INTRO TO STATISTICAL REASONING

A friendly introduction to statistical concepts and reasoning with emphasis on developing statistical intuition rather than on mathematical rigor. Topics include design of experiments, descriptive statistics, correlation and regression, probability, chance variability, sampling, chance models, and tests of significance.

• TR 10:10 a.m.-11:25 a.m.
• MW 6:10 p.m.-7:25 p.m.
• MW 8:40 a.m.-9:55 a.m.
• Instructor: Pratyay Datta
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• Instructor: Anthony Donoghue
  CULPA: 17 Info
• Instructor: Musa Elbulok
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• 3 points

INTRODUCTION TO STATISTICS

Prerequisites: intermediate high school algebra. Designed for students in fields that emphasize quantitative methods. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, analysis of variance, confidence intervals and hypothesis testing. Quantitative reasoning and data analysis. Practical experience with statistical software. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.

• TR 6:10 p.m.-7:25 p.m.
• MW 8:40 a.m.-9:55 a.m.
• Instructor: Dobrin Marchev
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• Instructor: Alex Pijyan
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• 3 points

Intro to Statistics Recitation

This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024. 

• M 10:30 a.m.-11:45 a.m.
• M 2:30 p.m.-3:45 p.m.
• T 9 a.m.-10:15 a.m.
• T 1 p.m.-2:15 p.m.
• W 1 p.m.-2:15 p.m.
• W 4 p.m.-5:15 p.m.
• R 11 a.m.-12:15 p.m.
• R 3 p.m.-4:15 p.m.
• 0 points

CALC-BASED INTRO TO STATISTICS

Prerequisites: one semester of calculus. Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value, confidence intervals, maximum likelihood estimation. Serves as the pre-requisite for ECON W3412.

• TR 8:40 a.m.-9:55 a.m.
• MW 2:40 p.m.-3:55 p.m.
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Banu Baydil
  CULPA: 10 Info
• Instructor: Chenyang Zhong
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• Instructor: Tat Sang Fung
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• 3 points

UNDERGRADUATE SEM/STATISTICS

Prerequisites: Previous or concurrent enrollment in a course in statistics would make the talks more accessible. Prepared with undergraduates majoring in quantitative disciplines in mind, the presentations in this colloquium focus on the interface between data analysis, computation, and theory in interdisciplinary research. Meetings are open to all undergraduates, whether registered or not. Presenters are drawn from the faculty of department in Arts and Sciences, Engineering, Public Health and Medicine.

• F 10:10 a.m.-noon
• Instructor: Ronald Neath
  CULPA: 13 Info
• 1 points

Applied Statistical Computing

Corequisites: An introductory course in statistic (STAT UN1101 is recommended). This course is an introduction to R programming. After learning basic programming component, such as defining variables and vectors, and learning different data structures in R, students will, via project-based assignments, study more advanced topics, such as conditionals, modular programming, and data visualization. Students will also learn the fundamental concepts in computational complexity, and will practice writing reports based on their data analyses.

• TR 4:10 p.m.-5:25 p.m.
• Instructor: Alex Pijyan
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• 3 points

APPLIED LINEAR REG ANALYSIS

Prerequisites: An introductory course in statistics (STAT UN1101 is recommended). Students without programming experience in R might find STAT UN2102 very helpful. Develops critical thinking and data analysis skills for regression analysis in science and policy settings. Simple and multiple linear regression, non-linear and logistic models, random-effects models. Implementation in a statistical package. Emphasis on real-world examples and on planning, proposing, implementing, and reporting.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Ronald Neath
  CULPA: 13 Info
• 3 points

Applied Bayesian Analysis

This is a course in intermediate statistical inference techniques in the context of applied research
questions in data science. Assuming some prior exposure to probability and statistics, this course will
first introduce the student to the principles of Bayesian inference, then apply them in estimation and
prediction in the context of linear and generalized linear models, counting and classification, mixture and
multilevel models, including scientific computation (like MCMC methods). Students will also learn
about the main benefits of using Bayesian vs. frequentist methods, like naturally combining prior
information with the data; posterior probabilities as easier to interpret alternatives to p-values; parameter
estimation “pooling” in hierarchical model and so on.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Dobrin Marchev
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• 3 points

