Prasanna Tambe

Prasanna Tambe
  • Associate Professor of Operations, Information and Decisions

Contact Information

  • office Address:

    3730 Walnut Street
    558 Jon M. Huntsman Hall
    Philadelphia, PA 19104

Research Interests: Economics of IT Labor, Technological Change and Labor Markets

Links: CV, Personal Website

Overview

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 labor markets for high-tech workers. Some recent research projects focus on understanding how leading firms attract high-tech talent, why markets in different cities differ in the technical skills available to employers, and how the spread of new technologies impacts career paths.

Much of this research has uses Internet data sources to measure skills and labor market activity at novel levels of granularity. His published papers have analyzed data from online job sites, data aggregators, and other labor market intermediaries that generate large 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 boards of Management Science and 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.

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Teaching

Current Courses

  • OIDD215 - Intro To Analyt & D Econ

    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.

    OIDD215001 ( Syllabus )

    OIDD215003 ( Syllabus )

  • OIDD245 - Advanced Analyt & D Econ

    This course is meant as a follow-on to OIDD 215 (or with instructor permission). The goal of this segment is to further immerse students in the world of data science projects. Specifically, we focus on working with large, unstructured data sources and gain experience with introductory machine learning concepts. Students who take this segment of the course will spend time inside and outside of the classroom combining data and code to develop data products for a number of new industries, including finance, the restaurant industry, and health care. At the end of the course, students will be expected to complete an advanced data project, which involves acquiring data from an online web property (e.g. Uber, Facebook) through an API and developing an interactive data visualization. Students who complete this course should have the necessary tools to begin building a portfolio of data science projects that they can share online through platforms such as GitHub or with future employers.

    OIDD245002 ( Syllabus )

Past Courses

  • OIDD215 - INTRO TO ANALYT & D ECON

    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.

  • OIDD245 - ADVANCED ANALYT & D ECON

    This course is meant as a follow-on to OIDD 215 (or with instructor permission). The goal of this segment is to further immerse students in the world of data science projects. Specifically, we focus on working with large, unstructured data sources and gain experience with introductory machine learning concepts. Students who take this segment of the course will spend time inside and outside of the classroom combining data and code to develop data products for a number of new industries, including finance, the restaurant industry, and health care. At the end of the course, students will be expected to complete an advanced data project, which involves acquiring data from an online web property (e.g. Uber, Facebook) through an API and developing an interactive data visualization. Students who complete this course should have the necessary tools to begin building a portfolio of data science projects that they can share online through platforms such as GitHub or with future employers.

Awards and Honors

  • Management Science Best Paper Published in Information Systems, 2016
  • Information Systems Research, Best Associate Editor, 2017
  • ISS Sandra A. Slaughter Early Career Award, 2016

In the News

Activity

In the News

After Equifax, Can Our Data Ever Be Safe?
Knowledge@Wharton - 09/19/2017
All News

Awards and Honors

Management Science Best Paper Published in Information Systems 2017
All Awards