Statistics

This is only a request to create zero credit recitation for STAT1001UN Intro to Stat Reasoning starting in Fall 2026.

This is only a request to create zero credit recitation for STAT1001UN Intro to Stat Reasoning starting in Fall 2026.

This is only a request to create zero credit recitation for STAT1001UN Intro to Stat Reasoning starting in Fall 2026.

This is a seminar in probability and statistics taught by faculty in the Department of
Statistics and with guests from other Departments.
The format is that of a weekly Department seminar, but the presentations are geared for
undergraduates.
Meetings are once per week and open to all undergraduates, but undergraduates who
register and attend regularly earn one point of credit and a notation on their transcript.

This is only a recitation for STAT UN1010. We are requesting 2 sections of recitation to align with the one section of 1010 offered for Fall 2026. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

This is only recitation for STAT UG1201. We are requesting 8 sections of recitation to align with the THREE sections of 1201 offered for Fall 2026.

This is only recitation for STAT UG1201. We are requesting 8 sections of recitation to align with the THREE sections of 1201 offered for Fall 2026.

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.

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.

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.

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a recitation request for STAT1202UN. We would like to have this recitation align with STAT1201UN offered for Fall 2026.   

This is only a zero credit recitation request to create STAT 2002UN. We would like to have this recitation align with STAT1202UN offered for Fall 2026.   

This is only a zero credit recitation request to create STAT 2002UN. We would like to have this recitation align with STAT1202UN offered for Fall 2026.   

This is only a recitation request for STAT3008UN. We would like to have this recitation to align with STAT3108UN offered for Fall 2026.   

This is only a recitation request for STAT3008UN. We would like to have this recitation to align with STAT3108UN offered for Fall 2026.   

Prerequisites: STAT UN2103. Students without programming experience in R might find STAT UN2102 very helpful. This course is a machine learning class from an application perspective. We will cover topics including data-based prediction, classification, specific classification methods (such as logistic regression and random forests), and basics of neural networks. Programming in homeworks will require R.

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.

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.

This course provides a non-mathematical introduction to the principles and architectures of deep learning and generative AI models. Designed for undergraduates in the Applied Data Science minor, the curriculum covers the mathematical foundations of neural networks and their application to spatial, temporal, and multimodal data. Students will examine the mechanics of convolutional and recurrent architectures, the self-attention mechanism in Transformers, and the training objectives of Large Language Models (LLMs). The course also addresses optimization strategies, reinforcement learning for model alignment, and generative paradigms, including diffusion and autoregressive models. Emphasis is placed on understanding model internal representations, architectural tradeoffs, and the evaluation of complex AI systems.

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.

adding zero credit recitation to STAT 4203UN PROBABILITY THEORY for Fall 2026

adding zero credit recitation to STAT 4203UN PROBABILITY THEORY for Fall 2026

adding zero credit recitation to STAT 4203UN PROBABILITY THEORY for Fall 2026

adding zero credit recitation to STAT 4203UN PROBABILITY THEORY for Fall 2026

adding zero credit recitation to STAT 4204GU STATISTICAL INFERENCE for Fall 2026

adding zero credit recitation to STAT 4204GU STATISTICAL INFERENCE for Fall 2026

adding zero credit recitation to STAT 4205GU for Fall 2026

adding zero credit recitation to STAT 4205GU for Fall 2026

zero credit recitation for STAT GU4206

zero credit recitation for STAT GU4206

zero credit recitation for STAT GU4206

zero credit recitation for STAT GU4206

This is only a request to create zero credit recitation for STAT4265GU/5265GR Stochastic Methods in Finance starting in Fall 2026.

Course Overview: This course introduces Python programming, covering data structures, control-flow, objects, and functions, along with libraries like re, requests, numpy, pandas, scikit-learn, scipy, and more. These skills are applied to real-world data science tasks, including AB testing, data manipulation, modeling, optimization, simulations, and data visualization.

Students will develop computational thinking abilities, including problem decomposition, pattern recognition, data representation, abstraction, and algorithm design, through practical exercises.

This course covers various topics in advanced machine learning. Topics may include optimization algorithms, Python libraries for ML, principles for applied supervised and unsupervised learning, hyperparameter selection, computational trade-offs, modern neural network architectures such as ConvNets, LSTMs, and transformers for computer vision and natural language processing, and deep learning for LLMs.

This course covers various topics in advanced machine learning. Topics may include optimization algorithms, Python libraries for ML, principles for applied supervised and unsupervised learning, hyperparameter selection, computational trade-offs, modern neural network architectures such as ConvNets, LSTMs, and transformers for computer vision and natural language processing, and deep learning for LLMs.

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

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

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

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.

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.

Recitation for STATGR5205 zero credits, not mandatory

Recitation for STAT 4205/5205

Recitation for STAT 4205/5205

Recitation for STAT 4206

Recitation for STAT 4206

Recitation for STAT 4206

Recitation for STAT 4206

adding zero credit recitation to STATGR5065

Course Overview: This course introduces Python programming, covering data structures, control-flow, objects, and functions, along with libraries like re, requests, numpy, pandas, scikit-learn, scipy, and more. These skills are applied to real-world data science tasks, including AB testing, data manipulation, modeling, optimization, simulations, and data visualization.

Students will develop computational thinking abilities, including problem decomposition, pattern recognition, data representation, abstraction, and algorithm design, through practical exercises.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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. 

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.

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.

Each year, approximately 10–15% of MA in Statistics students participate in on-campus academic research, contributing to advances in statistical methodology and applied areas. Some projects demonstrate exceptional promise and benefit from additional time and support for further development. The MA Research Specialization in Statistics allows qualified students to extend their MA program to a fourth semester to continue their research under the supervision of a faculty mentor. This competitive, merit-based program requires demonstrated research progress, a nomination from a faculty mentor, and an outstanding academic record. STAT GR5999 serves as the course through which students admitted to the MA Research Specialization fulfill their research requirements.

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.

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.

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

.

Departmental colloquium in probability theory.

A colloquiim in applied probability and risk.

A colloquium on topics in mathematical finance