3730 Walnut Street
558 Jon M. Huntsman Hall
Philadelphia, PA 19104
Prasanna (Sonny) Tambe is an Associate Professor of Operations, Information and Decisions at the Wharton School at the University of Pennsylvania. His research focuses on the economics of technology and labor. Some recent research projects focus on 1) understanding the labor market for AI skills and 2) how workers choose to specialize in technologies. He is also interested in the use of data science tools for HR analytics.
Much of this research has uses Internet-scale data sources to measure skills and labor market activity at novel levels of granularity. His published papers have analyzed data from online job sites and other labor market intermediaries that generate databases of fine-grained information on workers’ skills and career paths or on employers’ job requirements. He is a co-author of “The Talent Equation: Big Data Lessons for Navigating the Skills Gap and Building a Competitive Workforce,” published by McGraw Hill in 2013.
His research has been published or is forthcoming in a number of academic journals including Management Science, Information Systems Research, MIS Quarterly, The Review of Financial Studies, Industrial and Labor Relations Review, Communications of the ACM, and Information Economics and Policy and it has been supported by the Alfred P. Sloan Foundation. His research has also won a number of awards, including the Best Published Paper in Information Systems Research and two papers have been nominees for the Best Published IS Paper in Management Science. He currently serves on the editorial board of Management Science and in the past, has served on the editorial board of Information Systems Research.
Professor Tambe received his S.B. and M.Eng. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and his Ph.D. in Managerial Science and Applied Economics from the Wharton School of the University of Pennsylvania.
Individual study and research under the direction of a member of the Economics Department faculty. At a minimum, the student must write a major paper summarizing, unifying, and interpreting the results of the study. This is a one semester, one c.u. course. Please see the department for permission.
Over the past decade, there has been a dramatic rise in the use of technology skills and data analytic thinking to solve business problems in many domains, including finance, HR, policy, transport, and strategy. As a result, the modern "analytic leader" increasingly requires the use of technology, statistics, and data skills to facilitate business analysis. This includes knowing how to effectively frame data-driven questions and use a new generation of technology tools that are becoming available to acquire, analyze, interpret, and communicate insights derived from data. Students in this hands-on course will engage with weekly labs that introduce them to new technologies, techniques, and data-driven business challenges.
Students who take this course will engage with the world of data science using tools such as Tableau and R that are becoming increasingly popular in industry. The first half of the course is designed for students with limited experience with data projects, and while familiarity with R, via courses such as STAT 405 or STAT 470, will be ideal preparation, students with other programming exposure can pick up the required skills via review sessions and self-instruction. The second half of the course extends students' experience to industry applications of text mining and machine learning and requires students to work with more unstructured data. Each week of the course will be devoted to analysis of a data set from a particular industry (e.g. HR, sports, fashion, real estate, music, education, politics, restaurants, non-profit work), which we will use to answer business questions by applying analytic techniques. The course is very hands-on, and students will be expected to become proficient at applying data to business decisions and at effectively analyzing large data sets to inform decisions about business problems.
This course number is currently used for several course types including independent studies, experimental courses and Management & Technology Freshman Seminar. Instructor permission required to enroll in any independent study. Wharton Undergraduate students must also receive approval from the Undergraduate Division to register for independent studies. Section 002 is the Management and Technology Freshman Seminar; instruction permission is not required for this section and is only open to M&T students. For Fall 2020, Section 004 is a new course titled AI, Business, and Society. The course provides a overview of AI and its role in business transformation. The purpose of this course is to improve understanding of AI, discuss the many ways in which AI is being used in the industry, and provide a strategic framework for how to bring AI to the center of digital transformation efforts. In terms of AI overview, we will go over a brief technical overview for students who are not actively immersed in AI (topic covered include Big Data, data warehousing, data-mining, different forms of machine learning, etc). In terms of business applications, we will consider applications of AI in media, Finance, retail, and other industries. Finally, we will consider how AI can be used as a source of competitive advantage. We will conclude with a discussion of ethical challenges and a governance framework for AI. No prior technical background is assumed but some interest in (and exposure to) technology is helpful. Every effort is made to build most of the lectures from the basics.
WIEP features short-term courses that focus on various industries and feature visits to businesses, lectures, extracurricular activities, and networking opportunities with alumni. Students must apply online: https://undergrad-inside.wharton.upenn.edu/wiep/
New research from Wharton's Peter Cappelli and Prasanna Tambe points out the promise and pitfalls of using artificial intelligence in human resources, where algorithms can’t easily replace human decision-making.Knowledge @ Wharton - 2019/08/30