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STATS 32: Introduction to R for Undergraduates

This short course runs for weeks one through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for data analysis. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, data transformation and visualization, simple statistical tests, etc, and some useful packages in R. Prerequisite: undergraduate student. Priority given to non-engineering students. Laptops necessary for use in class.
Terms: Aut, Spr | Units: 1

STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

STATS 117: Theory of Probability I

Introduction to probability theory, including probability axioms, conditional probability, independence, random variables, and expectation. Joint, marginal, and conditional distributions. Discrete models (binomial, hypergeometric, Poisson) and continuous models (normal, exponential). Prerequisites: Single-variable calculus including infinite series (e.g., MATH 21) and at least one MATH course at Stanford. May not be taken for credit by students with credit in STATS 116, CS 109, MATH 151, or MS&E 120.
Terms: Spr, Sum | Units: 3

STATS 160: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5

STATS 191: Introduction to Applied Statistics

Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Prerequisite: introductory statistical methods course. Recommended: 60, 110, or 141.
Terms: Spr, Sum | Units: 3 | UG Reqs: GER:DB-Math, WAY-AQR

STATS 199: Independent Study

For undergraduates.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

STATS 205: Introduction to Nonparametric Statistics

Nonparametric regression and nonparametric density estimation, modern nonparametric techniques, nonparametric confidence interval estimates, nearest neighbor algorithms (with non-linear features), wavelet, bootstrap. Nonparametric analogs of the one- and two-sample t-tests and analysis of variance
Terms: Spr | Units: 3

STATS 207: Introduction to Time Series Analysis (STATS 307)

Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and state-space models. Seasonality, transformations, and introduction to financial time series. Prerequisite: basic course in Statistics at the level of 200.
Terms: Spr | Units: 3

STATS 218: Introduction to Stochastic Processes II

Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales.
Terms: Spr | Units: 3
Instructors: ; Li, S. (PI); Zhou, Y. (TA)

STATS 242: NeuroTech Training Seminar (NSUR 239)

This is a required course for students in the NeuroTech training program, and is also open to other graduate students interested in learning the skills necessary for neurotechnology careers in academia or industry. Over the academic year, topics will include: emerging research in neurotechnology, communication skills, team science, leadership and management, intellectual property, entrepreneurship and more.
Terms: Aut, Win, Spr | Units: 1 | Repeatable 9 times (up to 9 units total)

STATS 260C: Workshop in Biostatistics (BIODS 260C)

Applications of data science techniques to current problems in biology, medicine and healthcare. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write a two page critical summary of one of the workshops, with the choice made by the student
Terms: Spr | Units: 1-2 | Repeatable for credit

STATS 262: Intermediate Biostatistics: Regression, Prediction, Survival Analysis (EPI 262)

Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, time-dependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeated-measures ANOVA, GEE, and mixed models. Emphasis is on practical applications. Prerequisites: basic ANOVA and linear regression.
Terms: Spr | Units: 3

STATS 270: Bayesian Statistics (STATS 370)

This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will examine the construction of priors and the asymptotic properties of likelihoods and posterior distributions. The discussion will include but will not be limited to the case of finite dimensional parameter space. There will also be some discussions on the computational algorithms useful for Bayesian inference. Prerequisites: Stats 116 or equivalent probability course, plus basic programming knowledge; basic calculus, analysis and linear algebra strongly recommended; Stats 200 or equivalent statistical theory course desirable.
Terms: Spr | Units: 3
Instructors: ; Wong, W. (PI); Lu, S. (TA)

STATS 298: Industrial Research for Statisticians

Masters-level research as in 299, but with the approval and supervision of a faculty adviser, it must be conducted for an off-campus employer. Students must submit a written final report upon completion of the internship in order to receive credit. Repeatable for credit. Prerequisite: enrollment in Statistics M.S. program. IMPORTANT: F-1 international students enrolled in this CPT course cannot start working without first obtaining a CPT-endorsed I-20 from Bechtel International Center (enrolling in the CPT course alone is insufficient to meet federal immigration regulations).
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable 3 times (up to 3 units total)

STATS 299: Independent Study

For Statistics M.S. students only. Reading or research program under the supervision of a Statistics faculty member. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-5 | Repeatable for credit

STATS 300C: Theory of Statistics III

Decision theory formulation of statistical problems. Minimax, admissible procedures. Complete class theorems ("all" minimax or admissible procedures are "Bayes"), Bayes procedures, conjugate priors, hierarchical models. Bayesian non parametrics: diaichlet, tail free, polya trees, bayesian sieves. Inconsistency of bayes rules.
Terms: Spr | Units: 3

STATS 301: Statistics Teaching Practicum

Ordinarily for Statistics first year PhD students. Discussion of effective teaching, assessment, and course design. Students practice teaching in a guided environment. There will be a total of 10 course meetings spread out across autumn, winter, and spring quarters, but students enroll in spring quarter.
Terms: Spr | Units: 1 | Repeatable 3 times (up to 3 units total)
Instructors: ; Sun, D. (PI)

