102 courses with the subject STAT, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
STAT 0001Introduction to Statistics and Data Science
In this course, we will learn introductory statistics using R with a focus on the application of statistical thinking to business problems. We will learn basic statistical concepts such as mean, variance, quantiles and hypothesis testing, and basic R programming for data management and analysis. We will work with traditional R's data, frame structure as well as the modern tibbles structure. Prerequisite: Percentages, average, powers, exponential, linear equation of a line, polynomials. 0.5 Course Units
STAT 0002Introduction to Statistics and Data Science
Continuation of STAT 0001. In this course, we will learn basic statistical inference procedures of estimation, confidence intervals, and hypothesis testing. We will also cover statistical inference of bivariate data, including correlation and simple linear regression models. Prerequisite: STAT 0001 or equivalent coursework. Basic R knowledge. 0.5 Course Units
The STAT 1018 honors section, which fulfills the STAT 1010 requirement, offers an introduction to probability and statistics for students who have studied calculus and are seeking a class with mathematical content. The class will assume good command of the material in MATH 1070 or MATH 1400 as a prerequisite. STAT 1018 is particularly recommended for students who are considering the concentration or minor in statistics and data science or other quantitative fields (such as quantitative finance).
or STAT 1028 Introductory Business Statistics or STAT 1120 Introductory Statistics or STAT 4310 Statistical Inference or ESE 4020 Statistics for Data Science or ECON 2310 Econometric Methods and Models
Additional NRSC Major Elective Courses Select 3 course units from the following: NRSC 0000-4999 Or any course with: Attribute ABBE (https:// catalog.upenn.edu/attributes/abbe/) University of Pennsylvania Catalog 171 Select 5 course units from the following: 1 NRSC 0000-4999 Or any course with: Attribute ABBM (https:// catalog.upenn.edu/attributes/abbm/) Total Course Units 36 You may count no more than one course toward both a Major and a Sector requirement. For Exceptions, check the Policy Statement (http:// www.college.upenn.edu/sectors-policy/). See the NRSC web site for approved courses in areas of specialized study. Students are encouraged to take a research course (NRSC 3999 Independent Research, NRSC 4999 Advanced Independent Research ) or do sponsored research in their junior or senior year. ry
Further development of the material in STAT 1110, in particular the analysis of variance, multiple regression, non-parametric procedures and the analysis of categorical data. Data analysis via statistical packages. This course may be taken concurrently with the prerequisite with instructor permission.
This course would introduce undergraduate students to the growing field of sports analytics, while allowing them to implement and integrate their knowledge base by exploring real sports data sets to solve real problems. While the context will be sports related, the skills and techniques gained will be widely applicable and generalizable with applications in diverse areas. Prerequisites: Must be a declared Statistics Concentrator or Business Analytics Concentrator or Statistics Minor or Data Science Minor. Permission from the Instructor is required. An interest in sports is highly recommended. 0.5 Course Units 2026-27 Catalog | Generated 08/03/26
STAT 4020Communicating Quantitative Analyses: A Capstone Course
This seminar-based capstone course provides an opportunity for students to hone their data science and statistical modeling skills, together with an emphasis on communicating quantitative results. This is not a “theoretical class”, but rather, experiential. It allows students to bring their existing knowledge from different disciplines to bear on new problems. Four real-life datasets will be analyzed during the quarter, and students will be expected to create and deliver in-class presentations for each analysis. The course will be suitable for anyone who wants more opportunities to analyze data, continue developing their programming skills and those who want to gain experience and confidence in presenting results and conclusions to an audience. Prerequisites: The course presumes that students have taken a sequence of stat courses such as STAT 1010/1020, or 4300/4310 and so are familiar with multiple regression analysis. In addition, they should have been exposed to more advanced techniques such as logistic regression and tree-based methods as taught in classes like STAT 4220/4230/4710. Finally, it will be assumed that students have knowledge of a programming language such as R or Python and an IDE such as R-Studio or Jupyter notebooks. Classes such as STAT 4050/4700 would meet this requirement. OR STAT 4300 OR STAT 4310) AND (STAT 4050 OR STAT 4700 OR STAT 4710) AND WH 1010 AND WH 2010 AND MGMT 3010 0.5 Course Units
The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.
