Jacobs MSQF Program Course Descriptions

STAT5000 - Applied Reg & Analy Var (Course Syllabus)

An applied graduate level course in multiple regression and analysis of variance for students who have completed an undergraduate course in basic statistical methods. Emphasis is on practical methods of data analysis and their interpretation. Covers model building, general linear hypothesis, residual analysis, leverage and influence, one-way anova, two-way anova, factorial anova. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.

STAT5010 - Int To Nonp & Loglin Mod (Course Syllabus)

An applied graduate level course for students who have completed an undergraduate course in basic statistical methods. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.

STAT5030 - Data Analy & Stat Comp (Course Syllabus)

This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Two courses at the statistics 4000 or 5000 level.

STAT5100 - Probability (Course Syllabus)

Elements of matrix algebra. Discrete and continuous random variables and their distributions. Moments and moment generating functions. Joint distributions. Functions and transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency, maximum likelihood, minimum variance. Confidence intervals. A one-year course in calculus is recommended.

STAT5110 - Statistical Inference (Course Syllabus)

Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course.

Prerequisites: STAT 5100

STAT5120 - Mathematical Statistics (Course Syllabus)

An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures.

Prerequisites: STAT 4300 OR STAT 5100

STAT5150 - Adv Stat Inference I (Course Syllabus)

STAT 5150 is aimed at first-year Ph.D. students and builds a good foundation in statistical inference from the first principles of probability.

Prerequisites: STAT 4300 AND STAT 4310 AND MATH 2400

STAT5160 - Adv Stat Inference II (Course Syllabus)

STAT 5160 is a natural continuation of STAT 5150, and the main focus is on asymptotic evaluations and regression models. Time permitting, it also discusses some basic nonparametric statistical methods.

Prerequisites: STAT 5150

STAT5200 - Applied Econometrics I (Course Syllabus)

This is a course in econometrics for graduate students. The goal is to prepare students for empirical research by studying econometric methodology and its theoretical foundations. Students taking the course should be familiar with elementary statistical methodology and basic linear algebra, and should have some programming experience. Topics include conditional expectation and linear projection, asymptotic statistical theory, ordinary least squares estimation, the bootstrap and jackknife, instrumental variables and two-stage least squares, specification tests, systems of equations, generalized least squares, and introduction to use of linear panel data models.

Prerequisites: (MATH 1080 OR MATH 1410) AND MATH 3120

STAT5210 - Applied Econometrics II (Course Syllabus)

Topics include system estimation with instrumental variables, fixed effects and random effects estimation, M-estimation, nonlinear regression, quantile regression, maximum likelihood estimation, generalized method of moments estimation, minimum distance estimation, and binary and multinomial response models. Both theory and applications will be stressed.

Prerequisites: STAT 5200

STAT5330 - Stochastic Processes (Course Syllabus)

An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.

Prerequisites: STAT 5100

STAT5350 - Forecasting Methods Mgmt (Course Syllabus)

This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations.

STAT5420 - Bayesian Meth & Comp (Course Syllabus)

Sophisticated tools for probability modeling and data analysis from the Bayesian perspective. Hierarchical models, mixture models and Monte Carlo simulation techniques.

Prerequisites: STAT 4300 OR STAT 5100

STAT5440 - Applied Bayesian Modeling (Course Syllabus)

This is a 5000-level graduate course that focuses on the application of statistical techniques from a Bayesian perspective. It is designed for high-level senior undergraduate students who have completed course 4420, as well as for graduate students from various non-statistics fields interested in applying Bayesian methods to their research. The curriculum begins with a refresher on Bayesian statistical principles, followed by practical applications using established software like Stan for model sampling. Critical subjects included in the course are Bayesian model selection, Stan programming, BRMS, variational Bayes methods, regression and mixed effects models, hierarchical structures, dynamic linear models, survival analysis, Gaussian processes, and explorations in nonparametric Bayesian approaches.

