Research-Focused Courses

Visit the Penn Course Catalog to learn more.

Visit the Penn Course Catalog to learn more.

The focus of this course is on analysis of the issues and options which must be faced in developing a successful technological venture and on the creation of a winning business plan. Particular attention is directed to the identification of technology-based venture opportunities, evaluation of technical feasibility and commercial potential, and planning for successful commercialization. All students must receive instructor’s permission.

This course examines the role of marketing research in the formulation and solution of marketing problems, and the development of the student’s basic skills in conducting and evaluating marketing research projects. Special emphasis is placed on problem formulation, research design, alternative methods of data collection (including data collection instruments, sampling, and field operations), and data analysis techniques. Applications of modern marketing research procedures to a variety of marketing problems are explored.

In the past decade, massive shifts in how companies interact with their customers have suddenly made field experiments an economically feasible way to learn about a variety of business questions such as what types of promotions are most effective, what products should be stocked at a store, how e-mail promotions should be designed, how sales staff should be compensated, etc. Many marketers engaged in online retailing, direct-marketing, online advertising, media management, etc. are rapidly embracing a “test and learn” philosophy and a number of platforms such as Google Website Optimizer, have been developed to facilitate rigorous field experiments in the online environment. Just as with the quality revolution in manufacturing during the 1980s and 1990s, the rapid rise of the “test and learn” philosophy in marketing has created a huge demand for those who can design, field, and analyze marketing experiments.

Through this course, you will learn and practice a wide range of critical skills, from the statistical methods used to design and analyze experiments to the management and strategy required to execute an experiment and act on the results. Although the cases and examples will focus on marketing problems, the material covered can be applied in a number of other domains particularly operations management and product design.

Prerequisites: MKTG101 or faculty permission is required; STAT 101, STAT 431, or equivalent is recommended.

Basic neuroscience made steady progress throughout the 20th century with only small areas of application outside of medicine.  Over the past 30 years, however, breakthroughs in measurement and computation have accelerated basic research and created major applications for business and technology. Currently, applications to consumer research and product development are experiencing explosive growth that has been met with both excitement and skepticism.  This mini-course provides an overview of these developments.

There are three parts to the course.  First, there will be a “crash course” in neuroscience.  A key take-away from this course is to gain the elementary knowledge of the science that is necessary to separate “neuro-reality” from “neuro-hype.” Second, current applications of neuroscience in marketing research will be covered in some detail.  Topics will range from well-known and widely used eye-tracking measures in the lab and the field to emerging methods and measures, such as mobile EEG and face reading technologies. Application areas include, packaging and shelf display, copy testing for television and print advertisements, video games, and driving simulators.  The third part will cover application of neuroscience in the development of new products.  Potential topics include usability studies, wearable physiologically devices and apps, and sensory branding for foods and fragrances.  Traditional application in pharmaceuticals and medical devices will also be covered.

This class provides a high-level introduction to the field of judgment and decision making (JDM) and in-depth exposure to the process of doing research in this area. Throughout the semester you will gain hands-on experience with several different JDM research projects. You will be paired with a PhD student or faculty mentor who is working on a variety of different research studies. Each week you will be given assignments that are central to one or more of these studies, and you will be given detailed descriptions of the research projects you are contributing to and how your assignments relate to the successful completion of these projects. To complement your hands-on research experience, throughout the semester you will be assigned readings from the book Nudge by Thaler and Sunstein, which summarizes key recent ideas in the JDM literature.

This course is taught with the more descriptive title of “Scripting for Business Analytics.” “Business Analytics” refers to modeling and analysis undertaken for purposes of management and supporting decision-making. The varieties of techniques and methods are numerous and growing, including simple equational models, constrained optimization models, probabilistic models, visualization, data analysis, and much more. Elementary modeling of this sort can be undertaken in Excel and other spreadsheet programs, but “industrial strength” applications typically use more sophisticated tools, based on scripting languages. Scripting languages are programming languages that are designed to be learned easily and to be used for special purposes, rather than for large-scale application programming. This course focuses on the special purposes associated with business analytics and teaches MATLAB and Python in this context. MATLAB and Python are widely used in practice (both in management and in engineering), as are the business analytic methods covered in the course. Prior programming experience is useful, but not required or presumed for this course.

The past few years have seen an explosion in the amount of data collected by businesses and have witnessed enabling technologies such as database systems, client-server computing and artificial intelligence reach industrial strength. These trends have spawned a new breed of systems that can support the extraction of useful information from large quantities of data. Understanding the power and limitations of these emerging technologies can provide managers and information systems professionals new approaches to support the task of solving hard business problems. This course will provide an overview of these techniques (such as genetic algorithms, neural networks, and decision trees) and discuss applications such as fraud detection, customer segmentation, trading, marketing strategies and customer support via cases and real datasets.

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).

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.

Fundamentals of modern decision analysis with emphasis on managerial decision making under uncertainty and risk. The basic topics of decision analysis are examined. These include payoffs and losses, utility and subjective probability, the value of information, Bayesian analysis, inference and decision making. Examples are presented to illustrate the ideas and methods. Some of these involve: choices among investment alternatives; marketing a new product; health care decisions; and costs, benefits, and sample size in surveys.

An introduction to the use of statistical methods in the increasingly important scientific areas of genomics and bioinformatics. The topics to be covered will be decided in detail after the initial class meeting, but will be taken from the following: background probability theory of one and many random variables and of events; background statistical inference theory, classical and Bayesian; Poisson processes and Markov chain; the analysis of one and many DNA sequences, in particular shotgun sequencing, pattern analysis and motifs; substitution matrices, general random walk theory, advanced statistical inference, the theory of BLAST, hidden Markov models, microarray analysis, evolutionary models.

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 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 will expose students to the theoretical and empirical “building blocks” that will allow them to construct, estimate, and interpret powerful models of customer 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.

The research seminars for Wharton Research Scholars and Joseph Wharton Scholars are designed to promote skills that will assist students in both understanding and performing analytical research. This will be accomplished over two semesters, in which students will:

• Participate in the fall seminar series featuring top University of Pennsylvania faculty who will present professional journal articles, books, and research in progress. Students will be introduced to the wide variety of research topics and methodologies employed by Penn faculty.
• Attend one or more faculty research presentation(s).
• Frame projects in the fall to be formally proposed in November, and then perform, write, and present their research projects in the spring.