APPLIED STATISTICAL METHODS

Prerequisites: At least one, and preferably both, of STAT UN2103 and UN2104 are strongly recommended. Students without programming experience in R might find STAT UN2102 very helpful. This course is intended to give students practical experience with statistical methods beyond linear regression and categorical data analysis. The focus will be on understanding the uses and limitations of models, not the mathematical foundations for the methods. Topics that may be covered include random and mixed-effects models, classical non-parametric techniques, the statistical theory causality, sample survey design, multi-level models, generalized linear regression, generalized estimating equations and over-dispersion, survival analysis including the Kaplan-Meier estimator, log-rank statistics, and the Cox proportional hazards regression model. Power calculations and proposal and report writing will be discussed.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Wayne Lee
  CULPA: 7 Info
• 3 points

Topics in Modern Statistics: Stat Analys

Stat Analysis/Neural Data

Topics in Modern Statistics that provide undergraduate students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field. Courses listed are reviewed and approved by the Undergraduate Advisory Committee of the Department of Statistics. A good working knowledge of basic statistical concepts (likelihood,
Bayes' rule, Poisson processes, Markov chains, Gaussian random vectors), including especially linear-algebraic concepts related to regression and principal components analysis, is necessary. No previous experience with neural data is required.

• F 10 a.m.-noon
• Instructor: Liam M Paninski
  CULPA: 4 Info
• 3 points

INTRODUCTION TO PROBABILITY AND STATISTI

INTRODUCTION TO PROB & ST

Prerequisites: Calculus through multiple integration and infinite sums. A calculus-based tour of the fundamentals of probability theory and statistical inference. Probability models, random variables, useful distributions, conditioning, expectations, law of large numbers, central limit theorem, point and confidence interval estimation, hypothesis tests, linear regression. This course replaces SIEO 4150.

• MW 6:10 p.m.-7:25 p.m.
• Instructor: Arian Maleki
  h-index: 31 Info
• 3 points

PROBABILITY THEORY

Prerequisites: At least one semester, and preferably two, of calculus. An introductory course (STAT UN1201, preferably) is strongly recommended. A calculus-based introduction to probability theory. A quick review of multivariate calculus is provided. Topics covered include random variables, conditional probability, expectation, independence, Bayes’ rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markov’s inequality.

• MW 10:10 a.m.-11:25 a.m.
• TR 6:10 p.m.-7:25 p.m.
• Instructor: Richard Davis
  CULPA: 3 h-index: 60 Info
• Instructor: Pratyay Datta
  Info
• 3 points

PROBABILITY THEORY

Prerequisites: At least one semester, and preferably two, of calculus. An introductory course (STAT UN1201, preferably) is strongly recommended. A calculus-based introduction to probability theory. A quick review of multivariate calculus is provided. Topics covered include random variables, conditional probability, expectation, independence, Bayes’ rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markov’s inequality.

• TR 6:10 p.m.-7:25 p.m.
• Instructor: Pratyay Datta
  Info
• 3 points

STATISTICAL INFERENCE

Prerequisites: STAT GU4203. At least one semester of calculus is required; two or three semesters are strongly recommended. Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.

• MW 6:10 p.m.-7:25 p.m.
• Instructor: Michael Sobel
  CULPA: 5 Wikipedia h-index: 35 Info
• 3 points

STATISTICAL INFERENCE

Prerequisites: STAT GU4203. At least one semester of calculus is required; two or three semesters are strongly recommended. Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.