Prerequisites: STAT 5100 OR STAT 9300

STAT5710 - Modern Data Mining (Course Syllabus)

Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression. Contemporary methods such as KNN (K nearest neighbor), Random Forest, Support Vector Machines, Principal Component Analyses (PCA), the bootstrap and others are also covered. Text mining especially through PCA is another topic of the course. While learning all the techniques, we keep in mind that our goal is to tackle real problems. Not only do we go through a large collection of interesting, challenging real-life data sets but we also learn how to use the free, powerful software "R" in connection with each of the methods exposed in the class. Prerequisite: two courses at the statistics 4000 or 5000 level or permission from instructor.

STAT5770 - Intro To Python Data Sci (Course Syllabus)

The goal of this course is to introduce the Python programming language within the context of the closely related areas of statistics and data science. Students will develop a solid grasp of Python programming basics, as they are exposed to the entire data science workflow, starting from interacting with SQL databases to query and retrieve data, through data wrangling, reshaping, summarizing, analyzing and ultimately reporting their results. Competency in Python is a critical skill for students interested in data science. Prerequisites: No prior programming experience is expected, but statistics, through the level of multiple regression is required.

STAT5800 - Adv Stat Computing (Course Syllabus)

This course covers the underlying computational methods that both underlie modern statistical and machine learning tools, as well as explicitly computational approaches to performing statistical methods. The class will cover the basics of computer arithmetic, simulation, bootstrap, jackknife and permutation methods, numerical methods for optimization and their application to statistical estimation and machine learning, nonparametric smoothing, generating random variables, and simulation methods. The course will assume familiarity with programming in the R computing environment. By the end of the course, students should be able to design and code estimation methods for sophisticated statistical models, as well as procedures to provide uncertainty about those estimates

Prerequisites: (STAT 1110 OR STAT 1120 OR STAT 4310 OR ESE 4020) AND (STAT 4300 OR ESE 3010) AND (MATH 1080 OR MATH 1410 OR MATH 1610) AND (MATH 2400 OR MATH 3120 OR MATH 3140)

STAT5810 - Conv Optim Stat Data Sci (Course Syllabus)

Convex optimization has become a real pillar of modern data science and has transformed algorithm designs. A wide spectrum of problems in statistics, machine learning, and engineering can be formulated as optimization tasks that exhibit favorable convexity properties, which admit standardized and efficient solutions. This course aims to introduce the elements of convex optimization, concentrating on modeling aspects and algorithms that are useful in data science applications. Topics include convex sets, convex functions, linear and quadratic programs, semidefinite programming, optimality conditions and duality theory. We will visit important applications in statistics and machine learning to demonstrate the wide applicability of convex optimization. We will also cover effective optimization algorithms like gradient descent and Newton's method. Prerequisites: Basic linear algebra, basic calculus, basic probability, and knowledge of a programming language like MATLAB or Python to conduct simulation exercises.

STAT5850 - Foundations of Deep Learning (Course Syllabus)

This course serves as a first, conceptual introduction to Deep Learning, which is the technology at the heart of modern AI. Topics include: what is a neural network and how to train it, generative AI, failure modes and safety of deep learning, and efficient deep learning. Prerequisites: Calculus, beginner programming experience with Python. Highly recommended: basic linear algebra (matrices and matrix multiplication).

STAT5900 - Causal Inference (Course Syllabus)

Questions about cause are at the heart of many everyday decisions and public policies. Does eating an egg every day cause people to live longer or shorter or have no effect? Do gun control laws cause more or less murders or have no effect? Causal inference is the subfield of statistics that considers how we should make inferences about such questions. This course will cover the key concepts and methods of causal inference rigorously. Background in probability and statistics; some knowledge of R is recommended.

STAT5920 - Community Data Science (Course Syllabus)

This course provides students with the opportunity to hone their data science skills and gain practical experience by working with a community organization on a data science problem of interest to the organization. Students will gain skills in problem formulation, collaboration with community organizations and communication of data science results. Students will work in groups of 3-5 on a data science problem of interest to a community organization. This is an Academically Based Community Service (ABCS) course. Prerequisite: The course presumes that students have taken a sequence of introductory statistics courses such as STAT 1010/1020, or 4300/4310 and that they have taken a course that has exposed them to more advanced techniques such as STAT 4220, 4230, 4420, 4710 or 4730. It will be assumed that students have knowledge of a statistical programming language such as R or Python. Classes such as STAT 4050, 4700, 4770 or 4800 would meet this requirement.