STATS 305C: Applied Statistics III

Methods for multivariate responses. Theory, computation, and practice for multivariate statistical tools. Topics may include multivariate Gaussian models, probabilistic graphical models, MCMC and variational Bayesian inference, dimensionality reduction, principal components, factor analysis, independent components analysis, canonical correlations, linear discriminant analysis, hierarchical clustering, bi-clustering, multidimensional scaling and variants (e.g., Isomap, spectral clustering, t-SNE), matrix completion, topic modeling, and state space models. Extensive work with data involving programming, ideally in Python and/or R. Prerequisites: Stats 305A and Stats 305B or consent of the instructor.
Terms: Spr | Units: 3

STATS 307: Introduction to Time Series Analysis (STATS 207)

Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and state-space models. Seasonality, transformations, and introduction to financial time series. Prerequisite: basic course in Statistics at the level of 200.
Terms: Spr | Units: 3

STATS 310C: Theory of Probability III (MATH 230C)

Continuous time stochastic processes: martingales, Brownian motion, stationary independent increments, Markov jump processes and Gaussian processes. Invariance principle, random walks, LIL and functional CLT. Markov and strong Markov property. Infinitely divisible laws. Some ergodic theory. Prerequisite: 310B or MATH 230B. http://statweb.stanford.edu/~adembo/stat-310c/
Terms: Spr | Units: 3
Instructors: ; Dembo, A. (PI); Tung, N. (TA)

STATS 319: Literature of Statistics

Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit

STATS 352: Topics in Computing for Data Science (BIODS 352)

A seminar-style course with lectures on a range of computational topics important for modern data-intensive science, jointly supported by the Statistics department and Stanford Data Science, and suitable for advanced undergraduate/graduate students engaged in either research on data science techniques (statistical or computational, for example) or research in scientific fields relying on advanced data science to achieve its goals. Seminars will alternate a presentation of a topic, usually by an expert on that topic, typically leading to exercises applying the techniques, with a follow up lecture to further discuss the topic and the exercises. Prerequisites: Understanding of basic modern data science and competence in related programming, e.g., in R or Python. https://stats352.stanford.edu/
Terms: Spr | Units: 1

STATS 361: Causal Inference

This course covers statistical underpinnings of causal inference, with a focus on experimental design and data-driven decision making. Topics include randomization, potential outcomes, observational studies, propensity score methods, matching, double robustness, semiparametric efficiency, treatment heterogeneity, structural models, instrumental variables, principal stratification, mediation, regression discontinuities, synthetic controls, interference, sensitivity analysis, policy learning, dynamic treatment rules, invariant prediction, graphical models, and structure learning. We will also discuss the relevance of optimization and machine learning tools to causal inference. Prerequisite: STATS 300A and STATS 300B, or equivalent graduate-level coursework on the theory of statistics.
Terms: Spr | Units: 3
Instructors: ; Wager, S. (PI); Jing, A. (TA)

STATS 370: Bayesian Statistics (STATS 270)

This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will examine the construction of priors and the asymptotic properties of likelihoods and posterior distributions. The discussion will include but will not be limited to the case of finite dimensional parameter space. There will also be some discussions on the computational algorithms useful for Bayesian inference. Prerequisites: Stats 116 or equivalent probability course, plus basic programming knowledge; basic calculus, analysis and linear algebra strongly recommended; Stats 200 or equivalent statistical theory course desirable.
Terms: Spr | Units: 3
Instructors: ; Wong, W. (PI); Lu, S. (TA)

STATS 375: Mathematical Problems in Machine Learning (MATH 276)

Mathematical tools to understand modern machine learning systems. Generalization in machine learning, the classical view: uniform convergence, Radamacher complexity. Generalization from stability. Implicit (algorithmic) regularization. Infinite-dimensional models: reproducing kernel Hilbert spaces. Random features approximations to kernel methods. Connections to neural networks, and neural tangent kernel. Nonparametric regression. Asymptotic behavior of wide neural networks. Properties of convolutionalnetworks. Prerequisites: EE364A or equivalent; Stat310A or equivalent.
Terms: Spr | Units: 3
Instructors: ; Montanari, A. (PI)

STATS 390: Consulting Workshop

Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in consulting service, analyze client data, and prepare formal written reports. Seminar provides supervised experience in short term consulting. May be repeated for credit. Prerequisites: graduate course work in applied statistics or data analysis, and consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit

STATS 398: Industrial Research for Statisticians

Doctoral research as in 399, but must be conducted for an off-campus employer. A final report acceptable to the advisor outlining work activity, problems investigated, key results, and any follow-up projects they expect to perform is required. The report is due at the end of the quarter in which the course is taken. May be repeated for credit. Prerequisite: Statistics Ph.D. candidate. IMPORTANT: F-1 international students enrolled in this CPT course cannot start working without first obtaining a CPT-endorsed I-20 from Bechtel International Center (enrolling in the CPT course alone is insufficient to meet federal immigration regulations).
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit
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