• MW 6:10 p.m.-7:25 p.m.
• Instructor: Michael Sobel
  CULPA: 5 Wikipedia h-index: 35 Info
• 3 points

LINEAR REGRESSION MODELS

Prerequisites: STAT GU4204 or the equivalent, and a course in linear algebra. Theory and practice of regression analysis. Simple and multiple regression, testing, estimation, prediction, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

• TR 1:10 p.m.-2:25 p.m.
• TR 2:40 p.m.-3:55 p.m.
• MW 7:40 p.m.-8:55 p.m.
• TR 8:40 a.m.-9:55 a.m.
• MW 8:40 a.m.-9:55 a.m.
• Instructor: Jingchen Liu
  CULPA: 2 h-index: 19 Info
• Instructor: Philip Protter
  h-index: 50 Info
• Instructor: Cristian G Pasarica
  Info
• Instructor: Yisha Yao
  Info
• Instructor: Yuqi Gu
  h-index: 6 Info
• 3 points

STAT COMP & INTRO DATA SCIENCE

Prerequisites: STAT GU4204 and GU4205 or the equivalent. Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

• F 10:10 a.m.-12:40 p.m.
• Instructor: Wayne Lee
  CULPA: 7 Info
• 3 points

ELEMENTARY STOCHASTIC PROCESS

Elementary Stochastic Processe

Prerequisites: STAT GU4203 and two, preferably three, semesters of calculus. Review of elements of probability theory. Poisson processes. Renewal theory. Walds equation. Introduction to discrete and continuous time Markov chains. Applications to queueing theory, inventory models, branching processes.

• MW 11:40 a.m.-12:55 p.m.
• Instructor: Mark Brown
  CULPA: 18 Wikipedia Info
• 3 points

TIME SERIES ANALYSIS

Prerequisites: STAT GU4205 or the equivalent. Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Rongning Wu
  CULPA: 1 Info
• 3 points

BAYESIAN STATISTICS

This course introduces the Bayesian paradigm for statistical inference.  Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models; Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software.

Prerequisites: A course in the theory of statistical inference, such as STAT GU4204 a course in statistical modeling and data analysis, such as STAT GU4205.

 

• MW 6:10 p.m.-7:25 p.m.
• Instructor: Ronald Neath
  CULPA: 13 Info
• 3 points

APPLIED DATA SCIENCE

Prerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will be covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.

• W 6:10 p.m.-8:55 p.m.
• Instructor: Bianca Dumitrascu
  Info
• 3 points

STATISTICAL METHODS IN FINANCE

Prerequisites: STAT GU4205 or the equivalent. A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

• F 10:10 a.m.-12:40 p.m.
• Instructor: Hammou El Barmi
  CULPA: 8 Info
• 3 points

STAT INF/TIME-SERIES MODELLING

Prerequisites: STAT GU4204 or the equivalent. STAT GU4205 is recommended. Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering. This is a core course in the MS program in mathematical finance.

• TR 6:10 p.m.-7:25 p.m.
• S 10:10 a.m.-12:40 p.m.
• Instructor: Alberto Gonzalez Sanz
  Info
• Instructor: Franz Rembart
  Info
• 3 points

STOCHASTC PROCSSES-APPLICTNS I

Prerequisites: STAT GU4203. STAT GU4207 is recommended. Basics of continuous-time stochastic processes. Wiener processes. Stochastic integrals. Ito's formula, stochastic calculus. Stochastic exponentials and Girsanov's theorem. Gaussian processes. Stochastic differential equations. Additional topics as time permits.

