Crime Analysis Graduate Certificate

Evidence-based and data-informed approaches are now central to modern criminal justice practice. How can data help you understand crime problems, evaluate programs, and support better policy decisions? Develop practical skills for analyzing and interpreting quantitative data used in justice policy and administration. You will learn how to frame research questions, summarize patterns in data, and draw meaningful conclusions from statistical evidence. The course introduces key analytical tools—including descriptive statistics, hypothesis testing, and measures of association—and demonstrates how they are used to analyze crime and justice data. Working with real datasets and justice-system contexts, you will build experience conducting analyses and communicating findings clearly to diverse audiences. By the end of the course, you will be able to interpret statistical evidence and “tell the story” behind the numbers.  [ 4 cr. ]

How do crime analysts turn complex data into insights that guide investigations and crime prevention strategies? Explore the analytical techniques used by contemporary law enforcement and public safety organizations to identify crime patterns, assess emerging problems, and inform operational decisions. You will examine the principles and practices of crime and intelligence analysis, including how analysts collect, interpret, and communicate information that supports investigative, tactical, and strategic decision-making. The course also considers the role of analysts within justice organizations and the legal and policy frameworks that shape their work. Through applied exercises and case-based simulations using real-world data sources, you will develop practical analytical skills used to address common crime problems and support effective public safety strategies.  [ 4 cr. ]

How can maps and spatial data reveal patterns in cities and communities that aren’t visible at street level? In this project-based course, you learn how Geographic Information Systems (GIS) help planners and urban professionals analyze places, understand patterns, and make better decisions. You’ll build practical skills in spatial thinking and spatial analysis while working with real geographic data. Through hands-on projects, you’ll learn how to collect, organize, and analyze spatial information, create maps that communicate clear insights, and evaluate the strengths and limits of different data sources. Along the way, you’ll explore how GIS is used to address real urban challenges—from understanding neighborhood change to informing planning and policy decisions. By the end of the course, you’ll be able to use GIS tools to analyze places, visualize patterns, and communicate spatial insights that support sustainable and more equitable communities.  [ 4 cr. ]

How can justice organizations use data to strengthen performance, accountability, and public trust? Explore how analytical tools support modern management and decision-making across policing, courts, corrections, and community-based organizations. You will examine contemporary performance management approaches and learn how data can be used to assess effectiveness, fairness, transparency, and responsiveness within justice systems. Working with real-world data sources, you will develop skills in identifying meaningful performance measures, organizing and analyzing datasets, and interpreting findings in organizational contexts. By the end of the course, you will be prepared to translate data into clear analytical products—such as reports and dashboards—that inform management decisions and support accountability to both agency leadership and community stakeholders.  [ 4 cr. ]

How can potential acts of violence be identified and prevented before they occur? Explore the principles and practices of behavioral threat assessment, a structured approach used to identify, evaluate, and manage individuals who may pose a risk of targeted violence. Drawing on insights from criminology, psychology, psychiatry, and law enforcement practice, you will examine how threat assessment teams analyze behavioral indicators, contextual factors, and warning signs associated with potential acts of violence. Through case studies and research on school shootings, targeted violence, domestic violence, and lone-actor terrorism, the course explores how professionals assess risk and develop prevention strategies. The course reflects contemporary practices used by multidisciplinary threat assessment teams working to prevent targeted violence in schools, workplaces, and communities.  [ 4 cr. ]

Students in the Crime Analysis (CA) concentration or certificate program are encouraged to engaged in a supervised project as part of their degree. The project is supervised by an instructor, typically the concentration area coordinator. It can be used to fulfill a 4-credit elective course within the concentration or certificate. Project-based and experiential learning activities are central to graduate professional education and to our own program's learning outcomes. The project gives students an opportunity to enhance and synthesize skills learned in other courses, apply skills to real-world problems and settings, and make connections into the professional field. Projects in the CA concentration or certificate will focus applied research and analytical methodologies and/or organizational processes connected to analysis within criminal justice or related agencies.  [ 4 cr. ]

Prerequisite: Programming experience in any language. Or Instructor's consent. Explore the object-oriented approach to software design and development using Python. You will engage in a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceed to more advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course, you will be able to apply software engineering principles to design and implement Python applications that can be used with analytics and big data. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Creativity/Innovation, Critical Thinking, Quantitative Reasoning 2.  [ 4 cr. ]

Prerequisites: MET CS 544 or MET CS 550 or consent of instructor. You will learn the foundations of statistical machine learning, regression, and classification, and explore the key components of statistical models, including how to construct, interpret, and evaluate them. Topics include data description and visualization, statistical inference, one- and two-sample tests for means and proportions, simple and multiple linear regression, multinomial and logistic regression, analysis of variance (ANOVA), and regression diagnostics. For each topic, you will examine the methodology, underlying assumptions, interpretation of results, and model assessment. The course includes a programming component using R or Python, providing hands-on experience that reinforces theoretical concepts. Methods are presented through real-world examples to help you understand when and how to apply different statistical techniques effectively.  [ 4 cr. ]

Prerequisite: (MET CS 521) or equivalent or instructor's consent. Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class.  [ 4 cr. ]

Prerequisites: MET CS 521, MET LB 103 and MET LB 104; and either MET CS 579 or MET CS 669; or consent of instructor. - Study basic concepts and techniques of data mining. Topics include data preparation, classification, performance evaluation, association rule mining, regression and clustering. You will learn underlying theories of data mining algorithms in the class and practice those algorithms through assignments and a semester-long class project using R. After finishing this course, you will be able to independently perform data mining tasks to solve real-world problems.  [ 4 cr. ]