STAT6130 - Regr Analysis For Bus (Course Syllabus)
This course provides the fundamental methods of statistical analysis, the art and science if extracting information from data. The course will begin with a focus on the basic elements of exploratory data analysis, probability theory and statistical inference. With this as a foundation, it will proceed to explore the use of the key statistical methodology known as regression analysis for solving business problems, such as the prediction of future sales and the response of the market to price changes. The use of regression diagnostics and various graphical displays supplement the basic numerical summaries and provides insight into the validity of the models. Specific important topics covered include least squares estimation, residuals and outliers, tests and confidence intervals, correlation and autocorrelation, collinearity, and randomization. The presentation relies upon computer software for most of the needed calculations, and the resulting style focuses on construction of models, interpretation of results, and critical evaluation of assumptions.
Prerequisites: STAT 6110
STAT6210 - Acc Regression Analysis (Course Syllabus)
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.
STAT7010 - 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.
STAT7050 - 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 6130 OR STAT 6210
STAT7100 - 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 7050 or Stat 7770 would meet this requirement. Statistics, through the level of multiple regression is required. This requirement may be fulfilled with MBA courses such as Stat 6130/6210, or by waiving MBA statistics.
STAT7110 - 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 6130 OR STAT 6210
STAT7220 - 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 methodology known as the random forest. By the end of the course the student will be familiar with and have applied all these tools and will be ready to use them in a work setting. The methodologies can all be implemented in either the JMP or R software packages. This course is formerly STAT 6220.
Prerequisites: STAT 6130 OR STAT 6210
STAT7230 - 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.
Prerequisites: STAT 6130 OR STAT 6210
STAT7240 - 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.
STAT7700 - 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.
STAT7760 - 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.
STAT7770 - 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 MBA courses such as STAT 6130/6210; or by waiving MBA statistics.
STAT7800 - 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
STAT8510 - 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. Prerequisite: One semester of calculus.
STAT8520 - 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.
Prerequisites: STAT 8510 OR BEPP 8510
STAT8530 - 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. Prerequisite: Two semesters of statistics.
STAT8990 - Independent Study (Course Syllabus)
Written permission of instructor, the department MBA advisor and course coordinator required to enroll.
Department of Statistics and Data Science
The Wharton School,
University of Pennsylvania
Academic Research Building
265 South 37th Street, 3rd & 4th Floors
Philadelphia, PA 19104-1686
Phone: (215) 898-8222