• MW 4:10 p.m.-5:25 p.m.
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Graeme Baker
  Info
• Instructor: Lars T Nielsen
  h-index: 17 Info
• 3 points

STOCHASTIC METHODS IN FINANCE

Stochastics Methods in Finance

Prerequisites: STAT GU4264. Mathematical theory and probabilistic tools for modeling and analyzing security markets are developed. Pricing options in complete and incomplete markets, equivalent martingale measures, utility maximization, term structure of interest rates. This is a core course in the MS program in mathematical finance.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Steven Campbell
  Info
• 3 points

ADVANCED DATA ANALYSIS

Prerequisites: STAT GU4205 and at least one statistics course numbered between GU4221 and GU4261. This is a course on getting the most out of data. The emphasis will be on hands-on experience, involving case studies with real data and using common statistical packages. The course covers, at a very high level, exploratory data analysis, model formulation, goodness of fit testing, and other standard and non-standard statistical procedures, including linear regression, analysis of variance, nonlinear regression, generalized linear models, survival analysis, time series analysis, and modern regression methods. Students will be expected to propose a data set of their choice for use as case study material.

• F 5:10 p.m.-7:40 p.m.
• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 3 points

PROBABILITY

Prerequisites: At least one semester of calculus. A calculus-based introduction to probability theory. Topics covered include random variables, conditional probability, expectation, independence, Bayes rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markovs inequality.

• TR 6:10 p.m.-7:25 p.m.
• MW 1:10 p.m.-3:40 p.m.
• TR 6:10 p.m.-8:40 p.m.
• Instructor: Pratyay Datta
  Info
• Instructor: Shaw-Hwa Lo
  CULPA: 14 Info
• Instructor: Jingchen Liu
  CULPA: 2 h-index: 19 Info
• 3 points

STATISTICAL INFERENCE

Prerequisites: STAT GR5203 or the equivalent, and two semesters of calculus. Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals, maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.

• MW 6:10 p.m.-7:25 p.m.
• MW 1:10 p.m.-3:40 p.m.
• MW 8:40 a.m.-11:25 a.m.
• TR 6:10 p.m.-8:40 p.m.
• Instructor: Michael Sobel
  CULPA: 5 Wikipedia h-index: 35 Info
• Instructor: Victor H de la Pena
  h-index: 20 Info
• Instructor: Regina Dolgoarshinnykh
  CULPA: 2 Info
• Instructor: Michael Sobel
  CULPA: 5 Wikipedia h-index: 35 Info
• 3 points

LINEAR REGRESSION MODELS

Prerequisites: STAT GR5203 and GR5204 or the equivalent. Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

• TR 2:40 p.m.-3:55 p.m.
• MW 7:40 p.m.-8:55 p.m.
• TR 8:40 a.m.-9:55 a.m.
• MW 8:40 a.m.-9:55 a.m.
• Instructor: Philip Protter
  h-index: 50 Info
• Instructor: Cristian G Pasarica
  Info
• Instructor: Yisha Yao
  Info
• Instructor: Yuqi Gu
  h-index: 6 Info
• 3 points

STAT COMP & INTRO DATA SCIENCE

STAT COMP & INTRO TO DATA SCI

Corequisites: STAT GR5204 and GR5205 or the equivalent. Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

• F 10:10 a.m.-12:40 p.m.
• Instructor: Wayne Lee
  CULPA: 7 Info
• 3 points

STAT COMP & INTRO DATA SCIENCE

Corequisites: STAT GR5204 and GR5205 or the equivalent. Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

• F 10:10 a.m.-12:40 p.m.
• F 6:10 p.m.-8:40 p.m.
• Instructor: Yongchan Kwon
  Info
• Instructor: Haiyuan Wang
  Info
• 3 points

ELEMENTARY STOCHASTIC PROCESSE

Corequisites: GR5203 or the equivalent. Review of elements of probability theory. Poisson processes. Renewal theory. Walds equation. Introduction to discrete and continuous time Markov chains. Applications to queueing theory, inventory models, branching processes.

• MW 11:40 a.m.-12:55 p.m.
• Instructor: Mark Brown
  CULPA: 18 Wikipedia Info
• 3 points

TIME SERIES ANALYSIS

Prerequisites: STAT GR5205 Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Rongning Wu
  CULPA: 1 Info
• 3 points

BAYESIAN STATISTICS

This course introduces the Bayesian paradigm for statistical inference.  Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models, Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software.