This course follows from the introductory regression classes, STAT 1020, STAT 1120, and STAT 4310 for undergraduates and STAT 6130 for MBAs. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It delves into classification methodologies such as logistic regression. It also introduces classification and regression trees (CART) and the popular predictive methodologies known as random forest and boosted trees. By the end of the course the student will be familiar with and have applied these concepts and will be ready to use them in a work setting. The methodologies are implemented in a variety of software packages. Applications in JMP emphasize concepts and key modeling decisions. This course may be taken concurrently with the prerequisite with instructor permission.
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. This course may be taken concurrently with the prerequisite with instructor permission.
Other Wharton Requirements 33 Total Course Units 37 Students may select at most 1 CU outside of Wharton. Students can count only one of the two courses (CIS 4190/5190 or CIS 5200) towards the Business Analytics concentration. Course Title Course
This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study an 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). not met. This course may be taken concurrently with the prerequisite with instructor permission.
Waiving the Statistics Core completely if prerequisites are
STAT 4710Modern Data Mining
With the advent of the internet age, data are being collected at unprecedented scale in almost all realms of life, including business, science, politics, and healthcare. Data mining—the automated extraction of actionable insights from data—has revolutionized each of these realms in the 21st century. The objective of the course is to teach students the core data mining skills of exploratory data analysis, selecting an appropriate statistical methodology, applying the methodology to the data, and interpreting the results. The course will cover a variety of data mining methods including linear and logistic regression, penalized regression (including lasso and ridge regression), tree-based methods (including random forests and boosting), and deep learning. Students will learn the conceptual basis of these methods as well as how to apply them to real data using the programming language R. This course may be taken concurrently with the prerequisite with instructor permission.
This course will cover the design and analysis of sample surveys. Topics include simple sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias. This course may be taken concurrently with the prerequisite with instructor permission. Not Offered Every Year 1 Course Unit
STAT 4810Convex Optimization for Statistics and Data Science
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 (Math 3120, 3130, 3140 or equivalent), basic calculus (Math 2400 or equivalent), basic probability (STAT 4300 or equivalent), and knowledge of a programming language like MATLAB or Python to conduct simulation exercises. Also Offered As: OIDD 4810 OR MATH 2600 OR MATH 3000 OR MATH 3120 OR MATH 3140 OR ESE 2030 OR ENM 2400) 1 Course Unit
STAT 4850Foundations of Deep Learning with Applications
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. Mutually Exclusive: STAT 5850 1 Course Unit
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. Prerequisites: 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
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.
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.
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.
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.
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.
. Sophisticated tools for probability modeling and data analysis from the Bayesian perspective. Hierarchical models, mixture models and Monte Carlo simulation techniques.
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. 1 Course Unit
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. Also Offered As: OIDD 5770 Mutually Exclusive: OIDD 4770, OIDD 7770, STAT 4770, STAT 7770 0.5-1 Course Unit
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
STAT 5810Convex Optimization for Statistics and Data Science
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, l 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 r 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. Also Offered As: OIDD 5810 1 Course Unit
STAT 5850Foundations of Deep Learning with Applications
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). Mutually Exclusive: STAT 4850 1 Course Unit
University of Pennsylvania Catalog 415 Other Wharton Requirements 33 Total Course Units 37 FNCE 4020 is a 1.0 credit course; however, if students take this to fulfill their capstone requirements, which is 0.5 credit units, they may only count 0.5 credit units toward their concentration. Business, Energy, Environment and Sustainability Track Code Title Course
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 6210Accelerated Regression Analysis for Business
STAT 6210 is intended for students with recent, practical knowledge of the use of regression analysis in the context of business applications. This course covers the material of STAT 6130, but omits the foundations to focus on regression modeling. The course reviews statistical hypothesis testing and confidence intervals for the sake of standardizing terminology and introducing software, and then moves into regression modeling. The pace presumes recent exposure to both the theory and practice of regression and will not be accommodating to students who have not seen or used these methods previously. The interpretation of regression models within the context of applications will be stressed, presuming knowledge of the underlying assumptions and derivations. The scope of regression modeling that is covered includes multiple regression analysis with categorical effects, regression diagnostic procedures, interactions, and time series structure. The presentation of the course relies on computer software that will be introduced in the initial lectures. Recent exposure to the theory and practice of regression modeling is recommended.
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.
The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.
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. This course may be taken concurrently with the prerequisite with instructor permission.
In addition to these electives and with the permission of the BUAN faculty advisors, at most 1CU in total can come from a relevant Global Modular Course, Domestic Modular Course, Global Virtual Course, or Independent Study..
