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: Ashley Datta
  Info
• Instructor: Musa Elbulok
  Info
• Instructor: Anthony Donoghue
  CULPA: 17 Info
• 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: Alex Pijyan
  Info
• 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 12:10 p.m.-1:25 p.m.
• M 6:10 p.m.-7:25 p.m.
• T 12:10 p.m.-1:25 p.m.
• T 4:10 p.m.-5:25 p.m.
• Instructor: Alex Pijyan
  Info
• 0 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.

• T 4:10 p.m.-5:25 p.m.
• W 8:40 a.m.-9:55 a.m.
• W 4:10 p.m.-5:25 p.m.
• R 6:10 p.m.-7:25 p.m.
• Instructor: Alex Pijyan
  Info
• 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 6:10 p.m.-7:25 p.m.
• Instructor: Chenyang Zhong
  Info
• Instructor: Banu Baydil
  CULPA: 10 Info
• 3 points

CALC-BASED INTRO TO STATISTICS

CALC BASED INTRO TO STAT

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.

• MW 8:40 a.m.-9:55 a.m.
• Instructor: Banu Baydil
  CULPA: 10 Info
• 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 1:10 p.m.-2:25 p.m.
• Instructor: Daniel Rabinowitz
  CULPA: 19 h-index: 34 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
  Info
• 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: Casey Bradshaw
  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
  Info
• 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: Benjamin K Goodrich
  Info
• 3 points

Undergraduate Mentored Research

Prerequisites: the project mentors permission. This course provides a mechanism for students who undertake research with a faculty member from the Department of Statistics to receive academic credit. Students seeking research opportunities should be proactive and entrepreneurial: identify congenial faculty whose research is appealing, let them know of your interest and your background and skills.

• Instructor: Anne Van Delft
  CULPA: 2 h-index: 5 Info
• 3 points

Undergraduate Mentored Research

Prerequisites: the project mentors permission. This course provides a mechanism for students who undertake research with a faculty member from the Department of Statistics to receive academic credit. Students seeking research opportunities should be proactive and entrepreneurial: identify congenial faculty whose research is appealing, let them know of your interest and your background and skills.

• Instructor: Bodhisattva Sen
  h-index: 24 Info
• 3 points

Topics in Modern Statistics

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.

• TR 10:10 a.m.-11:25 a.m.
• TR 6:10 p.m.-7:25 p.m.
• Instructor: Marcel F Nutz
  CULPA: 9 Info
• Instructor: Cristian G Pasarica
  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: Cristian G Pasarica
  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: Ashley 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.
• 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.

• MW 1:10 p.m.-2:25 p.m.
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Dobrin Marchev
  Info
• Instructor: Gabriel Young
  CULPA: 7 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.
• R 6:10 p.m.-8:40 p.m.
• Instructor: Benjamin K Goodrich
  Info
• Instructor: Haiyuan Wang
  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: Richard Davis
  CULPA: 3 h-index: 60 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: Casey Bradshaw
  Info
• 3 points

Causal Inference

This course is an introduction to Causal Inference at the masters and advanced undergraduate
level. Students will be introduced to a broad range of causal inference methods including randomized
experiments, observational studies, instrumental variables, di?erence-in-di?erences,
regression discontinuity design, and synthetic controls. In addition, the course will cover modern,
controversial debates regarding the foundations and limitations of causal inference.

The primary learning goal of this course will be to familiarize students with a variety of the
most popular causal inference methods: which causal e?ects they seek to estimate, basic assumptions
required for identi?cation and estimation, and their practical implementation. To
this end, the course will focus both on developing the pre-requisite statistical / methodological
theory and as well as gaining hands-on experience through implementation exercises with
real datasets. By the end of the course, students should have deep familiarity of various causal
inference methods and—more importantly—be able to determine which method is most appropriate
for a given applied problem and to judge whether the pre-requisite identifying conditions
are appropriate.