 

Prerequisites: A course in the theory of statistical inference, such as STAT GU4204/GR5204 a  course in statistical modeling and data analysis such as STAT GU4205/GR5205.

• MW 6:10 p.m.-7:25 p.m.
• Instructor: Ronald Neath
  CULPA: 13 Info
• 3 points

ADVANCED MACHINE LEARNING

Prerequisites: STAT GR5241 This course covers some advanced topics in machine learning and has an emphasis on applications to real world data. A major part of this course is a course project which consists of an in-class presentation and a written project report.

• M 10:10 a.m.-12:40 p.m.
• R 4:10 p.m.-6:40 p.m.
• 3 points

APPLIED DATA SCIENCE

Prerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.

• W 6:10 p.m.-8:55 p.m.
• Instructor: Bianca Dumitrascu
  Info
• 3 points

Python for Deep Learning

This course is an optional companion lab course for GR5242 Advanced Machine Learning.  The aim of this course is to help students acquire the basic computational skills in a python-based Deep Learning library (such as Troch, TensorFlow) to implement deep learning models.  lab class materials will be aligned closely with the topics covered in GR5242.  Google Colab will be used as the main tools for the hands-on lab exercises.

• R 5:35 p.m.-6:35 p.m.
• R 6:45 p.m.-7:45 p.m.
• Instructor: Ka-Yi Ng
  Info
• 1 points

STATISTICAL METHODS IN FINANCE

Prerequisites: STAT GR5204 or the equivalent. STAT GR5205 is recommended. A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

• F 10:10 a.m.-12:40 p.m.
• Instructor: Hammou El Barmi
  CULPA: 8 Info
• 3 points

STAT INF/TIME-SERIES MODELLING

Available to SSP, SMP Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering.

• TR 6:10 p.m.-7:25 p.m.
• S 10:10 a.m.-12:40 p.m.
• Instructor: Alberto Gonzalez Sanz
  Info
• Instructor: Franz Rembart
  Info
• 3 points

STOCHASTC PROCSSES-APPLICTNS I

Prerequisites: STAT GR5203 or the equivalent. Basics of continuous-time stochastic processes. Wiener processes. Stochastic integrals. Ito's formula, stochastic calculus. Stochastic exponentials and Girsanov's theorem. Gaussian processes. Stochastic differential equations. Additional topics as time permits.

• MW 4:10 p.m.-5:25 p.m.
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Graeme Baker
  Info
• Instructor: Lars T Nielsen
  h-index: 17 Info
• 3 points

STOCHASTIC METHODS IN FINANCE

Prerequisites: STAT GR5264 Available to SSP, SMP. Mathematical theory and probabilistic tools for modeling and analyzing security markets are developed. Pricing options in complete and incomplete markets, equivalent martingale measures, utility maximization, term structure of interest rates.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Steven Campbell
  Info
• 3 points

ADVANCED DATA ANALYSIS

Prerequisites: W4315 and either another statistics course numbered above the 4200 or permission of instructor. Required for the major in statistics. Data analysis using a computer statistical package and selected exploratory data analysis subroutines. Topics include editing of data for errors, exploratory and standard techniques for one-way analysis of variance, linear regression, and two-way analysis of variance. Material is presented in case-study format.

• F 5:10 p.m.-7:40 p.m.
• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 3 points

TOPICS IN MODERN STATISTICS

APP MACH LEARN COMP VIS

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• M 1:10 p.m.-3:40 p.m.
• Instructor: Xiaofu He
  h-index: 21 Info
• 3 points

TOPICS IN MODERN STATISTICS

DESIGN&ANALY/ONLINE EXPRM

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• M 6:10 p.m.-8:40 p.m.
• Instructor: Lei Kang
  Info
• 3 points

TOPICS IN MODERN STATISTICS

APPLIED SURVEY-DATA ANLYS

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• F 10 a.m.-noon
• Instructor: Liam M Paninski
  CULPA: 4 Info
• 3 points

