Criminal Justice MS - Curriculum

This course engages students in research that builds on problem identification and analysis in criminal justice. Topics may include exploration of community issues and views on justice, health and medical approaches to crime problems, practitioner orientations and practices relative to specific issues in criminal justice, and applications of new techniques, such as data science, to contemporary problems. Students will apply theoretical frameworks towards understanding the scope and nature of the problem in the form of working papers. The class culminates in a final project which will involve analysis and evaluation research components addressing the underlying problem/issue/program in consultation with the instructor. (Restricted to students in CRIM-BS/MS or CRIM-MS programs.) Lecture 3 (Fall).

This course provides an examination of current issues in criminal justice with an emphasis on the application of evaluation, management, theory and ethics to analysis of criminal justice policy, The goal is to engage students in discussion of current issues with their peers and with experts in the field. (CRIM-MS) Seminar 3 (Fall).

This course will be tailored to individual students’ research interests as they explore areas of inquiry that may become topics for their thesis research. An emphasis will be placed on building a theoretically informed research question via existing literature and research in criminal justice and other disciplines (economics, psychology, sociology, and so on). Parallel to that effort, students will work to identify locally relevant research questions, potential research designs, and possible projects and/or agencies with whom which to conduct this research. (CRIM-MS) Seminar 3 (Fall).

This course is designed to analyze and critique the mainstream media’s coverage of criminal justice issues, and to study how that coverage impacts society at large. The course will scrutinize, compare and contrast crime coverage of different eras, and will also discuss how that coverage is changing today with around-the-clock media outlets and ubiquitous social media. Among the issues studied will be the impact of crime coverage on public policy; the impact of televised trials; the editorial decisions made daily in newsrooms across America about the placement and priority of crime news; the trademarks that can catapult a crime story into local, regional or even national prominence; and the occasional alliances between law enforcement and media. (CRIM-MS) Seminar 3 (Biannual).

This course focuses on social media research and ethics of applying various methodological approaches to study public data, users and messages. Students will be introduced to a variety of techniques and concepts used to obtain, monitor and evaluate social media content with a focus on how the analytics could inform communication strategies. During the course, students will also learn how to design and evaluate social media-based research studies. Lec/Lab 3 (Fall or Spring).

Communication has been impacted by automation and advances in information technology, and now artificial intelligence is changing how we interact with socio-technical systems. In this course, we will explore historical, ethical, computational, and cultural perspectives to understand the implications of algorithmic processes on communication and society. During the course, students will learn how to analyze various digital products and identify the potential consequences of algorithmic systems on various demographics. Lecture 3 (Fall or Spring).

This course will introduce students to the spatial data science life cycle, which provides location- specific algorithms and analytical methods to solve big spatial data problems. This course Students are provided with s a hands-on experience in capturing, engineering, visualizing, analyzing, and sharing results of spatial data science workflows. Lastly, the course will provide students with a background in core spatial data science methods and theories, including Geographic Information Systems (GIS), spatial analysis, geographic visualization cartography, and web mapping. Lec/Lab 3 (Fall).

This online course introduces students to the world of Geographic Information Systems (GIS). Course lectures, reading assignments, and practical lab experiences will cover a mix of conceptual, practical and technical GIS topics. Topics include GIS data models, basic cartography, geodatabases, spatial analysis, GIS software, and theory and concepts from the Geographic Information Science and Technology domain. Lec/Lab 3 (Fall).

This course examines concepts and techniques associated with dynamic map construction, usage, and assessment. Specific topics include thematic cartography, geographic information visualization, sources of dynamic geographic information, developing animated and interactive maps, mapping mashup development, using maps as a means to support group work, usability of dynamic maps, and current geovisualization research areas. Development of a visualization prototype and an associated scholarly paper in an area related to thematic cartography and geographic visualization are required. Lec/Lab 3 (Spring).

This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments. Lecture 3 (Fall).

This course discusses issues associated with data capture, organization, storage, extraction, and modeling for planned and ad hoc reporting. Enables student to model data by developing conceptual and semantic data models. Techniques taught for managing the design and development of large database systems including logical data models, concurrent processing, data distributions, database administration, data warehousing, data cleansing, and data mining. Lecture 3 (Spring).