• T 8:40 a.m.-11:25 a.m.
• Instructor: Christopher Harshaw
  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.

• T 4:10 p.m.-6:40 p.m.
• Instructor: Bianca Dumitrascu
  Info
• 3 points

Unsupervised Learning

Description.
Unsupervised Learning is a masters level course on foundations, methods, practice, and applications in machine learning from data without associated labels or outcomes.  This course will focus on dimension reduction and clustering techniques while also covering graphical models, missing data imputation, anomaly detection, generative models, and others. The course will also emphasize conceptual understanding and practical applications of unsupervised learning in data visualization, exploratory data analysis, data pre-processing, and data-driven discovery.

• W 8:40 a.m.-11:25 a.m.
• Instructor: Genevera Allen
  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 1:10 p.m.-2:25 p.m.
• MW 4:10 p.m.-5:25 p.m.
• Instructor: Graeme Baker
  Info
• Instructor: Jaehyuk Choi
  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.
• MW 8:40 a.m.-11:25 a.m.
• Instructor: Cristian G Pasarica
  Info
• Instructor: Jingchen Liu
  CULPA: 2 h-index: 19 Info
• Instructor: Regina Dolgoarshinnykh
  CULPA: 2 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.
• Instructor: Ashley Datta
  Info
• Instructor: Victor H de la Pena
  h-index: 20 Info
• Instructor: Regina Dolgoarshinnykh
  CULPA: 2 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.

• MW 6:10 p.m.-7:25 p.m.
• Instructor: Gabriel Young
  CULPA: 7 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: Benjamin K Goodrich
  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.

• R 6:10 p.m.-8:40 p.m.
• 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: Richard Davis
  CULPA: 3 h-index: 60 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: Casey Bradshaw
  Info
• 3 points

Causal Inference

This course is an introduction to Causal Inference at the masters level. Students will be introduced to a broad range of causal inference methods including randomized
experiments, observational studies, instrumental variables, di?erence-in-di?erences, regression discontinuity design, and synthetic controls. In addition, the course will cover modern, controversial debates regarding the foundations and limitations of causal inference.

The primary learning goal of this course will be to familiarize students with a variety of the most popular causal inference methods: which causal e?ects they seek to estimate, basic assumptions required for identi?cation and estimation, and their practical implementation. To this end, the course will focus both on developing the pre-requisite statistical / methodological theory and as well as gaining hands-on experience through implementation exercises with real datasets. By the end of the course, students should have deep familiarity of various causal inference methods and—more importantly—be able to determine which method is most appropriate
for a given applied problem and to judge whether the pre-requisite identifying conditions are appropriate.

• T 8:40 a.m.-11:25 a.m.
• Instructor: Christopher Harshaw
  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.

• W 10:10 a.m.-12:40 p.m.
• R 4:10 p.m.-6 p.m.
• Instructor: Kamiar Rahnama Rad
  Info
• Instructor: Parijat Dube
  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 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.

• T 4:10 p.m.-6:40 p.m.
• Instructor: Bianca Dumitrascu
  Info
• 3 points

Unsupervised Learning

Description.
Unsupervised Learning is a masters level course on foundations, methods, practice, and applications in machine learning from data without associated labels or outcomes.  This course will focus on dimension reduction and clustering techniques while also covering graphical models, missing data imputation, anomaly detection, generative models, and others. The course will also emphasize conceptual understanding and practical applications of unsupervised learning in data visualization, exploratory data analysis, data pre-processing, and data-driven discovery.   

 

Prerequisites.

STAT GR 5206 Statistical Computing and Intro to Data Science

STAT GR 5241 Statistical Machine Learning (strongly recommended)

STAT GR 5205 Linear Regression (recommended)

STAT GR 5203 Probability (recommended)

Students should also be familiar with linear algebra.

