### STAT1010 - Intro Business Stat (Course Syllabus)

Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus, but is not inappropriate for students in the college. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: MATH 1070 OR MATH 1400 OR MATH 1100

### STAT1018 - Intro Business Stat (Course Syllabus)

The STAT 1018 honors section, which fulfills the STAT 1010 requirement, will cover the fundamentals of statistics through the lens of a skeptical statistician. Students will be introduced to the R language, used widely in industry as well as in upper-level statistics and data science courses. Using examples from the upcoming election, the pandemic and other topics, we will critically examine both well-accepted and controversial claims. Examples: Can we trust election polls anymore, and are some provably more reliable than others? Should we, or anyone, still be wearing masks? We will cover the basics of probability and statistics (using a textbook costing less than \$20), in order that you can use data to answer the following four questions: 1. What are the chances? 2. What's the best estimate? 3. Is there a difference? 4. How are these things related? This course is recommended for those considering a statistics and data science concentration or minor, as well as anyone interested in a more challenging introductory approach to statistical concepts. No prior knowledge of programming, probability or statistics is required for this course.

Prerequisites: MATH 1070 OR MATH 1400 OR MATH 1100

### STAT1020 - Intro Business Stat (Course Syllabus)

Continuation of STAT 1010 or STAT 1018. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: STAT 1010 OR STAT 1018

### STAT1028 - Intro Business Stat (Course Syllabus)

Honors continuation of STAT 1010 or STAT 1018. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: STAT 1010 OR STAT 1018

### STAT1110 - Introductory Statistics (Course Syllabus)

Introduction to concepts in probability. Basic statistical inference procedures of estimation, confidence intervals and hypothesis testing directed towards applications in science and medicine. The use of the JMP statistical package. Knowledge of high school algebra is required for this course.

### STAT1120 - Introductory Statistics (Course Syllabus)

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.

Prerequisites: STAT 1110

### STAT3990 - Independent Study (Course Syllabus)

Written permission of instructor and the department course coordinator required to enroll in this course.

### STAT4010 - Sports Analytics (Course Syllabus)

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.

### STAT4020 - Communicating Quant. Analyses (Course Syllabus)

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.

### STAT4050 - Stat Computing with R (Course Syllabus)

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.

Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4300

### STAT4100 - Data Collect & Acquisit (Course Syllabus)

This course will give students a solid grasp of different data collection strategies and when and how they can be applied in practice. At the same time, important current ideas such as data confidentiality and ethical considerations will be addressed. The students will have designed and fielded a sample survey and designed and fielded an online experiment (A/B test). Student will collect data through web scraping activities and/or using an API. Students will summarize their collected data and subsequent inferences, culminating with an in-class presentation. The course is structured in two parts. The first part is a "Strategies" component that addresses different data collection strategies. It will discuss sample designs, experimentation, and observational studies. The second part of the course is about "Platforms" and goes into the practicalities of the implementation of the different strategies. Given the data science perspective of this course, this is focused on web enabled approaches. Familiarity with either R or Python is expected and specifically the R-Studio or Jupyter notebooks platforms. Courses such as Stat 4050 or Stat 4770 would meet this requirement. Statistics, through the level of multiple regression is required. This requirement may be fulfilled with undergraduate courses such as Stat 1020 or Stat 1120.

### STAT4220 - Predictive Analytics (Course Syllabus)

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.

Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4310

### STAT4230 - Machine Learning in Business (Course Syllabus)

This course introduces students to machine learning techniques used in business applications. The main topics include: cross validation, variable selection procedures, shrinkage methods such as lasso, logistic regression, k-nearest neighbors, ROC curves and confusion matrix, trees, kernel based learning, resampling techniques, random forests, boosting, neural networks & deep learning, matrix methods including singular value decomposition (SVD) and its application in principal component analysis (PCA), and some unsupervised methods such as k-means and density based clustering. Students will learn to apply these methods in a wide range of settings such as marketing and finance, and will gain hands-on experience through class assignments and competitions. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4310

### STAT4240 - Text Analytics (Course Syllabus)

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.

### STAT4300 - Probability (Course Syllabus)

Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.