This course introduces modern text analytics, and the tools of natural language processing. Text and language are powerful repositories of knowledge and information, but the semi-structured nature of language makes deriving insights from text challenging. Modern analytic techniques introduced in this course make it significantly easier even for non-specialists to use text and language data to drive deep insights. The course will use several examples from real world applications in different industries such as ecommerce, healthcare and finance to illustrate these techniques. Students should be familiar with regression models at the level of Stat 6130 or Stat 1020, and the Python language at the level of Stat 4770 or Stat 7770. Familiarity with the Jupyter notebook development environment is presumed, as well as common Python packages such as pandas, NLTK and SpaCy. Those with more knowledge of Statistics, such as from Stat 7220/4220, or computing skills will benefit. The predominant software used in the course is Jupyter notebooks that use a Python interpreter. Familiarity with basic probability models is helpful but not presumed.
The “Moneyball revolution” in sports has inspired great interest in the transformative potential of statistics. This “1/2 credit course will introduce students to the growing field of sports analytics while creating for students an opportunity to practice and improve their analytical skills on real problems that are accessible and fun for anyone. This course is meant for students with an interest in sports and a foundational knowledge of statistics. While the context will be sports related and the expectation of students is that they are interested and knowledgeable about most major sports, the skills and techniques gained will be widely applicable and generalizable with applications in diverse areas. The course is very applied and very data driven. Students will conduct hands on work with real data using JMP software, R or Python. Along the way, students will learn new techniques for analyzing data and gain practical and useful skills that will be broadly applicable across many areas. 0.5 Course Units
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).
This course will expose students to the theoretical and empirical "building blocks" that will allow them to construct, estimate, and interpre powerful models of consumer behavior. Over the years, researchers and practitioners have used these models for a wide variety of applications, such as new product sales, forecasting, analyses of media usage, and targeted marketing programs. Other disciplines have seen equally broad utilization of these techniques. The course will be entirely lecture-based with a strong emphasis on real-time problem solving. Most sessions will feature sophisticated numerical investigations using Microsoft Excel. Much of the material is highly technical.
Select 5.5 course units of electives 5.5 MBA Core Requirements 9.5 Total Course Units 19 Please note: A student cannot declare both the Impact, Value, and Sustainable Business major and the Social and Governance Factors for Business major. A student also cannot declare both the Impact, Value, and Sustainable Business major and the Business, Energy, Environment, and Sustainability major.
This course will build on the fundamental concepts introduced in the prerequisite courses to allow students to acquire knowledge and programming skills in large-scale data analysis, data visualization, and stochastic simulation. Prerequisite: STAT 5030, 7050, or 7700 or equivalent background acquired through a combination of online courses that teach the R language and practical experience.
Statistical inference when the functional form of the distribution is not specified. Nonparametric function estimation, density estimation, survival analysis, contingency tables, association, and efficiency. Not Offered Every Year 1 Course Unit
This course will cover the design and analysis of sample surveys. Topics include simple random sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias. Not Offered Every Year 1 Course Unit 2026-27 Catalog | Generated 08/03/26
This course will provide an in depth investigation of statistical methods for drawing causal inferences from complex observational studies and imperfect randomized experiments. Formalization will be given for key concepts at the foundation of causal inference, including: confounding, comparability, positivity, interference, intermediate variables, total effects, controlled direct effects, natural direct and indirect effects for mediation analysis, generalizability, transportability, selection bias, etc.... These concepts will be formally defined within the context of a counterfactual causal model. Methods for estimating total causal effects in the context of both point and time-varying exposure will be discussed, including regression-based methods, propensity score techniques and instrumental variable techniques for continuous, discrete, binary and time to event outcomes. Mediation analysis will be discussed from a counterfactual perspective. Causal directed acyclic graphs (DAGs) and associated nonparametric structural equations models (NPSEMs) will be used to formalize identification of causal effects for static and dynamic longitudinal treatment regimes under unconfoundedness and unmeasured confounding settings. This formalization will be used to define, identify and make inferences about the joint effects of time-varying exposures in the presence of (possibly hidden) time-dependent covariates that are simultaneously confounders and intermediate variables. These methods include g-estimation of structural nested models, inverse probability weighted estimators of marginal structural models, and g-computation algorithm estimators. Credible quasi-experimental causal inference methods will be described, leveraging auxiliary variables such as instrumental variables, negative control variables, or more broadly confounding proxy variables. Quasi- experimental methods discussed will include the control outcome calibration approach, proximal causal inference, difference-in-differences and related generalizations of these methods. Semiparametric efficiency and the prospects for doubly robust inference will feature prominently throughout the course, including methods that combine modern semiparametric theory and machine learning techniques.
