Units

3

Course number

260

Course description

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

Instructor(s)

Vadim Gorin

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

O | 25 | 10 | 0 |

Course Times

MoWe 4:00pm - 4:59pm

Course Location

Anthro/Art Practice Bldg 160

Course Units

3

Course number

265

Course description

Forecasting has been used to predict elections, climate change, and the spread of COVID-19. Poor forecasts led to the 2008 financial crisis. In our daily lives, good forecasting ability can help us plan our work, be on time to events, and make informed career decisions. This practically-oriented class will provide students with tools to make good forecasts, including Fermi estimates, calibration training, base rates, scope sensitivity, and power laws.

Instructor(s)

Jacob Noah Steinhardt, Matthew Dworkin, Rahul Shah

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

O | 8 | 7 | 0 |

Course Times

We 9:00am - 10:59am

Course Location

Evans 443

Course Units

3

Course number

272

Course description

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.

Instructor(s)

James Bentley Brown

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

O | 12 | 2 | 0 |

Course Times

We 4:00pm - 4:59pm

Course Location

Evans 1011

Course Units

4

Course number

278B

Course description

Special topics, by means of lectures and informational conferences.

Instructor(s)

Ryan Giordano

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

O | 21 | 20 | 0 |

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

25945 | LAB 200 | Internet/Online | 4 | 0/0/0 |

Course Times

We 3:00pm - 3:59pm

Course Location

Evans 334

Course Units

4

Course number

278B

Course description

Special topics, by means of lectures and informational conferences.

Instructor(s)

Benson C Au

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

O | 15 | 10 | 0 |

Course Times

Mo 9:00am - 10:59am

Course Location

Evans 334

Course number

375

Course description

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

Instructor(s)

Andrew Bray

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

O | 20 | 16 | 0 |

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

26056 | LAB 375 | Evans 334 | 21/16/0 |

Course Location

Internet/Online

Course Units

4

Course number

2

Course description

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

Instructor(s)

Eaman Jahani

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 |

Course Times

MoWeFr 10:00am - 10:59am

Course Location

Wheeler 150

Course Units

4

Course number

C8

Course description

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.

Instructor(s)

Swupnil K Sahai, Muhammad R Khan

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 |

Course Times

MoWeFr 8:00am - 8:59am

Course Location

Wheeler 212

Course Units

4

Course number

20

Course description

Instructor(s)

Jeremy Sanchez

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 |

Course Times

MoTuTh 9:00am - 9:59am

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Shobhana Murali Stoyanov

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 |

Course Times

MoTuTh 11:00am - 11:59am

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Shobhana Murali Stoyanov

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 |

Course Times

MoWeFr 12:00pm - 12:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Silas Gifford

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 |

Course Times

MoTuTh 1:00pm - 1:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Iain Carmichael

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 |

Course Times

MoWeFr 2:00pm - 2:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Andrew Bray

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 |

Course Times

MoTuTh 3:00pm - 3:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Iain Carmichael

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 |

Course Times

MoWeFr 10:00am - 10:59am

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Jeremy Sanchez

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 |

Course Times

Mo 3:00pm - 3:59pm

Course Location

Evans 60

Course Units

1

Course number

33A

Course description

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.

Instructor(s)

Gaston Sanchez Trujillo

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 |

Course Times

We 3:00pm - 3:59pm

Course Location

Evans 60

Course Units

1

Course number

33B

Course description

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.

Instructor(s)

Gaston Sanchez Trujillo

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 |

Course Times

TuTh 3:30pm - 4:59pm

Course Location

Li Ka Shing 245

Course Units

4

Course number

C102

Course description

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.

Instructor(s)

Ramesh Sridharan, Adityanand Guntuboyina

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 |

Course Times

MoWeFr 2:00pm - 2:59pm

Course Location

Evans 60

Course Units

4

Course number

C131A

Course description

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.

Instructor(s)

Jeremy Sanchez

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 |

Course Times

MoWeFr 1:00pm - 1:59pm

Course Location

Physics Building 4

Course Units

3

Course number

133

Course description

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.

Instructor(s)

Gaston Sanchez Trujillo

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 |

Course Times

MoWeFr 9:00am - 9:59am

Course Location

Dwinelle 155

Course Units

4

Course number

134

Course description

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.