TOPICS IN MODERN STATISTICS

Machine Learn in Finance

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• T 6:10 p.m.-8:40 p.m.
• Instructor: Jeonghoe Lee
  Info
• 3 points

Practitioners Seminar

This upcoming fall, we are going to kick off the “Practitioners Seminar” course, where successful practitioners from various industry fields (tech, finance, insurance, pharmaceutical, etc..) will have a chance to meet our students and present the projects they work on, technologies they utilize to achieve their goals, solutions they came up with etc. In addition, guest speakers will share their career development path (what kind of obstacles they faced, what pitfalls to avoid, and in general give advice on career development in their fields). We will finish up the meeting with a Q&A session with students.

• F 3:10 p.m.-4:55 p.m.
• Instructor: Gabriel Young
  CULPA: 7 Info
• 0 points

PROFESSIONAL DEVELOPMENT

The course aims to teach MA in Statistics students how to manage their careers and develop professionally. Topics include resume and cover-letter writing, negotiation, mentoring, interviewing skills and communication across global teams. Top professionals from across the globe speak to students and help improve leadership skills. 

• W 4:10 p.m.-5:25 p.m.
• Instructor: Banu Baydil
  CULPA: 10 Info
• 0 points

Probability & Stat for DS Recitation

This course is an introduction to probability and statistics for data science. Topics
include probability theory, probability distributions, simulations, parameters estima-
tion, hypothesis testing, simple regression. Python examples will be used throughout
the course for illustrations.

• M 11:40 a.m.-12:55 p.m.
• W 11:40 a.m.-12:55 p.m.
• T 6 p.m.-7:15 p.m.
• 1-0 points

PROBABILITY & STAT FOR DATA SC

This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.

• MW 1:10 p.m.-2:40 p.m.
• Instructor: Dobrin Marchev
  Info
• 3 points

EXPLORATORY DATA ANALYSIS/VISUAL

This course is covers the following topics: fundamentals of data visualization, layered grammer of graphics, perception of discrete and continuous variables, intreoduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.

• MW 4:10 p.m.-5:25 p.m.
• TR 4:10 p.m.-5:25 p.m.
• Instructor: Joyce T Robbins
  CULPA: 5 Info
• 3 points

STAT INFERENCE & MODELING

Prerequisites: (STAT GR5701) working knowledge of calculus and linear algebra (vectors and matrices), STAT GR5701 or equivalent, and familiarity with a programming language (e.g. R, Python) for statistical data analysis. In this course, we will systematically cover fundamentals of statistical inference and modeling, with special attention to models and methods that address practical data issues. The course will be focused on inference and modeling approaches such as the EM algorithm, MCMC methods and Bayesian modeling, linear regression models, generalized linear regression models, nonparametric regressions, and statistical computing. In addition, the course will provide introduction to statistical methods and modeling that addresses various practical issues such as design of experiments, analysis of time-dependent data, missing values, etc. Throughpout the course, real-data examples will be used in lecture discussion and homework problems. This course lays the statistical foundation for inference and modeling using data, preparing the MS in Data Science students, for other courses in machine learning, data mining and visualization.

• TR 6:10 p.m.-7:25 p.m.
• Instructor: Marco A Avella Medina
  Info
• 3 points

APPLIED STATISTICS I

First semester of the doctoral program sequence in applied statistics.

• TR 10:10 a.m.-11:25 a.m.
• Instructor: Andrew Gelman
  CULPA: 6 Wikipedia h-index: 112 Info
• 4 points

APPLIED STATISTICS III

Prerequisites: STAT GR6102 Modern Bayesian methods offer an amazing toolbox for solving science and engineering problems. We will go through the book Bayesian Data Analysis and do applied statistical modeling using Stan, using R (or Python or Julia if you prefer) to preprocess the data and postprocess the analysis. We will also discuss the relevant theory and get to open questions in model building, computing, evaluation, and expansion. The course is intended for students who want to do applied statistics and also those who are interested in working on statistics research problems.