This course explores topics related to understanding individuals in a social context. Topics may include, but are not limited to: Social Perception and Social Cognition; Attitudes; Social Identity; Prejudice and Discrimination; Interpersonal Attraction; Close Relationships; Social Influence; Prosocial Behavior; Aggression; Group Behavior; Artifacts and Methodological Issues in Social Psychology. Course format is seminar focused on reading assigned texts each week, writing reaction papers, and participating in discussion. Students will also conduct a study on the topic of their choice and present their findings both in an oral and written format. Seminar (Biannual).

This course introduces students to more advanced inferential parametric and non-parametric data-analysis techniques commonly used in psychological research, but not covered (or not covered in depth) in the Graduate Statistics course. These techniques may include, but are not limited to: Reliability Analysis, Multiple Regression, Discriminant Analysis, Logistic Regression, Factor Analysis, Analysis of Covariance, Multivariate Analysis of Variance, Contrast Analysis, Mediator and Moderator Variable Analysis, Non-Parametric Tests, and Multi-level Modeling. The focus is on the conceptual understanding of these statistics, how different statistical procedures are applied in different research methods, how to perform analyses, how to interpret the results in the context of the research question, and how to communicate these results. (Prerequisites: PSYC-640 or equivalent course.) Lecture 3 (Biannual).

Technological innovation, the incremental and revolutionary improvements in technology, has been a major driver in economic, social, military, and political change. This course will introduce generic models of innovation that span multiple sectors including: energy, environment, health, and bio- and information-technologies. The course will then analyze how governments choose policies, such as patents, to spur and shape innovation and its impacts on the economy and society. Students will be introduced to a global perspective on innovation policy including economic competitiveness, technology transfer and appropriate technology. Lecture 3 (Spring).

An in-depth inquiry into key contemporary public policy issues. Students will be exposed to a wide range of important public policy texts, and will learn how to write a literature review in a policy area of their choosing. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Seminar (Fall).

This course provides graduate students with necessary tools to help them become effective policy analysts. The course places particular emphasis on understanding the policy process, the different approaches to policy analysis, and the application of quantitative and qualitative methods for evaluating public policies. Students will apply these tools to contemporary public policy decision making at the local, state, federal, and international levels. Lecture 3 (Fall).

This course provides students with an introduction to decision science and analysis. The course focuses on several important tools for making good decisions, including decision trees, including forecasting, risk analysis, and multi-attribute decision making. Students will apply these tools to contemporary public policy decision making at the local, state, federal, and international levels. Lecture 3 (Spring).

This course provides an in-depth look at key concepts in public management and governance. Starting with the basic structure of the U.S. Constitution, the course examines how the tensions facing public administrators and officials have changed over time in both public organizational theory and practice. Topics include public institution and organization theory, public budgeting, citizen engagement, e-government, public-private partnerships, collaboration and governance, and recent innovations in management practice. Class will include guest lectures from current public administrators. Lecture 3 (Spring).

The concept of sustainability has driven many national and international policies. More recently, we have become aware that unless we physically build and rebuild our communities in ways that contribute to sustainability, making progress toward that goal is unlikely. It is equally important to recognize the social/cultural context of sustainability. In addition, it is at the local level that the goals of equity (a key consideration in community sustainability), most often achieved through citizen participation and collaborative processes are most easily realized. This course will broaden students understanding of the concept of sustainability, particularly the concept of social sustainability. This course focuses on sustainability as a way to bring light to the connections between natural and human communities, between nature and culture, and among environmental, economic, and social systems. Working closely with local organizations, students will explore the applicability of theoretical concepts. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall).

This course will introduce students to the spatial data science life cycle, which provides location- specific algorithms and analytical methods to solve big spatial data problems. This course Students are provided with s a hands-on experience in capturing, engineering, visualizing, analyzing, and sharing results of spatial data science workflows. Lastly, the course will provide students with a background in core spatial data science methods and theories, including Geographic Information Systems (GIS), spatial analysis, geographic visualization cartography, and web mapping. Lec/Lab 3 (Fall).