• W 8:40 a.m.-11:25 a.m.
• Instructor: Genevera Allen
  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 1:10 p.m.-2:25 p.m.
• MW 4:10 p.m.-5:25 p.m.
• Instructor: Graeme Baker
  Info
• Instructor: Jaehyuk Choi
  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

Course Description

STAT GR5291 Advanced Data Analysis serves as one of the required capstone experiences for MA students in statistics. This course is project-based and covers advanced topics in traditional data analysis. Students are presented with a mix of theory and application in homework assignments.  The final project  is a major contribution to the final grade and is arguably considered the capstone project for the MA in Statistics Program.

Students will learn a myriad of topics related to data analysis and hypothesis testing, and are responsible for application through statistical packages or manual programming. Topics include, exploratory data analysis & descriptive statistics, review of sampling distribution, point estimation, review of hypothesis testing & confidence interval procedures, non-parametric tests, computational methods (Monte Carlo, bootstrap, permutation tests), categorical data analysis, linear regression, diagnostics & residual analysis, robust regression, model selection, non-linear regression & smoothers, aspects of experimental design (ANOVA, two-way ANOVA, blocking, multiple comparisons, ANCOVA, semi-parametric procedures, random effects models, mixed effects models, nested models, repeated measures), and general linear models (logistic regression, penalized logistic, multinomial regression, link functions).

Also, time permitting the class covers:
survival analysis (hazard function, survival curve), time series analysis (stationarity, ACF/PACF, MA, AR, ARMA, ARIMA, order selection, forecasting).

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

ADVANCED DATA ANALYSIS

Course Description

STAT GR5291 Advanced Data Analysis serves as one of the required capstone experiences for MA students in statistics. This course is project-based and covers advanced topics in traditional data analysis. Students are presented with a mix of theory and application in homework assignments.  The final project  is a major contribution to the final grade and is arguably considered the capstone project for the MA in Statistics Program.

Students will learn a myriad of topics related to data analysis and hypothesis testing, and are responsible for application through statistical packages or manual programming. Topics include, exploratory data analysis & descriptive statistics, review of sampling distribution, point estimation, review of hypothesis testing & confidence interval procedures, non-parametric tests, computational methods (Monte Carlo, bootstrap, permutation tests), categorical data analysis, linear regression, diagnostics & residual analysis, robust regression, model selection, non-linear regression & smoothers, aspects of experimental design (ANOVA, two-way ANOVA, blocking, multiple comparisons, ANCOVA, semi-parametric procedures, random effects models, mixed effects models, nested models, repeated measures), and general linear models (logistic regression, penalized logistic, multinomial regression, link functions).

Also, time permitting the class covers:
survival analysis (hazard function, survival curve), time series analysis (stationarity, ACF/PACF, MA, AR, ARMA, ARIMA, order selection, forecasting).

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

ADVANCED DATA ANALYSIS

Course Description

STAT GR5291 Advanced Data Analysis serves as one of the required capstone experiences for MA students in statistics. This course is project-based and covers advanced topics in traditional data analysis. Students are presented with a mix of theory and application in homework assignments.  The final project  is a major contribution to the final grade and is arguably considered the capstone project for the MA in Statistics Program.

Students will learn a myriad of topics related to data analysis and hypothesis testing, and are responsible for application through statistical packages or manual programming. Topics include, exploratory data analysis & descriptive statistics, review of sampling distribution, point estimation, review of hypothesis testing & confidence interval procedures, non-parametric tests, computational methods (Monte Carlo, bootstrap, permutation tests), categorical data analysis, linear regression, diagnostics & residual analysis, robust regression, model selection, non-linear regression & smoothers, aspects of experimental design (ANOVA, two-way ANOVA, blocking, multiple comparisons, ANCOVA, semi-parametric procedures, random effects models, mixed effects models, nested models, repeated measures), and general linear models (logistic regression, penalized logistic, multinomial regression, link functions).

Also, time permitting the class covers:
survival analysis (hazard function, survival curve), time series analysis (stationarity, ACF/PACF, MA, AR, ARMA, ARIMA, order selection, forecasting).