Instructor(s)

Adam R Lucas

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

O | 361 | 360 | 0 |

Course Times

MoWeFr 11:00am - 11:59am

Course Location

Evans 10

Course Units

4

Course number

135

Course description

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.

Instructor(s)

Adam R Lucas

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 |

Course Times

MoWeFr 10:00am - 10:59am

Course Location

Stanley 106

Course Units

3

Course number

150

Course description

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.

Instructor(s)

Benson C Au, Adam Quinn Jaffe

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

O | 96 | 83 | 0 |

Course Times

TuTh 2:00pm - 3:29pm

Course Location

Stanley 106

Course Units

4

Course number

151A

Course description

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.

Instructor(s)

Shobhana Murali Stoyanov

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 |

Course Times

MoWeFr 1:00pm - 1:59pm

Course Location

Social Sciences Building 20

Course Units

4

Course number

153

Course description

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.

Instructor(s)

Ryan Tibshirani

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 |

Course Times

TuTh 9:30am - 10:59am

Course Location

Etcheverry 3106

Course Units

4

Course number

154

Course description

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.

Instructor(s)

Nikita Zhivotovskiy

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 |

Course Times

TuTh 3:30pm - 4:59pm

Course Location

Stanley 106

Course Units

4

Course number

156

Course description

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.

Instructor(s)

Peng Ding

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 |

Course Times

TuTh 12:30pm - 1:59pm

Course Location

Evans 332

Course Units

3

Course number

157

Course description

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.

Instructor(s)

Shirshendu Ganguly

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

O | 35 | 34 | 0 |

Course Times

TuTh 11:00am - 12:29pm

Course Location

Birge 50

Course Units

4

Course number

158

Course description

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.

Instructor(s)

Sam Pimentel

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 |

Course Times

MoWeFr 11:00am - 11:59am

Course Location

GSPP 150

Course Units

4

Course number

201B

Course description

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

Instructor(s)

Elizabeth Purdom

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 |

Course Times

TuTh 3:30pm - 4:59pm

Course Location

Davis 534

Course Units

4

Course number

201A

Course description

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

Instructor(s)

Adrian Gonzalez Casanova Soberon

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 |

Course Times

TuTh 2:00pm - 3:29pm

Course Location

Evans 332

Course Units

4

Course number

204

Course description

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.

Instructor(s)

Steven N Evans, Ella Veronika Hiesmayr

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

O | 25 | 19 | 0 |

Course Times

TuTh 2:00pm - 3:29pm

Course Location

Evans 334

Course Units

4

Course number

C205A

Course description

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.

Instructor(s)

Alan Hammond

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

O | 21 | 20 | 0 |

Course Times

TuTh 11:00am - 12:29pm

Course Location

Evans 60

Course Units

4

Course number

210A

Course description

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.

Instructor(s)

William Fithian, Tae Joo Ahn

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

O | 90 | 69 | 0 |

Course Times

TuTh 12:30pm - 1:59pm

Course Location

Evans 334

Course Units

4

Course number

215A

Course description

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).

Instructor(s)

Bin Yu

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 |

Course Times

MoWeFr 10:00am - 10:59am

Course Location

Evans 60

Course Units

4

Course number

243

Course description

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

Instructor(s)

Christopher Paciorek

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 |

Course Times

TuTh 12:30pm - 1:59pm

Course Location

Berkeley Way West 1206

Course Units

4

Course number

C245B

Course description

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.

Instructor(s)

Mark van der Laan

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 |

Course Times

TuTh 9:30am - 10:59am

Course Location

Etcheverry 3106

Course Units

4

Course number

254

Course description

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.

Instructor(s)

Nikita Zhivotovskiy

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 |

Course Times

TuTh 3:30pm - 4:59pm

Course Location

Stanley 106

Course Units

4

Course number

256

Course description

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.

Instructor(s)

Peng Ding

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 |

Course Times

We 9:00am - 10:59am

Course Location

Evans 443

Course Units

3

Course number

272

Course description

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.

Instructor(s)

James Bentley Brown

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

O | 12 | 6 | 0 |

Course Times

We 4:00pm - 4:59pm

Course Location

Evans 1011

Course number

278B

Course description

Special topics, by means of lectures and informational conferences.

Instructor(s)

Ryan Giordano

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

O | 35 | 16 | 0 |

Course Times

We 3:00pm - 3:59pm

Course Location

Evans 334

Course number

278B

Course description

Special topics, by means of lectures and informational conferences.

