The Nursing Assistant Instructor Train-the-Trainer Program is designed to equip healthcare professionals with the knowledge, skills, and competencies needed to effectively teach and train nursing assistant students. This comprehensive program focuses on adult learning principles, curriculum development, lesson planning, teaching strategies, and student performance evaluation. Participants will also gain expertise in supervising clinical practice and ensuring compliance with state and federal regulations governing nursing assistant education.
Learner must have valid Wisconsin RN license and two years work experience (one year in long-term, home health, or rehabilitation care). This course will meet the state requirement for nurses who wish to become nursing assistant instructors.
This course focuses on the development of advanced clinical skills across the lifespan. Content includes advanced intravenous skills, blood product administration, chest tube systems, basic electrocardiogram interpretation and nasogastric/feeding tube insertion.
Prepares learners to perform basic nursing skills under the supervision of a nurse for job entry as a certified nursing assistant (CNA) or a home health aide (HHA) in health care agencies. Face-to-face and hybrid classroom, campus lab and clinical instruction are offered at various nursing homes and hospitals throughout the district. Students need to submit an application and complete background check.
Design charts, graphs, dashboards, and other visualizations with an understanding of color and chart type. Learners use the appropriate types of chart based on the data that is being presented and the audience that is viewing the presentation and also build charts for the purpose of exploratory data analysis.
This course covers nursing management and professional issues related to the role of the registered nurse. Emphasis is placed on preparing for practice as a registered nurse.
This clinical experience integrates all knowledge learned in the previous courses in transitioning to the role of the graduate nurse. The course promotes relatively independent clinical decisions, delegation, and works collaboratively with others to achieve client and organizational outcomes. Continued professional development is fostered.
This advanced clinical course requires the student to integrate concepts from all previous courses in the management of groups of clients facing complex health alterations. Students will have the opportunity to further develop critical thinking skills using the nursing process in making clinical decisions. Continuity of care through interdisciplinary collaboration is emphasized.
Employs RapidMiner, R, and Orange software packages in order to facilitate exploration of clustering, association, and text mining algorithms. Learners import a variety of data and use the algorithms in the various software products to extract meaningful information. Learners demonstrate their findings via PowerPoint and short video presentations.
Provides a comprehensive introduction to data engineering principles and foundational cloud computing for data. Students will learn to build and maintain scalable data pipelines, process and store large datasets, and implement cloud-based solutions for secure and efficient data management. With a focus on real-world applications, this course explores data ingestion, transformation, and storage strategies using AWS tools. By combining essential cloud computing skills with data engineering techniques, students will develop the expertise needed to support analytics, machine learning, and business intelligence workflows across diverse environments.
Complex Health Alterations I prepares the learner to provide and evaluate care for patients across the lifespan with alterations in cardiovascular, respiratory, endocrine, and hematologic systems as well as patients with fluid/electrolyte and acid-base imbalance, and alterations in comfort.
Introduces computer programming and terminology in the Python programming language. Special attention is paid to concepts essential to writing basic computer programs. These concepts include: Data Types, Expressions, Loops, File Interaction, Collections, and Functions. Additionally, several tools required to develop Python applications will be explored. Throughout the course learners will develop increasingly complex applications as new topics are introduced.
Introduces the fundamental concepts and history of artificial intelligence (AI), including machine learning, neural networks, large language models and natural language processing. Students will explore AI technologies, review their development over time, and study their impact on society. This foundation will provide students with the necessary framework to understand and work with AI in future courses and professional settings.
This intermediate level clinical course develops the RN role when working with clients with complex health care needs. A focus of the course is developing skills needed for managing multiple clients across the lifespan and priorities. Using the nursing process, students will gain experience in adapting nursing practice to meet the needs of clients with diverse needs and backgrounds.
This clinical experience applies nursing concepts and therapeutic nursing interventions to groups of patients across the lifespan. It also provides an introduction to leadership, management, and team building.
This clinical experience applies nursing concepts and therapeutic interventions to patients across the lifespan. It also provides an introduction to concepts of teaching and learning. Extending care to include the family is emphasized.
Complex Health Alterations II prepares the learner to provide and evaluate care for patients across the lifespan with alterations in the immune, neuro-sensory, musculoskeletal, gastrointestinal, hepatobiliary, renal/urinary, reproductive systems and shock, burns and trauma. The learner will also focus on management of care for patients with high-risk perinatal conditions and high-risk newborns.
This course focuses on topics related to health promotion for individuals and families throughout the lifespan. We will cover nursing care of the developing family, which includes reproductive issues, pregnancy, labor and delivery, post-partum, the newborn, and the child. Recognizing the spectrum of healthy families we will discern patterns associated with adaptive and maladaptive behaviors applying mental health principles. An emphasis is placed on teaching and supporting healthy lifestyles choices for individuals of all ages. Nutrition, exercise, stress management, empowerment, and risk reduction practices are highlighted. Study of the family will cover dynamics, functions, discipline styles, and stages of development.
This course elaborates upon the basic concepts of health and illness as presented in Nursing Fundamentals. It applies theories of nursing in the care of patients through the lifespan, utilizing problem solving and critical thinking. This course will provide an opportunity to study conditions affecting different body systems and apply evidence-based nursing interventions. It will also introduce concepts of leadership and management.
This course will cover topics related to the delivery of community and mental health care. Specific health needs of individuals, families, and groups will be addressed across the lifespan. Attention will be given to diverse and at-risk populations. Mental health concepts will concentrate on adaptive/maladaptive behaviors and specific mental health disorders. Community resources will be examined in relation to specific types of support offered to racial, ethnic, economically diverse individuals and groups.
Builds on Data Visualizations 1 and emphasizes choosing proper charts for quantitative and time-series analysis. Learners build effective dashboards and tell effective stories based on audience needs and analytical comfort. Learners contrast the ways in which data visualization can be used to tell truthful and untruthful stories.
Employs RapidMiner, R, and Orange software packages in order to explore text mining using classification algorithms. Uses k-nearest neighbor and decision trees to further explore classification on structured data. Lastly, learners evaluate time series data using forecasting algorithms. Learners demonstrate their findings via PowerPoint and short video presentations.
Explores the use of AI in modern business environments, focusing on practical applications such as predictive analytics, customer relationship management, and automation. Students will analyze real-world case studies, assess the benefits and challenges of integrating AI into business operations, and explore AI-powered business tools and platforms.
Introduces topics and libraries related to data analytics in the Python programming language. Learners will explore reading, processing, and writing files in native Python. Then they will explore data analytics, processing and visualization using NumPy, Pandas, Matplotlib and Seaborn.
Employs Python, Excel, R, and other GUI software to explore a variety of algorithms that fall under the umbrella of predictive analytics and data mining. Learners derive meaning from data using neural networks. Learners apply statistical models including linear and logistic regression. Lastly, learners evaluate data using Naïve Bayes and Bayesian Networks. Learners demonstrate their findings via PowerPoint and short video presentations.
Examines the ethical, legal, and societal implications of artificial intelligence (AI). Students will explore issues such as bias in algorithms, data privacy, and the broader impact of AI on employment and social structures and will utilize frameworks for ethical AI development and deployment, emphasizing responsible data-driven decision making.