This is a course that prepares PhD students in statistics for research in multivariate statistics and high dimensional statistical inference. Topics from classical multivariate statistics include the multivariate normal distribution and the Wishart distribution; estimation and hypothesis testing of mean vectors and covariance matrices; principal component analysis, canonical correlation analysis and discriminant analysis; etc. Topics from modern multivariate statistics include the Marcenko-Pastur law, the Tracy-Widom law, nonparametric estimation and hypothesis testing of high-dimensional covariance matrices, high-dimensional principal component analysis, etc. Not Offered Every Year 1 Course Unit
This is a course that prepares PhD students in statistics for research in multivariate statistics and data visualization. The emphasis will be on a deep conceptual understanding of multivariate methods to the point where students will propose variations and extensions to existing methods or whole new approaches to problems previously solved by classical methods. Topics include: principal component analysis, canonical correlation analysis, generalized canonical analysis; nonlinear extensions of multivariate methods based on optimal transformations of quantitative variables and optimal scaling of categorical variables; shrinkage- and sparsity-based extensions to classical methods; clustering methods of the k-means and hierarchical varieties; multidimensional scaling, graph drawing, and manifold estimation. Not Offered Every Year 1 Course Unit
Students must choose courses from 3 different buckets. Suggestions for projects will be provided to students. Students may choose from these suggested projects or may also come up with their own project/advisor ideas. Students will be mentored jointly by the Program Director and by an advisor in the area of the project, and must receive approval by Faculty Director. The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2026 and later. Students should consult with their academic program regarding final certifications and requirements for graduation. Penn’s online Master of Science in Engineering (MSE) in Data Science builds on the achievements of its on-campus counterpart, preparing students for a wide range of data-centric careers, whether in technology and engineering, consulting, science, policy-making, or understanding patterns in literature, art or communications. No matter the discipline, fluency with data analysis methods is becoming essential in today’s world. Flexible and accessible in its online format, MSE-DS Online is available for both the full-time and part-time student. Its curriculum dives deeply into topics such as artificial intelligence, big data systems, data science for health, deep learning, natural language processing, internet and web systems, machine learning, etc. Graduates in MSE-DS Online will be able to apply a background in scalable, robust computational and statistical methods in whatever field they choose to pursue. For more information: https://online.seas.upenn.edu/degrees/mse-ds- online/ For students interested in learning more about the MSE in Data Science on campus program, click here (https://dats.seas.upenn.edu/program/).
Statistical learning theory studies the statistical aspects of machine learning and automated reasoning, through the use of (sampled) data. In particular, the focus is on characterizing the generalization ability of learning algorithms in terms of how well they perform on "new" data when trained on some given data set. The focus of the course is on: providing the fundamental tools used in this analysis; understanding the performance of widely used learning algorithms; understanding the "art" of designing good algorithms, both in terms of statistical and computational properties. Potential topics include: empirical process theory; online learning; stochastic optimization; margin based algorithms; feature selection; concentration of measure. Background in probability and linear algebra recommended.
Continuation of MATH 6480/STAT 9300, the 2nd part of Probability Theory for PhD students in the math or statistics department. The main topics include Brownian motion, martingales, Ito's formula, and their applications to random walk and PDE. Not Offered Every Year Also Offered As: AMCS 6491, MATH 6490 1 Course Unit
This course aims to prepare students for graduate work in the design, analysis, and implementation of statistical algorithms. The target audience is Ph.D. students in statistics or in adjacent fields, such as computer science, mathematics, electrical engineering, computational biology, economics, and marketing. We will take a fundamental approach and focus on classes of algorithms of primary importance in statistics and statistical machine learning. Some meta-classes of algorithms that may receive significant attention are optimization, sampling, and numerical linear algebra. I aim to make the content complementary rather than overlapping with other courses at Penn, such as ESE6050, CIS6770, and the CIS7000 series. While there may be some overlap in the portions of the course that cover optimization, the sampling (Monte Carlo and related) aspects of the course are, to my knowledge, hard to find elsewhere at Penn. The course is fast paced and I expect a certain degree of mathematical preparation. Most students in the above mentioned programs will have the requisite mathematics background. I also expect familiarity with an appropriate programming language such as R, python, or matlab. The course will be mostly language agnostic. However, I may at times give example code in one of these languages, and you will be expected to be able to read the code even if it is not in your "primary" language. We may make use of various open-source toolboxes and packages for these environments, such as the Stan probabilistic programming language (best used with R) and the cvx toolbox for convex programming (available for multiple platforms but perhaps best used with matlab).
