Courses

BUAN 683 AI AND FINTECH IN FINANCIAL MARKETS   3.0 Credit(s)
Course Title - Artificial Intelligence and Financial Technology in Financial Markets. We will cover a variety of applications of AI and Fintech in the financial markets, including streamlining credit and loan transactions, automating and personalizing financial services, predictive analysis for investment and risk management, fraud detection and regulatory compliance, as well as direct and cost-effective fundraising. Students will create their own crowdfunding projects through ICOs and NFTs.
Offered: All Semesters All Years

BUAN 684 MACHINE LEARNING IN FINANCE   3.0 Credit(s)
This course is focused on the subsequent financial use cases: Risk management, credit scoring, fraud detection, unstructured big data insights (NLP), consumer sentiment, social finance and product evaluation. In addition to supervised (e.g., classification and prediction) and unsupervised (e.g., clustering) machine learning, students will gain knowledge of causal machine learning and apply it to solve problems associated with the aforementioned use cases.
Offered: All Semesters All Years

BUAN 682 DIGITAL CURRENCIES: BLOCKCHAIN & CRYPTO   3.0 Credit(s)
Course Title - Digital Currencies: Blockchain and Cryptocurrency We will cover the mainstream blockchain-based digital currencies (e.g., Bitcoin, Ethereum, and Uniswap), stable coins (e.g., MakerDAO, USDT, etc.), as well as Non Fungible Tokens (NFT). We will also discuss the Central Bank Digital Currency (CBDC) and its interdependency with blockchain technology. We will focus on the financial aspects of digital currencies, including valuation, trading, liquidity, investment, collection, and regulation. Students will create their own digital currencies.
Offered: All Semesters All Years

CS 649 STATISTICS FOR DATA SCIENCE   3.0 Credit(s)
This course provides the necessary skills to successfully navigate through the Data Science track. Topics include: data sampling, tendency and distribution of data, hypothesis testing, variations, regression and probability

BU 691 AI CAPSTONE   3.0 Credit(s)
This course equips students with the ability to use AI and ML principles and algorithms, which they have learned in previous semesters, to solve a business challenge or problem of their choice. The chosen topic or problem must be approved by the instructor. This course specifically equips students with the requisite technical expertise and tools, including no code or low code solutions, to effectively address a business challenge in a data-driven way.
Offered: All Semesters All Years

BU 690 INTRODUCTION TO AI   3.0 Credit(s)
This course covers the essential elements and model areas of Artificial Intelligence and the application of AI in business.
Offered: All Semesters All Years

BU 692 IMPACT OF AI ON INNOVATION   3.0 Credit(s)
The impact of AI on innovation is not just in its technological advancements but also in its ability to reshape industries, economies, and societal norms, underscoring the importance of navigating its development and application responsibly. AI is contributing to the creation of entirely new categories of products and services, such as autonomous vehicles, smart home devices, and personalized content recommendations, enhancing consumer experiences and creating new markets. However, the integration of AI into innovation processes also presents challenges, including ethical considerations, the potential for job displacement, and the need for regulatory frameworks. As AI becomes increasingly sophisticated, ensuring that innovation remains human-centered and benefits society as a whole is crucial.
Offered: All Semesters All Years

MFIM 653 PREDICTIVE ALGORITHMS IN FINANCE   3.0 Credit(s)
This course covers a variety of methods used for predicting the behavior of assets, as well as complete portfolios. We will cover traditional methods ranging from the Capital Asset Pricing Model and its offshoots, to Factor Modeling and Portfolio Simulation, as well as more recent innovations under the broad headings of "Machine Learning" or "Artificial Intelligence". Students will learn how modeling future behavior almost always depends critically on analyzing data, as well as hands on techniques for turning that data into reliable hints about what may happen in the future. Prerequisite: Take MFIM-636 MFIM-640
Offered: Late Spring Semester All Years

NU 700 THEORETICAL COMPONENTS OF NURSING SCI.   3.0 Credit(s)
This course investigates the study of knowledge shared among members of the nursing discipline, the patterns of knowing and knowledge development, criteria for evaluating knowledge claims, and the philosophy of science. The nature of theory, theory development in nursing, and significant conceptualizations of nursing are discussed. Through guided search and discussion, doctoral students will become knowledgeable about the utilization of theory to guide practice.
Offered: Fall, Spring & Summer Sems All Years

NU 710 HEALTH CARE POLICY ADVOCACY & ETHICS   3.0 Credit(s)
This course explores the effect of the interrelationship between policy, advocacy, and ethics on clinical practice and health care leadership. The advanced practice nurse's role in health care policy and planning is examined. An overview of issues in health care policy and planning, including the socio-political and economic context of health and health-seeking behaviors will be provided. Health care policy and planning at the local, state, and federal levels will be considered. Issues in clinical practice will be examined for how legislation and regulations impact care. The course examines the structure and function of legislative and regulatory organizations, governance, public relations, and global health care issues. Broader social issues common to the care of underserved and vulnerable populations will be examined. Ethical dimensions of public policy formulations and implementation will be highlighted.
Offered: Fall, Spring & Summer Sems All Years