This online course introduces students to the world of Geographic Information Systems (GIS). Course lectures, reading assignments, and practical lab experiences will cover a mix of conceptual, practical and technical GIS topics. Topics include GIS data models, basic cartography, geodatabases, spatial analysis, GIS software, and theory and concepts from the Geographic Information Science and Technology domain. Lec/Lab 3 (Fall).

This course examines concepts and techniques associated with dynamic map construction, usage, and assessment. Specific topics include thematic cartography, geographic information visualization, sources of dynamic geographic information, developing animated and interactive maps, mapping mashup development, using maps as a means to support group work, usability of dynamic maps, and current geovisualization research areas. Development of a visualization prototype and an associated scholarly paper in an area related to thematic cartography and geographic visualization are required. Lec/Lab 3 (Spring).

Communication has been impacted by automation and advances in information technology, and now artificial intelligence is changing how we interact with socio-technical systems. In this course, we will explore historical, ethical, computational, and cultural perspectives to understand the implications of algorithmic processes on communication and society. During the course, students will learn how to analyze various digital products and identify the potential consequences of algorithmic systems on various demographics. Lecture 3 (Fall or Spring).

Artificial intelligence (AI) presents many complex issues for society, as technological developments have greatly outpaced public policy. Moreover, the open and commercialized nature of AI tools provides criminals and other adversarial actors with new advantages yet to be effectively countered. This class looks at the legal and policy frameworks and practices needed to build an ecosystem of privacy, security, and trust that will help ensure stakeholders that AI is being developed and deployed in an ethical, safe, and reliable manner. The class will also discuss how organizations are designing their own practices for operationalizing trustworthy or ethical AI in various sectors including law enforcement and criminal justice, commercial sectors, medical and biological research, among others. Students will be given a foundation in the emerging laws, regulations, and policies regarding AI, as well as insight on the broader process of how laws and policies need to adapt for other rapidly emerging technologies. We will explore in detail several approaches currently being considered, including regulatory approaches, standards, and considerations for national and international security. The course also will explore certain other legal issues arising in connection with AI, such antitrust and competition law, intellectual property and proprietary rights matters, and concerns for future technologies (quantum computing, AI and synthetic biology, etc.). Lecture 3 (Fall, Spring).

Technological innovation, the incremental and revolutionary improvements in technology, has been a major driver in economic, social, military, and political change. This course will introduce generic models of innovation that span multiple sectors including: energy, environment, health, and bio- and information-technologies. The course will then analyze how governments choose policies, such as patents, to spur and shape innovation and its impacts on the economy and society. Students will be introduced to a global perspective on innovation policy including economic competitiveness, technology transfer and appropriate technology. Lecture 3 (Spring).

Allows examination of a special problem or topical area in the field of public policy at the graduate level. Topics and specific content and methods vary from year to year or semester to semester. Lecture 3 (Fall, Spring).

An in-depth inquiry into key contemporary public policy issues. Students will be exposed to a wide range of important public policy texts, and will learn how to write a literature review in a policy area of their choosing. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Seminar (Fall).

This course provides graduate students with necessary tools to help them become effective policy analysts. The course places particular emphasis on understanding the policy process, the different approaches to policy analysis, and the application of quantitative and qualitative methods for evaluating public policies. Students will apply these tools to contemporary public policy decision making at the local, state, federal, and international levels. Lecture 3 (Fall).

This course provides students with an introduction to decision science and analysis. The course focuses on several important tools for making good decisions, including decision trees, including forecasting, risk analysis, and multi-attribute decision making. Students will apply these tools to contemporary public policy decision making at the local, state, federal, and international levels. Lecture 3 (Spring).

This course provides an in-depth look at key concepts in public management and governance. Starting with the basic structure of the U.S. Constitution, the course examines how the tensions facing public administrators and officials have changed over time in both public organizational theory and practice. Topics include public institution and organization theory, public budgeting, citizen engagement, e-government, public-private partnerships, collaboration and governance, and recent innovations in management practice. Class will include guest lectures from current public administrators. Lecture 3 (Spring).