STAT 301-1 Data Science 1 with R (1 Unit)  

First course in Data Science with a focus on data management, manipulation, and visualization skills and techniques for exploratory data analysis. The course also introduces the R programming language. Students may not receive credit for both this course and STAT 303-1.

Prerequisite: STAT 201-0 or COMP_SCI 110-0 and STAT 202-0 or STAT 210-0 or STAT 232-0 or PSYCH 201-0 or IEMS 201-0 or IEMS 303-0 or equivalent.

STAT 301-2 Data Science 2 with R (1 Unit)  

Introduction to supervised machine/statistical learning with a focus on application using R. Course covers essential concepts in machine learning while surveying standard machine learning models such as linear and logistic regression. Course provides a foundation for learning more machine learning methods. Students may not receive credit for both this course and STAT 303-2.

Prerequisite: STAT 301-1.

STAT 301-3 Data Science 3 with R (1 Unit)  

An intermediate course that covers machine learning methods in R, including supervised and unsupervised learning. It provides the knowledge and skills necessary to tackle real world problems with machine learning. Students may not receive credit for both this course and STAT 303-3.

Prerequisite: STAT 301-2.

STAT 302-0 Data Visualization (1 Unit)  

Introduction to the knowledge, skills, and tools required to visualize data of various formats across statistical domains and to create quality visualizations for both data exploration and presentation.

Prerequisite: STAT 202-0 or equivalent.

STAT 303-1 Data Science 1 with Python (1 Unit)  

First course in Data Science, with focus on data management, manipulation, and visualization skills and techniques for exploratory data analysis. The course also introduces the Python programming language in the context of Data Science. Students may not receive credit for both this course and STAT 301-1.

Prerequisite: STAT 201-0 or COMP_SCI 110-0 and STAT 202-0 or STAT 210-0 or STAT 232-0 or PSYCH 201-0 or IEMS 201-0 or IEMS 303-0 or equivalent.

STAT 303-2 Data Science 2 with Python (1 Unit)  

Introduction to supervised machine/statistical learning with a focus on application using Python. Course covers essential concepts in machine learning while surveying standard machine learning models such as linear and logistic regression. Course provides a foundation for learning more machine learning methods. Students may not receive credit for both this course and STAT 301-2.

Prerequisite: STAT 303-1.

STAT 303-3 Data Science 3 with Python (1 Unit)  

An intermediate course that covers machine learning methods in Python, including supervised and unsupervised learning. It provides the knowledge and skills necessary to tackle real world problems with machine learning. Students may not receive credit for both this course and STAT 301-3.

Prerequisite: STAT 303-2.

STAT 304-0 Data Structures and Algorithms for Data Science (1 Unit)  

This course will introduce students to the design, implementation, analysis, and proper application of abstract data types, data structures, and their algorithms. Python will be used to implement and explore various algorithms and data structures. Students should be prepared for a significant amount of hands-on programming.

Prerequisite: STAT 201-0 or COMP_SCI 110-0 and STAT 202-0 or STAT 210-0 or STAT 232-0 or PSYCH 201-0 or IEMS 201-0 or IEMS 303-0 or equivalent.

STAT 305-0 Information Management for Data Science (1 Unit)  

This course aims to give students an extensive data processing and visualization skillset using various Python libraries. It will also focus on relational databases and queries in SQL. Students will learn data scraping from online sources and mobile applications as well as a brief introduction to statistical and predictive analysis after the data is clean and ready to use.

Prerequisite: STAT 201-0 or COMP_SCI 110-0 and STAT 202-0 or STAT 210-0 or STAT 232-0 or PSYCH 201-0 or IEMS 201-0 or IEMS 303-0 or equivalent.

STAT 320-1 Statistical Theory & Methods 1 (1 Unit)  

Sample spaces, computing probabilities, random variables, distribution functions, expected values, variance, correlation, limit theory. May not receive credit for both STAT 320-1 and any of STAT 383-0, MATH 310-1, MATH 311-1, MATH 314-0, MATH 385-0, ELEC_ENG 302-0, or IEMS 302-0. Co-requisites: STAT 202-0 or STAT 210-0 or STAT 232-0 or PSYCH 201-0 or IEMS 201-0 or IEMS 303-0 or equivalent, STAT 228-0 or MATH 235-0 or both MATH 226-0 and MATH 230-2 or MATH 228-2 or MATH 281-2 or MATH 285-3 or MATH 290-3 or MATH 291-3 or ES_APPM 252-2.