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

ADVANCED DATA ANALYSIS

Course Description

STAT GR5291 Advanced Data Analysis serves as one of the required capstone experiences for MA students in statistics. This course is project-based and covers advanced topics in traditional data analysis. Students are presented with a mix of theory and application in homework assignments.  The final project  is a major contribution to the final grade and is arguably considered the capstone project for the MA in Statistics Program.

Students will learn a myriad of topics related to data analysis and hypothesis testing, and are responsible for application through statistical packages or manual programming. Topics include, exploratory data analysis & descriptive statistics, review of sampling distribution, point estimation, review of hypothesis testing & confidence interval procedures, non-parametric tests, computational methods (Monte Carlo, bootstrap, permutation tests), categorical data analysis, linear regression, diagnostics & residual analysis, robust regression, model selection, non-linear regression & smoothers, aspects of experimental design (ANOVA, two-way ANOVA, blocking, multiple comparisons, ANCOVA, semi-parametric procedures, random effects models, mixed effects models, nested models, repeated measures), and general linear models (logistic regression, penalized logistic, multinomial regression, link functions).

Also, time permitting the class covers:
survival analysis (hazard function, survival curve), time series analysis (stationarity, ACF/PACF, MA, AR, ARMA, ARIMA, order selection, forecasting).

• 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

Today's data scientist needs to have familiarity with data processing and management tools for processing large volumes of data, so-called Big Data. The class introduces the main principles of database management systems (DBMS) and will provide in depth knowledge in accessing data with SQL.  This class will study techniques and systems for ingesting, efficiently processing, and statistically analyzing large data sets. We will touch on modern technologies developed specifically for Big Data, but also focus on the ways cloud service help to store, access, analyze and model data. Upon completion of the course, you should be able to:

 


Utilize database management systems to parse and explore Big Data

Distribute data processing and statistical summary calculations efficiently

Carry out statistical analysis at different scales using local and distributed computing systems

Use Software Engineering principles to create and share standardized Machine Learning (ML) solutions

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

TOPICS IN MODERN STATISTICS

STAT ANALYSIS-NEURAL DATA

Today's data scientist needs to have familiarity with data processing and management tools for processing large volumes of data, so-called Big Data. The class introduces the main principles of database management systems (DBMS) and will provide in depth knowledge in accessing data with SQL.  This class will study techniques and systems for ingesting, efficiently processing, and statistically analyzing large data sets. We will touch on modern technologies developed specifically for Big Data, but also focus on the ways cloud service help to store, access, analyze and model data. Upon completion of the course, you should be able to:

 


Utilize database management systems to parse and explore Big Data

Distribute data processing and statistical summary calculations efficiently

Carry out statistical analysis at different scales using local and distributed computing systems

Use Software Engineering principles to create and share standardized Machine Learning (ML) solutions

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

TOPICS IN MODERN STATISTICS

Decode AI w/Info Theory

Today's data scientist needs to have familiarity with data processing and management tools for processing large volumes of data, so-called Big Data. The class introduces the main principles of database management systems (DBMS) and will provide in depth knowledge in accessing data with SQL.  This class will study techniques and systems for ingesting, efficiently processing, and statistically analyzing large data sets. We will touch on modern technologies developed specifically for Big Data, but also focus on the ways cloud service help to store, access, analyze and model data. Upon completion of the course, you should be able to:

 


Utilize database management systems to parse and explore Big Data

Distribute data processing and statistical summary calculations efficiently

Carry out statistical analysis at different scales using local and distributed computing systems

Use Software Engineering principles to create and share standardized Machine Learning (ML) solutions