This is a course that prepares 1st year PhD students in statistics for a research career. This is not an applied statistics course. Topics covered include: linear models and their high-dimensional geometry, statistical inference illustrated with linear models, diagnostics for linear models, bootstrap and permutation inference, principal component analysis, smoothing and cross-validation.
This course is designed for Ph.D. students in statistics and will cover various advanced methods and models that are useful in applied statistics. Topics for the course will include missing data, measurement error, nonlinear and generalized linear regression models, survival analysis, experimental design, longitudinal studies, building R packages and reproducible research.
Theory of the Gaussian Linear Model, with applications to illustrate and complement the theory. Distribution theory of standard tests and estimates in multiple regression and ANOVA models. Model selection and its consequences. Random effects, Bayes, empirical Bayes and minimax estimation for such models. Generalized (Log-linear) models for specific non-Gaussian settings.
Behavioral and Biological Sciences Statistics and Data Science, PhD Wharton’s PhD program in Statistics and Data Science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, well as fields across the rest of the University such as biostatistics w the Medical School and computer science within the Engineering School. Major areas of departmental research include: • analysis of observational studies; • Bayesian inference, bioinformatics; • decision theory; • game theory; • high dimensional inference; • information theory; • machine learning; • model selection; • nonparametric function estimation; and • time series analysis. Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skill mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries. For more information: https://statistics.wharton.upenn.edu/programs/ phd/curriculum/ The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2026 and later. Students should 2026-27 Catalog | Generated 08/03/26 consult with their academic program regarding final certifications and requirements for graduation. 0.5 Curriculum Code Title Course
STAT 9800Introductory to Biomedical Data Science Research
This course offers a comprehensive introduction to biomedical data science research, tailored for graduate students from Statistics and various interdisciplinary domains. Aimed at facilitating end-to-end data science research capabilities, this course covers the development and application of computational methods and statistical techniques for analyzing voluminous datasets, particularly in biology, healthcare, and medicine. Students will gain insights into various data types prevalent in biomedical research, emerging large-scale data resources, and the art of formulating scientific questions. The course encompasses methodology research, scientific research, collaborative research, computing tools, software development, as well as scientific writing, including both research papers and grant proposals. By the end of the course, students will be equipped with the foundational skills and knowledge required to excel as statisticians and research scientists, whether they choose to pursue a career in industry or academia. Prerequisite: For students from the STAT department, this course is tailored for those who have successfully completed the qualifying exam and are ready to embark on their research journey. Exceptions for first-year students will be considered on an individual basis. For master's or Ph.D. students from other departments or programs, such as AMCS, the prerequisites will differ based on their specific curriculum. At a minimum, students should have master-level expertise in one or more of the following areas: applied mathematics and probability, computing and software development, web development, bioinformatics, biostatistics, epidemiology, computational biology, genetics/genomics, neuroscience, radiology, and medical imaging. 1 Course Unit
STAT 9910Seminar in Advanced Application of Statistics
This seminar is for graduate students who wish to learn about current research frontiers. It covers advanced topics in probability, statistical theory and methods, applied statistics, data science and artificial intelligence. Specific topics vary from year to year and emphasize both theoretical foundations and applications.
STAT 9912Seminar in Advanced Application of Statistics (Optimization)
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications. 0.5-1 Course Unit
STAT 9913Seminar in Advanced Application of Statistics - Probability
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications. 0.5-1 Course Unit
STAT 9915Seminar in Advanced Application of Statistics
This seminar-based 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 on a data science problem of interest to a community organization. 0.5-1 Course Unit
STAT 9916Seminar in Advanced Application of Statistics
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications. 0.5-1 Course Unit
STAT 9917Seminar in Advanced Application of Statistics
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications. 0.5-1 Course Unit
STAT 9918Seminar in Advanced Application of Statistics
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications. 0.5-1 Course Unit