STAT 320-2 Statistical Theory & Methods 2 (1 Unit)  

Parameter estimation, confidence intervals, hypothesis tests.

Prerequisite: STAT 320-1 or MATH 310-1 or MATH 310-1 or MATH 311-1 or MATH 314-0 or MATH 385-0 or ELEC_ENG 302-0 or IEMS 302-0 or STAT 383-0.

STAT 320-3 Statistical Theory & Methods 3 (1 Unit)  

Comparison of parameters, goodness-of-fit tests, regression analysis, analysis of variance, and nonparametric methods.

Prerequisites: STAT 320-2 and MATH 240-0 or MATH 285-1 or MATH 281-3 or MATH 290-1or MATH 291-1 or GEN_ENG 205-1 or GEN_ENG 206-1.

STAT 325-0 Survey Sampling (1 Unit)  

Probability sampling, simple random sampling, error estimation, sample size, stratification, systematic sampling, replication methods, ratio and regression estimation, cluster sampling.

Prerequisites: MATH 230-1 and 2 quarters of statistics, or consent of instructor.

STAT 328-0 Causal Inference (1 Unit)  

Introduction to modern statistical thinking about causal inference. Topics include completely randomized experiments, confounding, ignorability of assignment mechanisms, matching, observational studies, noncompliance, and Bayesian methods.

Prerequisites: STAT 320-2, STAT 350-0.

STAT 330-1 Applied Statistics for Research 1 (1 Unit)  

First Quarter: Design of experiments and surveys, numerical summaries of data, graphical summaries of data, correlation and regression, probability, sample mean, sample proportion, confidence intervals and tests of significance, one and two sample problems, ANOVA. Second Quarter: Simple linear regression, inference, diagnostics, multiple regression diagnostics, autocorrelation, 1-way ANOVA, power and sample size determination, 2-way ANOVA, ANCOVA, randomized block designs.

STAT 344-0 Statistical Computing (1 Unit)  

Exploration of theory and practice of computational statistics with emphasis on statistical programming in R.

Prerequisite: STAT 320-2 or equivalent. Some R programming experience is desired.

STAT 348-0 Applied Multivariate Analysis (1 Unit)  

Statistical methods for describing and analyzing multivariate data. Principal component analysis, factor analysis, canonical correlation, clustering. Emphasis on statistical and geometric motivation, practical application, and interpretation of results.

Prerequisites: STAT 320-2 and STAT 350-0 and MATH 240-0 or MATH 285-1 or MATH 281-3 or MATH 290-1or MATH 291-1 or GEN_ENG 205-1 or GEN_ENG 206-1. The course uses R extensively; hence, some experience with R will be useful.

STAT 350-0 Regression Analysis (1 Unit)  

Simple linear regression and correlation, multiple regression, residual analysis, model building, variable selection, multi-collinearity and shrinkage estimation, nonlinear regression.

Prerequisites: STAT 201-0 or COMP_SCI 110-0 and STAT 202-0 or STAT 210-0 or STAT 232-0 or PSYCH 201-0 or IEMS 201-0 or IEMS 303-0. Co-requisite: STAT 320-1 or STAT 383-0 or MATH 310-1 or MATH 311-1 or MATH 314-0 or MATH 385-0 or ELEC_ENG 302-0 or IEMS 302-0.

STAT 351-0 Design and Analysis of Experiments (1 Unit)  

Methods of designing experiments and analyzing data obtained from them: one-way and two-way layouts, incomplete block designs, factorial designs, random effects, split-plot and nested designs.

Prerequisite: STAT 320-1 or STAT 383-0 or MATH 310-1 or MATH 311-1 or MATH 314-0 or MATH 385-0 or ELEC_ENG 302-0 or IEMS 302-0 or equivalent.

STAT 352-0 Nonparametric Statistical Methods (1 Unit)  

Survey of nonparametric methods, with emphasis on understanding their application. Estimation of a distribution function, density estimation, and nonparametric regression.

Prerequisite: STAT 350-0.

STAT 353-0 Advanced Regression (1 Unit)  

This course covers modern regression methods, including: (1) generalized linear models (binary, categorical, and count data), (2) random effects, mixed effects, and nonlinear models, and (3) model selection. The course emphasizes both the theoretical development of the methods, as well as their application, including the communication of models and results both verbally and in writing.

Prerequisites: STAT 350-0 and STAT 320-2 or STAT 420-2 or MATH 310-2.