Instructor(s)

Benson C Au

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

O | 10 | 6 | 0 |

Course Times

We 9:00am - 10:59am

Course Location

Evans 332

Course Units

4

Course number

375

Course description

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 |

Course Times

MoWeTh 3:00pm - 3:59pm

Course Location

Internet/Online

Course Units

4

Course number

2

Course description

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 |

Course Times

MoTuWeThFr 10:00am - 10:59am

Course Location

Dwinelle 155

Course Units

4

Course number

C8

Course description

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 |

Course Times

MoTuWeTh 11:00am - 12:29pm

Course Location

Anthro/Art Practice Bldg 160

Course Units

4

Course number

20

Course description

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 |

Course Location

Internet/Online

Course Units

4

Course number

21

Course description

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 |

Course Times

MoTuWeTh 2:00pm - 3:29pm

Course Location

Valley Life Sciences 2050

Course Units

4

Course number

C100

Course description

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 |

Course Times

MoTuWeTh 9:00am - 10:29am

Course Location

North Gate 105

Course Units

4

Course number

134

Course description

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 |

Course Times

TuWeTh 1:00pm - 2:59pm

Course Location

Etcheverry 3106

Course Units

4

Course number

135

Course description

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 |

Course Times

TuWeTh 10:00am - 11:59am

Course Location

Evans 60

Course Units

3

Course number

155

Course description

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 |

Course Location

Internet/Online

Course Units

4

Course number

2

Course description

Instructor(s)

Chun Yu Hong

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 |

Course Times

MoWeFr 10:00am - 10:59am

Course Location

Wheeler 150

Course Units

4

Course number

C8

Course description

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.

Instructor(s)

Swupnil K Sahai, Joseph Edgar Gonzalez

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 | |

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 |

Course Times

MoWeFr 8:00am - 8:59am

Course Location

Wheeler 212

Course Units

4

Course number

20

Course description

Instructor(s)

Andrew Paul Bray

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 |

Course Times

MoWeFr 8:00am - 8:59am

Course Location

GSPP 150

Course Units

4

Course number

20

Course description

Instructor(s)

Jeremy Sanchez

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 |

Course Times

MoWeFr 10:00am - 10:59am

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Shobhana Murali Stoyanov

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 |

Course Times

MoWeFr 12:00pm - 12:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Jeremy Sanchez

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 |

Course Times

MoWeFr 2:00pm - 2:59pm

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Jeremy Sanchez

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 |

Course Times

MoWeFr 5:00pm - 5:59pm

Course Location

Wheeler 212

Course Units

4

Course number

20

Course description

Instructor(s)

Silas Gifford

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 |

Course Times

MoWeFr 8:00am - 8:59am

Course Location

Moffitt Library 145

Course Units

4

Course number

20

Course description

Instructor(s)

Shobhana Murali Stoyanov

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 |

Course Times

Mo 2:00pm - 2:59pm

Course Location

Valley Life Sciences 2040

Course Units

1

Course number

33A

Course description

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.

Instructor(s)

Gaston Sanchez

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 |

Course Times

TuTh 11:00am - 12:29pm

Course Location

Wheeler 150

Course Units

4

Course number

C100

Course description

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.

Instructor(s)

Lisa Yan, Narges Norouzi

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 | |

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 | |

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 |

Course Times

TuTh 12:30pm - 1:59pm

Course Location

Li Ka Shing 245

Course Units

4

Course number

C102

Course description

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.

Instructor(s)

Eaman Jahani, Ramesh Sridharan

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 |

Course Times

MoWeFr 2:00pm - 2:59pm

Course Location

Social Sciences Building 170

Course Units

4

Course number

C131A

Course description

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.

Instructor(s)

Elizabeth Purdom

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 |

Course Times

MoWeFr 9:00am - 9:59am

Course Location

Stanley 105

Course Units

3

Course number

133

Course description

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.

Instructor(s)

Gaston Sanchez

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 |

Course Times

MoWeFr 1:00pm - 1:59pm

Course Location

Dwinelle 155

Course Units

4

Course number

134

Course description

Instructor(s)

Adam R Lucas

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

O | 360 | 349 | 0 |

Course Times

MoWeFr 10:00am - 10:59am

Course Location

Evans 10

Course Units

4

Course number

135

Course description

Instructor(s)

Adam R Lucas

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 |