Gregory Ridgeway

Gregory Ridgeway
  • Professor of Criminology
  • Professor of Statistics and Data Science

Contact Information

  • office Address:

    483 McNeil Building, 3718 Locust Walk, Philadelphia, PA 19104

Teaching

Past Courses

  • BSTA6990 - Lab Rotation

    Student lab rotation.

  • CRIM1100 - Criminal Justice

    This course examines how the criminal justice system responds to crime in society. The course reviews the historical development of criminal justice agencies in the United States and Europe and the available scientific evidence on the effect these agencies have on controlling crime. The course places an emphasis on the functional creation of criminal justice agencies and the discretionary role decision makers in these agencies have in deciding how to enforce criminal laws and whom to punish. Evidence on how society measures crime and the role that each major criminal justice agency plays in controlling crime is examined from the perspective of crime victims, police, prosecutors, jurors, judges, prison officials, probation officers and parole board members. Using the model of social policy evaluation, the course asks students to consider how the results of criminal justice could be more effectively delivered to reduce the social and economic costs of crime.

  • CRIM1200 - Stat for the Social Sci.

    Statistical techniques and quantitative reasoning are essential tools for properly examing questions in the social sciences. This course introduces students to the concepts of probability, estimation, confidence intervals, and how to use the statistical concepts and methods to answer social science questions. The course will require the use of R, a free, open source statistical analysis program. This course has been approved for the quantitative data analysis requirement (QDA).

  • CRIM4001 - Senior Research Thesis

    Senior Research Thesis is for senior Criminology majors only. Students are assigned advisors with assistance from the Undergraduate Chair.

  • CRIM4002 - Data Analytics in R

    This course covers the tools and techniques to acquire, organize, link and visualize complex data in order to answer questions about crime and the criminal justice system. The course is organized around key questions about police shootings, victimization rates, identifying crime hotspots, calculating the cost of crime, and finding out what happens to crime when it rains. On the way to answer these questions, the course will cover topics including data sources, basic programming techniques, SQL, regular expressions, webscraping, and working with geographic data. The course will use R, an open-source, object oriented scripting language with a large set of available add-on packages.

  • CRIM4012 - Mach Learn for Social Science

    This course provides an introduction to machine learning techniques for social science researchers. The course will cover a range of techniques including supervised and unsupervised learning, as well as more specialized methods such as deep learning and natural language processing. The course will also discuss ethical and privacy considerations in the use of machine learning, as well as the role of machine learning in policy and decision-making. The aim of the course is to be focused on applications. While the class will present the formal background on the development of the machine learning methods, the class will focus on putting the tools into practice. We will use data on a variety of topics including criminal justice data (recidivism prediction) as well as other social science disciplines. Students completing the course will know how to apply several of the most common machine learning tools to a variety of social science problems including prediction and clustering. The course will also discuss the role of machine learning in causal inference.

  • CRIM6002 - Data Analytics in R

    This course covers the tools and techniques to acquire, organize, link and visualize complex data in order to answer questions about crime and the criminal justice system. The course is organized around key questions about police shootings, victimization rates, identifying crime hotspots, calculating the cost of crime, and finding out what happens to crime when it rains. On the way to answer these questions, the course will cover topics including data sources, basic programming techniques, SQL, regular expressions, webscraping, and working with geographic data. The course will use R, an open-source, object oriented scripting language with a large set of available add-on packages.

  • CRIM6003 - Res Meth/Crime Analysis

    This course provides an overview of the application of social science research methods and data analysis to criminology. Students will learn research design principles and statistical techniques for the analysis of social science data, including how to interpret results as part of the rigorous practice of evidence-based criminology. M.S. students will conduct a semester-long, data-intensive crime analysis project using quantitative methods to address a specific research question. Student projects culminate with a poster presentation, an oral defense, and the submission of a written research paper.

  • CRIM6004A - Criminology in Practice

    In this capstone course, students will meet weekly with guests who work on or close to the front line of the criminal justice system. Past guests have included police chiefs, forensic scientists, lobbyists for gun rights and lobbyist for gun control, formerly incarcerated individuals, crime analysts, directors of sentencing commissions, prosecutors and defenders, politicians, and researchers at research organizations working closely with criminal justice agencies. Guests share their career paths, the roles of their organizations in the justice system, and key justice system challenges. Students interact with all guest speakers.