STAT 354-0 Time Series Modeling (1 Unit)  

Introduction to modern time series analysis. Autocorrelation, time series regression and forecasting, ARIMA and GARCH models.

Prerequisites: STAT 320-1 or STAT 383-0 or MATH 310-1 or MATH 311-1 or MATH 314-0 or MATH 385-0 or ELEC_ENG 302-0 or IEMS 302-0. Corequisite: STAT 350-0.

STAT 355-0 Analysis of Qualitative Data (1 Unit)  

Introduction to the analysis of qualitative data. Measures of association, loglinear models, logits, and probits.

Prerequisite: STAT 320-2 or equivalent.

STAT 356-0 Hierarchical Linear Models (1 Unit)  

Introduction to the theory and application of hierarchical linear models. Two and three level linear models, hierarchical generalized linear models, and application of hierarchical models to organizational research and growth models.

Prerequisites: STAT 320-2 and STAT 350-0.

STAT 357-0 Introduction to Bayesian Statistics (1 Unit)  

Introduction to basic concepts and principles in Bayesian inference such as the prior, likelihood, posterior and predictive distributions, as well as an introduction to a variety of computational algorithms for Bayesian inference. Students learn how to develop, describe, implement and critique statistical models from a Bayesian perspective.

Prerequisites: STAT 320-2 and STAT 301-2 or STAT 350-0 or consent of instructor.

STAT 359-0 Topics in Statistics (1 Unit)  

Topics in theoretical and applied statistics to be chosen by instructor.

Prerequisite: varies by course topic.

STAT 360-0 Introduction to Generative AI (1 Unit)  

This course will provide an introduction to generative AI. In particular, we will cover large language models and diffusion models. By the end of the course, students should have a thorough understanding of all major components underpinning modern large language models. Students should be able to train their own large language models after taking this class.

Prerequisites: Linear algebra (MATH 240-0), STAT 320-2 or equivalent, some familiarity with deep learning, Python experience.

STAT 365-0 Introduction to the Analysis of Financial Data (1 Unit)  

Statistical methods for analyzing financial data. Models for asset returns, portfolio theory, parameter estimation. The statistical software R is used.

Prerequisites: STAT 320-3 and MATH 240-0 or MATH 285-1 or MATH 281-3 or MATH 290-1or MATH 291-1 or GEN_ENG 205-1 or GEN_ENG 206-1.

STAT 370-0 Human Rights Statistics (1 Unit)  

Development, analysis, interpretation, use, and misuse of statistical data and methods for description, evaluation, and political action regarding war, disappearances, justice, violence against women, trafficking, profiling, elections, hunger, refugees, discrimination, etc.

Prerequisites: Two of STAT 325-0, STAT 350-0, STAT 320-2, STAT 320-3; or ECON 381-1, ECON 381-2; or MATH 386-1, MATH 386-2; or IEMS 303-0, IEMS 304-0.

STAT 415-0 Introduction to Machine Learning (1 Unit)  

This course is for students doing advanced studies in statistics and certain other fields will provide an introduction to modern machine learning methods. Topics include supervised learning, sparsity, logistic regression, SVM, kernel methods, deep learning, unsupervised learning, and real world problems including fairness and interpretability of black box models. Not for data science majors/minors - students studying data science should take STAT 362 instead.

Prerequisites: MATH 240-0 and MATH 230-2 and STAT 320-2 or Statistics and Data Science graduate standing.

STAT 420-1 Introduction to Statistical Theory & Methodology-1 (1 Unit)  

Distribution theory, characteristic functions, moments and cumulants, random variables, sampling theory, and common statistical distributions.

STAT 420-2 Introduction to Statistical Theory & Methodology-2 (1 Unit)  

Methods of estimation, hypothesis tests, confidence intervals, least squares, likelihood methods, and large-sample methods. Third Quarter: Normal linear models and its various extensions.

STAT 420-3 Introduction to Statistical Theory & Methodology-3 (1 Unit)  

Normal linear models and its various extensions.

STAT 430-1 Probability for Statistical Inference 1 (1 Unit)  

Foundations of measure theoretic probability, with applications to statistics.

Prerequisites: MATH 320-1 and STAT 420-1.

STAT 430-2 Probability for Statistical Inference 2 (1 Unit)  

A second course in measure-theoretic probability, with an eye towards statistics. Topics include Markov chains, conditional expectation, martingales, Poisson processes, Brownian motion, and selected advanced topics, together with statistical applications.

