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Courses for Spring 2025

Title Instructor Location Time All taxonomy terms Description Section Description Cross Listings Fulfills Registration Notes Syllabus Syllabus URL Course Syllabus URL
CRIM 1100-401 Criminal Justice Dilara Bural MW 1:45 PM-3:14 PM 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. SOCI2921401 Society sector (all classes)
CRIM 2025-301 Transnational Crime Dilara Bural MW 5:15 PM-6:44 PM This course examines the complex and evolving landscape of transnational crime, which generates billions of dollars annually and operates across international borders. Far from the glamorized portrayals in popular media, the reality of transnational organized crime is rooted in fluid, global networks that thrive on illegal markets for drugs, firearms, human trafficking, counterfeit goods, and more. In this course students will explore the multifaceted nature of these crimes, their impact on global security, and their role in undermining economic, social, and political systems worldwide.
CRIM 2050-001 The Use and Impact of Technology in the Criminal Justice System Maria Cuellar TR 1:45 PM-3:14 PM This course explores the integration of technology in the criminal justice system, focusing on its impact on law enforcement, judicial processes, and corrections. Students will examine cutting-edge technological tools such as forensic analysis, AI algorithms, predictive policing, ShotSpotter, and facial recognition. The course will emphasize the ethical and fairness implications of these technologies, questioning their efficacy and the potential for bias. Through a combination of lectures, case studies, and hands-on projects, students will critically analyze the benefits and challenges associated with the adoption of technology in criminal justice.
CRIM 2080-401 Neighborhood Dynamics of Crime John M Macdonald MW 10:15 AM-11:44 AM Crime varies in time, space and populations as it reflects ecological structures and the routine social interactions that occur in daily life. Concentrations of crime can be found among locations, with antisocial activities like assaults and theft occurring at higher rates because of the demographic make-up of people (e.g. adolescents) or conflicts (e.g. competing gangs), for reasons examined by ecological criminology. Variation in socio-demographic structures (age, education ratios, and the concentration of poverty) and the physical environment (housing segregation, density of bars, street lighting) predicts variations between neighborhoods in the level of crime and disorder. Both ethnographic and quantitative research methods are used to explore the connections between the social and physical environment of areas and antisocial behavior. URBS2090401
CRIM 4012-401 Machine Learning for Social Science Greg Ridgeway MW 10:15 AM-11:44 AM 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. CRIM6012401, SOCI3501401, SOCI6012401 https://coursesintouch.apps.upenn.edu/cpr/jsp/fast.do?webService=syll&t=202510&c=CRIM4012401
CRIM 4013-401 Social Network Analysis David Kirk TR 10:15 AM-11:44 AM This course is an introduction to the theory and methods employed in social network analysis. Foundational to the study of social network analysis is the understanding that actors are interdependent, and that social structure emerges from regularities in this interdependence. Focus in social network analysis includes how networks form and evolve, and on how relationships affect the behavior and outcomes of actors in a network. Social network analysis also refers to the corresponding methods for investigating and measuring social structures and the dependencies between actors. This course primarily focuses on social network methods but will necessarily cover foundational concepts. Both descriptive and inferential approaches to social network analysis will be covered. The approach taken to the material will be a combination of lecture, labs, quizzes, and homework, with final application in the form of a research proposal/paper. Homework assignments will focus on the analysis and interpretation of a variety of network data sets. By the end of the class, each student should be familiar with the most commonly employed methods in social network analysis and should be competent at both applying and interpreting these methods for both exploratory analysis and theory testing. Students will also be exposed to the use of the R statistical computing system for network analysis and RStudio, yet familiarity with programming in R is not a pre-requisite. CRIM6013401
CRIM 4022-401 Juvenile Justice in the United States Charles E Loeffler R 8:30 AM-11:29 AM What should society do when children break the law? This seminar will explore the theory and practice of juvenile justice in the United States from the 18th century until the present. Using the lenses of legal history and policy evaluation we will explore the development and current state of juvenile justice policies, practices, and institutions. Students will be asked to assess the continuities and changes between different policy regimes as well as the risks and opportunities that they represent. Class visits by juvenile justice practitioners will provide another opportunity to learn about and explore these themes and topics. CRIM6022401 https://coursesintouch.apps.upenn.edu/cpr/jsp/fast.do?webService=syll&t=202510&c=CRIM4022401
CRIM 6001-301 Pro-Seminar in Criminal Justice John M Macdonald T 1:45 PM-4:44 PM This course provides and overview of what we know about the criminal justice system in the United States and other developed nations. The central purpose of the course is to increas your knowledge about how the U.S. criminal justice system works but we will also spend a great deal of time thinking about the quality of the available evidence and how we know what we know. Topics covered will vary from year to year; recent topics have included police use of force, capital punishment, pre-trial detention, the use of predictive algorithms in the criminal justice system and the relationship between immigration and crime in the United States.
CRIM 6003-301 Research Methods/Crime Analysis Aaron J Chalfin M 3:30 PM-6:29 PM 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.
CRIM 6012-401 Machine Learning for Social Science Greg Ridgeway MW 10:15 AM-11:44 AM 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. CRIM4012401, SOCI3501401, SOCI6012401 https://coursesintouch.apps.upenn.edu/cpr/jsp/fast.do?webService=syll&t=202510&c=CRIM6012401
CRIM 6013-401 Social Network Analysis David Kirk TR 10:15 AM-11:44 AM Social network analysis also refers to the corresponding methods for investigating and measuring social structures and the dependencies between actors. This course primarily focuses on social network methods but will necessarily cover foundational concepts. Both descriptive and inferential approaches to social network analysis will be covered. The approach taken to the material will be a combination of lecture, labs, quizzes, and homework, with final application in the form of a research proposal/paper. Homework assignments will focus on the analysis and interpretation of a variety of network data sets. By the end of the class, each student should be familiar with the most commonly employed methods in social network analysis and should be competent at both applying and interpreting these methods for both exploratory analysis and theory testing. Students will also be exposed to the use of the R statistical computing system for network analysis and RStudio, yet familiarity with programming in R is not a pre-requisite. CRIM4013401
CRIM 6022-401 Juvenile Justice in the United States Charles E Loeffler R 8:30 AM-11:29 AM What should society do when children break the law? This seminar will explore the theory and practice of juvenile justice in the United States from the 18th century until the present. Using the lenses of legal history and policy evaluation we will explore the development and current state of juvenile justice policies, practices, and institutions. Students will be asked to assess the continuities and changes between different policy regimes as well as the risks and opportunities that they represent. Class visits by juvenile justice practitioners will provide another opportunity to learn about and explore these themes and topics. CRIM4022401 https://coursesintouch.apps.upenn.edu/cpr/jsp/fast.do?webService=syll&t=202510&c=CRIM6022401
CRIM 7100-301 Advanced Pro-Seminar in Criminal Justice Charles E Loeffler T 5:15 PM-8:14 PM This second year doctoral course is a weekly discussion group designed to help students integrate their coursework from different disciplines around the behavior and operation of criminal law systems. It focuses on preparation for the doctoral comprehensive examination, detailed critiques of published and unpublished research reports, and colloquia by leading guest lecturers presenting new research results. Students preparing for dissertation research on the behavior of criminal law will report on their developing research ideas.