Crime Analysis Graduate Certificate

Evidence-based and data-driven approaches to crime problems are the industry standard among criminal justice agencies and non-governmental organizations. This course will cover a variety of statistical "tools" from three broad areas: (1) descriptive statistics, (2) inferential statistics and hypothesis testing, and (3) measures of association. Students will learn how to develop research questions, describe and draw conclusions from quantitative data, and interpret statistical research findings, and be able to present these findings to a variety of audiences in a clear and accurate way -- to be able to "tell a story" with numbers. In addition, students will develop a proficiency working with large data sets and conducting analysis with a critical lens, using the analytical software -- Statistical Package for the Social Sciences (SPSS) -- commonly used in criminal justice and related fields.   [ 4 cr. ]

Contemporary law enforcement agencies regularly employ crime and intelligence analysis to develop and inform effective responses to crime. This course provides an in-depth examination of crime and intelligence analysis techniques. It also explores the role of the crime and intelligence analyst within law enforcement organizations and processes, the historical evolution of this approach, key legal and policy issues, and challenges to implementation. Students have the opportunity to apply these skills to case study simulations involving an array of common crime problems and cases using real-world examples and sources of information.  [ 4 cr. ]

Foundations of GIS and Spatial Analysis provides an introduction to Geographic Information Systems (GIS) specifically with a focus on applications in urban planning. The role of spatial analysis in local, state, and regional planning has steadily increased over the last decade with the infusion of windows-based GIS software such as ESRI ArcGIS. The class focus is to prepare students to feel comfortable communicating with other GIS users, research spatial data, and produce high-quality digital maps in an applied learning environment.  [ 4 cr. ]

Data analysis informs administration, management and accountability processes within criminal justice and related organizations. While traditional crime analysis often narrowly seeks to improve the organization's effectiveness towards public safety outcomes, management and accountability analysis seeks to also ensure fair, efficient, transparent, and accountable practices as well. This course examines contemporary management and accountability practices with an emphasis on the ways in which data can be employed to improve these practices. Students will learn skills to work with real data sources across justice-system domains, from policing to corrections, as well as community-based organizations. Contemporary challenges, such as disparate treatment, abuse of force, 'overpolicing,' frame discussions and assignments. Students will understand the strengths and limitations of data-informed approaches. The course is valuable to students seeking careers in analytical roles, other practitioners, non-profit managers, and those interested in justice reform more broadly.   [ 4 cr. ]

Threat assessment is a systematic strategy for identifying, assessing, and preventing potential violent incidents. Using an interdisciplinary approach combining insights from criminology, law enforcement, psychiatry, and psychology, behavioral threat assessment is designed to provide alternatives to violence before an attack occurs. In this class, students will learn the principles of behavioral threat assessment and explore ongoing developments in the field by reviewing case examples and studies of school shooters, mass murderers, lone actor terrorists, as well as more common offender types such as domestic violence offenders. The course is intended for a wide variety of law enforcement officers, mental health practitioners, school administrators, workplace managers, and public officials who are charged with maintaining community safety.  [ 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. ]

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

Prerequisites: MET CS 544 or MET CS 550 or consent of instructor. Learn the foundations of machine learning, regression, and classification. Topics include how to describe data, statistical inference, 1 and 2 sample tests of means and proportions, simple linear regression, multiple linear regression, multinomial regression, logistic regression, analysis of variance, and regression diagnostics. These topics are explored using the statistical package R, with a focus on understanding how to use these methods and interpret their outputs and how to visualize the results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results are discussed. Concepts are presented in context of real-world examples in order to help you learn when and how to deploy different methods.  [ 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. ]