Overview of nutritional needs of adults as it relates to obesity and weight management. Topics include weight inclusive practice in healthcare and diatetics, biology of obesity, multilevel interventions to manage weight, commercial and non-commercial weight loss programs, current fad diet trends, bariatric nutrition, and pharmacotherapy and surgical management of obesity.

Overview of the fundamental theories and processes of nutrition education for individuals and groups. Includes approaches to needs assessment; program planning and design, population-appropriate interventions, evaluation and assessment.

Action research in the field of nutrition/nutrition education is presented. Students develop and present an action research project utilizing skills and knowledge gained from all graduate courses. (Co-requisite NED 664)

This course validates BSN equivalent competencies in nursing leadership, research and community health as outlined in the Essentials of Baccalaureate Education for Professional Nursing Practice (2008). In this course the RN students (who do not have a BSN degree) present a completed professional portfolio to demonstrate knowledge foundational for MSN study. (Prerequisite: RN with a non-nursing baccalaureate degree).

Advanced nursing roles will be analyzed in terms of their major components: consultant, change agent, clinician, educator, mentor, researcher, leader and manager within the context of changing educational institutions and health care delivery system. The process of socialization into the advanced nursing role is explored, and strategies for effective role implementation and evaluation are discussed. Practical strategies for role transition and development will be reviewed.

Examines the development of the nurse practitioner role and competencies, engaging the student in the use of select theories around family dynamics, relationships, quality & safety, and leadership to successfully operationalize the role of the FNP. Students learn about information systems and technology and the application of those systems to improve quality, safety, outcomes, and efficiency in health care.

Examines the development of the nurse informaticist and competencies. This course focuses on the foundational knowledge for understanding and practicing nursing informatics in healthcare settings. Students are engaged in the use of selected theories and models that support the role of nursing informatics and the application of nursing information technology in promoting management and quality of healthcare.

Focuses on the social, political, and economic factors that influence health policy decisions. Students will examine the legal, ethical, financial, and political foundations of the health care delivery system and its function as a social institution. The role of the nurse in advanced practice in influencing policy decisions and in addressing the needs of vulnerable and culturally diverse populations will be emphasized.

This course is designed to provide the students with an exposure to the most commonly used statistical concepts, methods, techniques, and their applications to business problems. We cover the basic concepts of business statistics and data analysis integrated in a contemporary spreadsheet environment. The course emphasizes practical applications and business decision-making.

This course introduces the principles, concepts, techniques, and tools for visualizing information in large complex data sets. Unlike scientific visualization, which focuses on the presentation of data that has a spatial or physical correspondence, data visualization focuses on mapping complex, abstract information to a physical representation. The development of effective visualization strategies is crucial for not only facilitating an understanding of large complex data sets but also for driving knowledge discovery and the decision-making processes in a given domain. In this course, students will learn the key principles involved in data visualization and will explore a wide range of visualization approaches that can be applied for understanding complex data across different data types. Specifically, techniques for visualizing: one-dimensional data (e.g., temporal data), two-dimensional data (e.g., geospatial data), multidimensional data (e.g., mapping relational data in n-dimens