Dean Knox

Dean Knox
  • Assistant Professor of Operations, Information and Decisions
  • Assistant Professor of Statistics and Data Science

Contact Information

  • office Address:

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

Research Interests: Statistics, speech analysis, policing, ethnic politics

Links: Personal Website, CV

Overview

Dean Knox is a computational social scientist developing new methods for the study of complex and high-dimensional data. His research includes policing, speech analysis, ethnic politics, and political communication. His work has appeared or is forthcoming in Science, the Journal of the American Statistical Association, the Proceedings of the National Academy of Sciences, and the American Political Science Review. It has received the Gosnell Prize for excellence in political methodology, the John T. Williams dissertation prize, and the best poster award by the Society for Political Methodology. For details, see www.dcknox.com.

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Teaching

All Courses

  • OIDD4770 - Intro To Python Data Sci

    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.

  • OIDD7770 - Intro To Python Data Sci

    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.

  • STAT4770 - Intro To Python Data Sci

    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.

  • STAT7770 - Intro To Python Data Sci

    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.

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