• TR 10:10 a.m.-11:25 a.m.
• Instructor: John P Cunningham
  CULPA: 7 Info
• 4 points

Statistical Consulting

Prerequisites: STAT GR6102 or instructor permission. The Deparatments doctoral student consulting practicum. Students undertake pro bono consulting activities for Columbia community researchers under the tutelage of a faculty mentor.

• R noon-1:10 p.m.
• Instructor: Tian Zheng
  CULPA: 13 Wikipedia h-index: 25 Info
• 3 points

THEORETICAL STATISTICS I

Prerequisites: students in a masters program must seek the director of the M.A. program in statistics' permission; students in an undergraduate program must seek the director of undergraduate studies in statistics' permission. A general introduction to mathematical statistics and statistical decision theory. Elementary decision theory, Bayes inference, Neyman-Pearson theory, hypothesis testing, most powerful unbiased tests, confidence sets. Estimation: methods, theory, and asymptotic properties. Likelihood ratio tests, multivariate distribution. Elements of general linear hypothesis, invariance, nonparametric methods, sequential analysis.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Cynthia Rush
  CULPA: 1 h-index: 12 Info
• 4 points

THEORETICAL STATISTICS III

Prerequisites: STAT G6201 and STAT G6201 This course will mainly focus on nonparametric methods in statistics. A tentavie list of topics to be covered include nonparametric density and regression function estimation -- upper bounds on the risk of kernel estimators and matching lower bounds on the minimax risk, reproducing kernel Hilbert spaces, bootstrap and resampling methods, multiple hypothesis testing, and high dimensional stastistical analysis.

• M 10:10 a.m.-noon
• Instructor: Ming Yuan
  h-index: 45 Info
• 4 points

PROBABILITY THEORY I

Prerequisites: A thorough knowledge of elementary real analysis and some previous knowledge of probability. Overview of measure and integration theory. Probability spaces and measures, random variables and distribution functions. Independence, Borel-Cantelli lemma, zero-one laws. Expectation, uniform integrability, sums of independent random variables, stopping times, Wald's equations, elementary renewal theorems. Laws of large numbers. Characteristic functions. Central limit problem; Lindeberg-Feller theorem, infinitely divisible and stable distributions. Cramer's theorem, introduction to large deviations. Law of the iterated logarithm, Brownian motion, heat equation.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Anne Van Delft
  CULPA: 2 h-index: 5 Info
• 4 points

PROBABILITY THEORY III

• T 10:10 a.m.-noon
• 4 points

Probabilistic Models and Machine Learnin

Prob. Models & Macine Lea

Probabilistic Models and Machine Learning is a PhD-level course about how to design and use probability models. We study their mathematical properties, algorithms for computing with them, and applications to real problems. We study both the foundations and modern methods in this field. Our goals are to understand probabilistic modeling, to begin research that makes contributions to this field, and to develop good practices for building and applying probabilistic models.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Genevera Allen
  Info
• 3 points

Theoretical Statistics

STAT ANALYSIS-NEURAL DATA

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• F 10 a.m.-noon
• Instructor: Liam M Paninski
  CULPA: 4 Info
• 3 points

SEM-THEORETICAL STATISTIC

Departmental colloquium in statistics.

• M 4:10 p.m.-5:25 p.m.
• 3 points

SEMINAR PROBABILITY THEORY

Departmental colloquium in probability theory.

• F 11:40 a.m.-12:55 p.m.
• Instructor: Ivan Corwin
  CULPA: 1 Wikipedia h-index: 34 Info
• 3 points

SEMINAR IN APPLIED PROB & RISK

A colloquiim in applied probability and risk.

• R 1:10 p.m.-2:25 p.m.
• 1 points

SEM IN MATHEMATICAL FINANCE

Seminar in Mathematical Financ

A colloquium on topics in mathematical finance

• R 4:10 p.m.-5:25 p.m.
• 3 points