Prerequisite: STAT 430-1 or permission of instructor.

STAT 435-0 Mathematical Foundations of Machine Learning (1 Unit)  

In this course, students are expected to explore some mathematical foundations of modern machine learning under a problem-solving framework. Topics include probability theory, frequentist statistics, Bayesian statistics, tensor algebra, vector calculus, convex and stochastic optimization, stochastic processes and sampling, sequential optimization and dynamic programming. This class strongly emphasizes on developing problem-solving skills.

Prerequisite: 420-1(recommended but not required).

STAT 436-0 Reinforcement Learning (1 Unit)  

The first half of the course will cover classical reinforcement learning concepts: MDPs, tree search, trajectory optimization, value iteration, policy iteration, and SARSA. The second half will cover modern deep reinforcement learning: deep Q learning, policy gradients, Monte Carlo tree search, inverse reinforcement learning, noise contrastive estimation. We will conclude by discussing philosophical questions related to AI and superhuman intelligence.

Prerequisite: Linear algebra (MATH 240-0), some familiarity with Stochastic Gradient Descent, some Python experience, prior experience with PyTorch helpful but not required.

STAT 439-0 Meta-Analysis (1 Unit)  

Statistical methods for combining results of multiple studies. Effect size indexes and their estimators, combined estimation and test of heterogeneity, modeling between-study variation in effect sizes, models for publication selection.

Prerequisite: STAT 350-0 or a similar course in regression.

STAT 440-0 Applied Stochastic Processes for Statistics (1 Unit)  

We introduce statistical applications of stochastic processes, such as in survival analysis, Markov Chain Monte Carlo, and clinical trials. An integral part will be the student presentations on related topics.

Prerequisites: STAT 420-3 and STAT 430-1 and STAT 430-2.

STAT 450-1 Advanced Statistical Theory I (1 Unit)  

This is a first part of a Ph.D. course in theoretical statistics. We will cover a selection of modern topics in mathematical statistics, with a focus on high-dimensional statistical models and non- parametric statistical models. One of the main goals of this course is to provide the student with some theoretical background and mathematical tools to read and understand the current statistical literature on high-dimensional models.

Prerequisite: Linear algebra, real analysis, probability theory, statistical inference.

STAT 450-2 Advanced Statistical Theory II (1 Unit)  

This is a second part of a Ph.D. course in theoretical statistics. We will cover a selection of modern topics in mathematical statistics, with a focus on high-dimensional statistical models and non-parametric statistical models. One of the main goals of this course is to provide you with some theoretical background and mathematical tools to read and understand the current statistical literature on high-dimensional models.

Prerequisites: STAT 450-1 Advanced Statistical Theory 1.

STAT 455-0 Advanced Qualitative Data Analysis (1 Unit)  

Probit, logit, log-linear, and latent-class models. Multi-dimensional contingency tables; polytomous responses with continuous independent variables.

Prerequisites: STAT 350-0 and STAT 420-3.

STAT 456-0 Generalized Linear Models (1 Unit)  

Inference and fitting of generalized linear models with application to classical linear models, binomial and multinomial logit models, log-linear models, Cox's proportional hazards model and GEE's for longitudinal data.

Prerequisites: STAT 350-0 and STAT 420-3.

STAT 457-0 Applied Bayesian Inference (1 Unit)  

Introduction to computational algorithms for Bayesian inference. Observed data and data augmentation methods are considered in detail. Methods are illustrated with real examples.

Prerequisites: STAT 350-0 and STAT 420-3 or equivalent or students who have earned a Master’s degree in Statistics or permission of the instructor.

STAT 461-0 Advanced Topics in Statistics (1 Unit)  

STAT 465-0 Statistical Methods for Bioinformatics and Computational Biology (1 Unit)  

An introduction of statistical methodologies in cutting-edge fields of computational biology and bioinformatics topics including gene expression data analysis; biological sequence analysis.

STAT 499-0 Independent Study (1-3 Units)  

SEE DEPT FOR SECTION AND PERMISSION NUMBERS.

STAT 519-0 Responsible Conduct of Research Training (0 Unit)  

STAT 590-0 Research (1-3 Units)  

SEE DEPT FOR SECTION AND PERMISSION NUMBERS.

STAT 595-0 Internship (0 Unit)  

It is an internship program under Curricular Practical Training (CPT), where students would do an unpaid or paid internship on campus or in a non-NU company.