Data and statistics are fundamental to understanding and addressing crime in the world. Without these tools, we would struggle to answer basic questions related to how much crime occurs, what types of crime occur most frequently, where crime occurs most frequently, etc. Since data is central to how agencies respond to crime and related social issues, it is crucial for anyone who hopes to work in or around the criminal justice system to be knowledgeable about data analytic techniques commonly used in criminology and crime science. This course 1.) Examines a range of computational social science techniques that serve to address crime and criminal justice system problems; 2.)Explores existing databases that may inform questions about crime and criminal justice; 3.)Introduces students to different ways to display or visualize quantitative data; and 4.) Offers students an opportunity to learn how to produce and consume quantitative information
Criminology, as a discipline, attempts to understand what causes crime to occur and how society responds to crime. To do so, criminologists conduct research studies using a wide variety of methods from data-driven statistics to interviews and focus groups. Based on such work, criminologists have made different arguments regarding the factors that drive crime as well as the factors that drive the nature of crime responses. This course will provide an overview of these different perspectives, generally focusing on those which have received the most attention within the field. This course 1.) Outlines the strategies that are utilized to ask and answer criminological questions; 2.) Discusses theories of crime and delinquency; and 3.) Examines how a criminological perspective can help us analyze and interpret contemporary issues and phenomena that relate to crime and the criminal justice system.
Public safety is one of the most fundamental ways of thinking about the nature of a community. Colloquially, we contrast “good” or “safe” communities against “tough” or “dangerous” ones. Policymakers are tasked with providing the services that best mitigate and respond to crime. And researchers seek to better understand the causes and consequences of crime in communities. In addition to a traditional curriculum of reading and discussion on communities and crime, this course will feature a few distinctive elements: (1) highlighting the complementary roles of “communities” and the “places” therein (i.e., individual properties) in shaping crime patterns, which is a popular contemporary debate in the field; (2) a series of community experience assignments that invite students to apply the concepts discussed in class to real-world communities; and (3) engagement with a variety of methodological tools used by researchers, policymakers, and practitioners alike to better understand community dynamics.
This class uses Boston and the surrounding region as a case study for integrating digitalscholarship and computational methods with the social sciences and humanities toprovide new insights into major cultural, historical, and societal questions as they relateto and extend beyond the city of Boston. Through lectures, discussions, and activities,the course aims to understand a sampling of Boston’s geographic, historical, political,civic, and institutional landscapes. Boston’s history is long and complex, and we will befocusing on particular moments and case studies that complement thinking about digitalscholarship and computational methods. his class is split into two halves. The first part presents episodes or case studies abouthistorical Boston and greater Massachusetts. This will provide some context forunderstanding how we came to be who we are today. The second part allows for close,in-depth analysis of the modern city. Both parts will involve using Python and other computational or digital scholarship tools to make sense of historical and current trends.
I have developed a curriculum that teaches statistics to undergraduate students through Python. The philosphy of this curriculum is to minimize the most frustrating parts of coding (installations, servers, terminals, etc.) while teaching students how to answer social science research questions using statistics, computational tools, and real data.
All tutorials have been published below using the browser-based Google Colab platform. I have also included links to download data used in all exercises - I encourage you to follow along!