Prerequisites: MATH 1080 OR MATH 1410 OR MATH 1510

### STAT4310 - 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. This course does not have business applications but has significant overlap with STAT 1010 and 1020. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: STAT 4300

### STAT4320 - 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. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: STAT 4300 OR STAT 5100

### STAT4330 - 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 4300 AND (MATH 2400 OR MATH 3120 OR MATH 3140)

### STAT4350 - 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. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4310

### STAT4420 - Intro Bayes Data Analys (Course Syllabus)

The course will introduce data analysis from the Bayesian perspective to undergraduate students. We will cover important concepts in Bayesian probability modeling as well as estimation using both optimization and simulation-based strategies. Key topics covered in the course include hierarchical models, mixture models, hidden Markov models and Markov Chain Monte Carlo. A course in probability (STAT 4300 or equivalent); a course in statistical inference (STAT 1020, STAT 1120, STAT 4310 or equivalent); and experience with the statistical software R (at the level of STAT 4050 or STAT 4700) are recommended.

### STAT4510 - Fund of Actuarial Sci I (Course Syllabus)

This course is the usual entry point in the actuarial science program. It is required for students who plan to concentrate or minor in actuarial science. It can also be taken by others interested in the mathematics of personal finance and the use of mortality tables. For future actuaries, it provides the necessary knowledge of compound interest and its applications, and basic life contingencies definition to be used throughout their studies. Non-actuaries will be introduced to practical applications of finance mathematics, such as loan amortization and bond pricing, and premium calculation of typical life insurance contracts. Main topics include annuities, loans and bonds; basic principles of life contingencies and determination of annuity and insurance benefits and premiums. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: MATH 1400 AND STAT 4300

### STAT4520 - Fund of Actuarial Sci II (Course Syllabus)

This specialized course is usually only taken by Wharton students who plan to concentrate in actuarial science and Penn students who plan to minor in actuarial mathematics. It provides a comprehensive analysis of advanced life contingencies problems such as reserving, multiple life functions, multiple decrement theory with application to the valuation of pension plans. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: STAT 4510 OR BEPP 4510

### STAT4530 - Actuarial Statistics (Course Syllabus)

This course covers models for insurer's losses, and applications of Markov chains. Poisson processes, including extensions such as non-homogeneous, compound, and mixed Poisson processes are studied in detail. The compound model is then used to establish the distribution of losses. An extensive section on Markov chains provides the theory to forecast future states of the process, as well as numerous applications of Markov chains to insurance, finance, and genetics. The course is abundantly illustrated by examples from the insurance and finance literature. While most of the students taking the course are future actuaries, other students interested in applications of statistics may discover in class many fascinating applications of stochastic processes and Markov chains. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: STAT 4300

### STAT4700 - 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: Waiving the Statistics Core completely if prerequisites are not met. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: (STAT 1010 AND STAT 1020) OR (STAT 1110 AND STAT 1120) OR STAT 4310 OR (ECON 2300 AND ECON 2310)

### STAT4710 - Modern Data Mining (Course Syllabus)

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.

Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4310

### STAT4740 - Modern Regression (Course Syllabus)

Function estimation and data exploration using extensions of regression analysis: smoothers, semiparametric and nonparametric regression, and supervised machine learning. Conceptual foundations are addressed as well as hands-on use for data analysis. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: STAT 1020 OR STAT 1120

### STAT4750 - Sample Survey Design (Course Syllabus)

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.

Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4310

### STAT4760 - Appl Prob Models Mktg (Course Syllabus)

This course will expose students to the theoretical and empirical "building blocks" that will allow them to construct, estimate, and interpret 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.

### STAT4770 - 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. This requirement may be fulfilled with Undergraduate courses such as Stat 1020, Stat 1120.

### STAT4800 - Adv Stat Computing (Course Syllabus)

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 7700 or 7050 or equivalent background acquired through a combination of online courses that teach the R language and practical experience. This course may be taken concurrently with the prerequisite with instructor permission.

Prerequisites: STAT 4050 OR STAT 4700

### STAT4900 - 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. The course is intended for statistics concentrators and minors. Knowledge of R such as that covered in STAT 4050 or STAT 4700 is recommended.

Prerequisites: STAT 4300 AND (STAT 1020 OR STAT 1120 OR STAT 4310)

### 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

### 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.

### STAT5800 - Adv Stat Computing (Course Syllabus)

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.

Prerequisites: STAT 5030 OR STAT 7050 OR STAT 7700

### 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.