• M 6:10 p.m.-8:40 p.m.
• Instructor: Ori Shental
  Info
• 3 points

TOPICS IN MODERN STATISTICS

ML Engineering for Stats

Today's data scientist needs to have familiarity with data processing and management tools for processing large volumes of data, so-called Big Data. The class introduces the main principles of database management systems (DBMS) and will provide in depth knowledge in accessing data with SQL.  This class will study techniques and systems for ingesting, efficiently processing, and statistically analyzing large data sets. We will touch on modern technologies developed specifically for Big Data, but also focus on the ways cloud service help to store, access, analyze and model data. Upon completion of the course, you should be able to:

 


Utilize database management systems to parse and explore Big Data

Distribute data processing and statistical summary calculations efficiently

Carry out statistical analysis at different scales using local and distributed computing systems

Use Software Engineering principles to create and share standardized Machine Learning (ML) solutions

• M 6:10 p.m.-8:40 p.m.
• Instructor: Iordan I Slavov
  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.

• R 1:10 p.m.-2:25 p.m.
• None None-None
• W 4:10 p.m.-5:25 p.m.
• Instructor: Sining Chen
  Info
• 0 points

MA Mentored Research

MA MENTORED RESEARCH

This course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.

• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 0-3 points

Statistical Fieldwork

Prerequisites: GR5203; GR5204 &GR5205 and at least 4 approved electives This course is an elective course for students in the M.A. in Statistics program that counts towards the degree requirements. To receive a grade and academic credits for this course, students are expected to engage in approved off-campus internships that can be counted as an elective. Statistical Fieldwork should provide students an opportunity to apply their statistical skills and gain practical knowledge on how statistics can be applied to solve real-world challenges.

• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 1 points

Honors Probability Theory

A rigorous introduction to probability theory. Topics covered include probability spaces and measures, Borel-Cantelli lemma, zero-one laws, conditional probability, Bayes rule, independence, random variables and distribution functions, random vectors and multivariate distributions, expectation, important distributions, characteristic functions, conditional distributions, transformations of random variables, probability and expectation inequalities, laws of large numbers, central limit theorem.

• TR 3:10 p.m.-5:55 p.m.
• Instructor: Michael Sobel
  CULPA: 5 Wikipedia h-index: 35 Info
• 3 points

Honors Statistical Inference

A rigorous introduction to the theory of statistics. Topics covered include elementary decision theory, distribution of the sample mean, point estimation methods and asymptotic properties, the bias variance tradeoff, exponential families, sufficiency and minimal sufficiency, completeness, Lehmann Scheffe, UMVUE and BLUE, Bayes inference, Neyman-Pearson theory, hypothesis testing, most powerful unbiased tests, likelihood ratio tests, confidence sets.

• TR 3:10 p.m.-5:55 p.m.
• Instructor: Bodhisattva Sen
  h-index: 24 Info
• 3 points

Honors Linear Regression Models

Hon Linear Regression Mod

This high-level course in linear regression delves deeply into the theoretical and geometric aspects of regression analysis, offering a comprehensive exploration of its foundational principles and advanced topics. Students will study regression within vector space contexts, emphasizing the role of inner products and orthogonal projections. The analysis of projection matrices will include their properties, such as idempotence and symmetry, and their implications for regression diagnostics and metrics. Students will explore why various test statistics follow t- and F-distributions, with careful attention to degrees of freedom and their derivations. As the course progresses, it will address the complexities of high dimensional regression scenarios.

• TR 8:40 a.m.-9:55 a.m.
• Instructor: Yisha Yao
  Info
• 3 points

Statistics - Graduate Recitation

Statistics - Grad Recitat

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 10:10 a.m.-11:25 a.m.
• T 7:10 p.m.-8:25 p.m.
• W 4:10 p.m.-5:25 p.m.
• Instructor: Dobrin Marchev
  Info
• 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 6:10 p.m.-7:25 p.m.
• TR 4:10 p.m.-5:25 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: Hammou El Barmi
  CULPA: 8 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.

• W 10:10 a.m.-noon
• Instructor: Yuqi Gu
  h-index: 6 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.

• M 8 a.m.-10 a.m.
• Instructor: Ashley Datta
  Info
• 3 points