MS in Criminal Justice, Crime Analysis Concentration

本课程通过借鉴犯罪学的社会科学，探索有关犯罪行为的复杂而重要问题的潜在答案。犯罪学是新濠影汇线上赌场研究法律发展、犯罪现象和社会对犯罪反应的跨学科学科。该课程有两个主要重点：1)探索和评估犯罪的主要解释或理论；2)理解和评估主要犯罪问题的政策影响。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. 课程讲授和讨论的重点是这些理论的历史发展，它们的主要假设和命题，以及它们与公共政策和实践的相关性。随着课程的发展，学生将有机会批判性地评估不同的犯罪解释的有效性，以及他们支持的刑事司法政策和实践。[4]

本课程将向学生介绍刑事司法管理和道德的概念。学生将学习：美国司法机构的管理，以及关于如何最好地实施犯罪控制的各种辩论。涵盖的主题包括：组织理论与结构，职业道德，领导与管理风格，组织偏差与社会化，员工动机，以及管理层对压力和倦怠的反应。本课程旨在帮助学生了解在刑事司法领域有效领导和政策执行的特点。学生还将有机会来分析道德困境中经常面临刑事司法工作。[4]

在新濠影汇线上赌场研究方法中使用的技能已经成为刑事司法管理和相关领域许多职位的核心。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. 本课程将带领学生完成从问题发展到管理和报告的新濠影汇线上赌场研究过程。它强调应用新濠影汇线上赌场研究的情况和设置，特别是项目评估和评估。在考察了各种新濠影汇线上赌场研究设计模型之后，本课程侧重于定量和定性评估新濠影汇线上赌场研究的具体技术。These include sampling procedures, survey design, interview techniques, participant observation and case studies and process evaluations. [4]

对犯罪问题采取循证和数据驱动的方法是刑事司法机构和非政府组织之间的行业标准。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]

Contemporary law enforcement agencies regularly employ crime and intelligence analysis to develop and inform effective responses to crime. 本课程提供对犯罪和情报分析技术的深入新濠影汇线上赌场研究。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. 学生有机会将这些技能应用于案例新濠影汇线上赌场研究模拟，涉及一系列常见的犯罪问题和案例，使用现实世界的例子和信息来源。[4]

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. ]

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 cr. ]

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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. ]

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 cr. ]

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 cr. ]

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 cr. ]

Studying Geospatial Intelligence (GEOINT) for Urban Analysis equips students with the analytical tools and methodologies to understand and solve complex urban challenges. This knowledge is crucial for making informed decisions related to urban planning, infrastructure development, disaster management, and sustainability efforts. Through practical applications and real-world case studies, the course prepares students to contribute effectively to the development of smarter, more resilient, sustainable, and equitable urban environments.  [ 4 cr. ]