  • CRIM6004B - Criminology in Practice

    In this capstone course, students will meet weekly with guests who work on or close to the front line of the criminal justice system. Past guests have included police chiefs, forensic scientists, lobbyists for gun rights and lobbyist for gun control, formerly incarcerated individuals, crime analysts, directors of sentencing commissions, prosecutors and defenders, politicians, and researchers at research organizations working closely with criminal justice agencies. Guests share their career paths, the roles of their organizations in the justice system, and key justice system challenges. Students interact with all guest speakers.

  • CRIM6012 - Mach Learn for Social Science

    This course provides an introduction to machine learning techniques for social science researchers. The course will cover a range of techniques including supervised and unsupervised learning, as well as more specialized methods such as deep learning and natural language processing. The course will also discuss ethical and privacy considerations in the use of machine learning, as well as the role of machine learning in policy and decision-making. The aim of the course is to be focused on applications. While the class will present the formal background on the development of the machine learning methods, the class will focus on putting the tools into practice. We will use data on a variety of topics including criminal justice data (recidivism prediction) as well as other social science disciplines. Students completing the course will know how to apply several of the most common machine learning tools to a variety of social science problems including prediction and clustering. The course will also discuss the role of machine learning in causal inference.

  • CRIM9999 - Independent Study

    Primarily for graduate students who work with individual faculty upon permission. Intended to go beyond existing graduate courses in the study of specific problems or theories or to provide work opportunities in areas not covered by existing courses.

  • SOCI2921 - Criminal Justice

    This course examines how the criminal justice system responds to crime in society. The course reviews the historical development of criminal justice agencies in the United States and Europe and the available scientific evidence on the effect these agencies have on controlling crime. The course places an emphasis on the functional creation of criminal justice agencies and the discretionary role decision makers in these agencies have in deciding how to enforce criminal laws and whom to punish. Evidence on how society measures crime and the role that each major criminal justice agency plays in controlling crime is examined from the perspective of crime victims, police, prosecutors, jurors, judges, prison officials, probation officers and parole board members. Using the model of social policy evaluation, the course asks students to consider how the results of criminal justice could be more effectively delivered to reduce the social and economic costs of crime.

  • SOCI3501 - Mach Learn for Social Science

    This course provides an introduction to machine learning techniques for social science researchers. The course will cover a range of techniques including supervised and unsupervised learning, as well as more specialized methods such as deep learning and natural language processing. The course will also discuss ethical and privacy considerations in the use of machine learning, as well as the role of machine learning in policy and decision-making. The aim of the course is to be focused on applications. While the class will present the formal background on the development of the machine learning methods, the class will focus on putting the tools into practice. We will use data on a variety of topics including criminal justice data (recidivism prediction) as well as other social science disciplines. Students completing the course will know how to apply several of the most common machine learning tools to a variety of social science problems including prediction and clustering. The course will also discuss the role of machine learning in causal inference.

  • SOCI6002 - Data Analytics in R

    This course covers the tools and techniques to acquire, organize, link and visualize complex data in order to answer questions about crime and the criminal justice system. The course is organized around key questions about police shootings, victimization rates, identifying crime hotspots, calculating the cost of crime, and finding out what happens to crime when it rains. On the way to answer these questions, the course will cover topics including data sources, basic programming techniques, SQL, regular expressions, webscraping, and working with geographic data. The course will use R, an open-source, object oriented scripting language with a large set of available add-on packages.

  • SOCI6012 - Mach Learn for Social Science

    This course provides an introduction to machine learning techniques for social science researchers. The course will cover a range of techniques including supervised and unsupervised learning, as well as more specialized methods such as deep learning and natural language processing. The course will also discuss ethical and privacy considerations in the use of machine learning, as well as the role of machine learning in policy and decision-making. The aim of the course is to be focused on applications. While the class will present the formal background on the development of the machine learning methods, the class will focus on putting the tools into practice. We will use data on a variety of topics including criminal justice data (recidivism prediction) as well as other social science disciplines. Students completing the course will know how to apply several of the most common machine learning tools to a variety of social science problems including prediction and clustering. The course will also discuss the role of machine learning in causal inference.

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