Criminal Justice concentration in Crime Analysis, Master’s Degree

This course explores potential answers to complex and important questions about criminal behavior by drawing on the social science of criminology. Criminology is the interdisciplinary study of the development of law, criminal phenomena and societal responses to crime. The course has two primary focuses: 1) to explore and evaluate major explanations or theories of crime and 2) understand and evaluate the policy implications of major crime problems. Because criminology is interdisciplinary, students will examine theories that are grounded in a range of academic perspectives, including sociological, biological, political, psychological and economic explanations for crime. These theories will be centered on important public policy debates about a host of contemporary problems, including: firearm violence, high post- incarceration recidivism, opioid use disorder crisis and human trafficking. Course lectures and discussions focus on the historical development of the theories, their major assumptions and propositions, their relevance for public policy and practice. As the course progresses through each explanation for crime, students will have the opportunity to critically evaluate the validity of different explanations for crime as well as criminal justice policies and practices that they support. [4 credits]

This course will introduce students to the concepts of criminal justice administration and ethics. Students will learn about: the management of justice organizations in the United States, and the various debates as to how best to carry out crime control. Topics covered include: organizational theory and structure, professional ethics, leadership and management styles, organizational deviance and socialization, employee motivation, and management responses to stress and burnout. The course is designed to help students understand the characteristics of effective leadership and policy implementation in the field of criminal justice. Students will also have the opportunity to analyze ethical dilemmas commonly confronted in criminal justice work. [4 credits]

The skills used in research method have become central to many positions within criminal justice administration and related areas. Not only are professionals required to understand and critically evaluate the program and policy science in their field to make decisions, they often employ methodological skills in program and policy development, implementation, management and assessment. This course takes students through the research process from question development to administration and reporting. It emphasizes applied research situations and settings and specifically program evaluation and assessment. After examining various research design models, the course focuses on specific techniques that inform both quantitative and qualitative evaluation studies. These include sampling procedures, survey design, interview techniques, participant observation and case studies and process evaluations. [4 credits]

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 credits]

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 credits]

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 credits]

Cyber-terrorists continuously leverage sophisticated techniques in efforts to attack the nation's critical infrastructures by damaging their functionalities and stealing highly sensitive intellectual property, private information, and valuable assets. This course is designed to explore a new explanatory angle for studying cyberterrorism issues from a cyber- intelligence perspective. Major cyberterrorism cases will be empirically analyzed and applied into a lab environment for gaining hands-on-experience and to develop defensive strategies and counter measures. This course aims to examine three main types of cyberterrorism 1) Information Attacks, 2) Infrastructure Attacks, and 3) Technology Facilitation for building both technical capability and a set of policy recommendations to counter these potential threats. [4 credits]

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 credits]

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 credits]

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 credits]

Formerly titled CS 555 Data Analysis and Visualization with R.
This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize 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. Recommended Prerequisite: MET CS 544 or equivalent knowledge, or instructor's consent. [4 credits]

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. Prerequisite: MET CS 521 or equivalent. Or, instructor's consent. [4 credits]

Prerequisites: MET CS 521 & MET CS 546; 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. Students learn underlying theories of data mining algorithms in the class and they practice those algorithms through assignments and a semester-long class project using R. After finishing this course, students will be able to independently perform data mining tasks to solve real-world problems. [4 credits]