NU 720 Leading Quality Initiatives & Info Sys   3.0 Credit(s)
This course introduces the fundamentals of patient safety and quality improvement (QI) in a variety of healthcare settings. Emphasis is placed on the development, implementation, and evaluation/measurement of evidence-based healthcare QI practices. Management of complex system change within the healthcare environment is reviewed as part of the QI process. Examining key issues related to patient safety is an important concept throughout this course. Information Systems (IS) is also addressed throughout this course to examine the best application to the QI process along with daily practice needs. Students examine key processes involved in optimal patient safety, outcomes, and the overall delivery of health care services.
Offered: Fall, Spring & Summer Sems All Years

NU 740 EPIDEMIOLOGY & POPULATION HEALTH   3.0 Credit(s)
The primary focus of this course is to equip students with a foundation in clinical prevention and population health. This course introduces students to the methods used by epidemiologists to assess factors associated with the distribution and determinants of health and disease in populations and to read, interpret, and apply literature using epidemiologic and statistical methods. Topics include a discussion of the historical background as well as practical applications of epidemiology, methods for identifying and evaluating sources of health applications of epidemiology, methods for identifying and evaluating sources of health information, calculation of key epidemiologic measures and investigation techniques, and an evaluation of the strengths and weaknesses of different study designs. Current concepts of public health, health promotion, evidence-based recommendations, determinants of health, environmental/occupational health, and cultural diversity and sensitivity are integrated throughout the course. Specifically, this course examines methods for describing disease rates and other vital statistics; cohort, case-control, and cross sectional studies; odds ratios, relative risks, their confidence intervals, and tests of significance; and concepts of confounding, effect modification, and bias. A basic understanding of introductory biostatistics is required for this course. This foundation will enable students to analyze epidemiological, biostatistical, occupational, and environmental data in the development, implementation, and evaluation of clinical prevention and population health.
Offered: Fall, Spring & Summer Sems All Years

NU 760 STRAT LEADERSHIP & COLL IN HEALTH CARE   3.0 Credit(s)
This course addresses organizational and systems leadership skills for advanced leadership in practice to improve clinical health care systems and promote excellence in care. Focus is on transformational leadership, strategic visioning and planning, collaboration with the health care team to make data driven decisions at both the micro and macro systems level. Understanding how healthcare is financed and the implications for health care organizations are applied. This course is a leadership elective 3-credit course for the PM/DNP Hybrid Program student.
Offered: Fall, Spring & Summer Sems All Years

NU 770 ADVANCED CARE OF SPECIAL POPULATIONS   3.0 Credit(s)
This course is designed to enable the doctoral student to refine and expand the diagnostic and management skills necessary to care for vulnerable and disenfranchised populations. The elimination of health disparities has been identified as an area of research emphasis by the National Institute of Nursing Research. This course examines health determinants and health disparities within the United States as well as in the global community. The student will examine health disparities and the burden of disease within social, cultural, political, economic, and environmental contexts using a systematic, multidisciplinary approach. Given the complexity of care, growth of information and biomedical technology, an aging and increasingly diverse population, and worsening disparities in care, this course will prepare the student to fill the growing societal need for expert clinicians. This course focuses on the complex management of healthcare problems experienced by special populations across the lifespan. Emphasis is placed on content specific to the special populations in the areas of infectious disease, psychiatric care, and care of medically underserved populations such as the homeless, refugee populations, and the incarcerated. Case examples and clinical experiences are provided that allows students to become increasingly independent in their own clinical practice with respect to critical thinking and problem-solving. Emphasis in role development is placed on effecting change and integration of the multiple roles for advanced practice nurses in an interdisciplinary, integrated health system.
Offered: Fall, Spring & Summer Sems All Years

NU 780 LEAD. CHRON DIS MGMT AGING POPULATIONS   3.0 Credit(s)
This course is designed for graduate students in the DNP program who seek to gain leadership skills and knowledge in the management of chronic disease and aging populations. Doctoral students will synthesize knowledge from physiological, psychological, and sociological/cultural perspectives that are important to the aging person and their families. Evidence-based practice guidelines are used to support clinical management plans and optimal patient outcomes for geriatric clients in both inpatient and primary care settings. Ethical principles will be used to guide clinical decision-making when complex problems or issues create a dilemma in the delivery of care to elderly populations (i.e., elder abuse, reimbursement-driven care, and advanced directives). The application of advanced nursing practice theory into supervised clinical practice will be included and emphasized.
Offered: Summer 1 Semester All Years