Business Applications of AI 10156122

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.

Data Concepts 1 10156131

Data Concepts 2 10156132

Data Mining & Predicitve Analytics 2 10156111

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.

Data Mining & Predictive Analytics 1 10156109

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.

Data Mining & Predictive Analytics 3 10156115

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.

Data Visualizations 1 10156113

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.

Data Visualizations 2 10156117

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.

Designing And Timing Your Garden 31001103

Utilizing planning and space saving techniques, learners will develop a wide range of garden design options available. Additionally, Wisconsin weather should be considered when creating the garden design. Learners will outline when and where to plant and harvest cool and warm season plants.

Ethics and Implications of AI 10156123

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.

Foundations of Data Engineering 10156124

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.

Fruit Crop Production I 31001118

Introduces learners to pomology and its role in both global and local food systems. Students will explore the foundations of fruit crop production, with emphasis on fruit growth and development, integrated pest and disease management, and the principles of harvest and post-harvest handling. Examines how fruit physiology and production practices intersect to influence yield, quality, and market value. Throughout the semester, a different fruit or nut crop will be highlighted each week, allowing students to build a broad understanding of diverse species and cultivar selection, growing requirements, and their importance in food systems.

Garden Production Basics 31001114

Examines plant development and factors that affect plant growth. Through hands-on experience, text, lab and classroom activities, learners will examine environmental effects, market gardening equipment, irrigation tools, and fertilizers necessary to grow healthy, productive plants. Emphasizes crop production practices in small- and large-scale gardens, high tunnels, and a greenhouse.

Greenhouse Operation and Design 31001113

Explores greenhouse parts, structure, and coverings along with environmental controls will be covered throughout this course. Learners will actively participate in the operation of a greenhouse. Learners will evaluate greenhouse structure and design, while participating in greenhouse site assessment.

Hydroponics 31001117

Explores the fundamentals of hydroponics and growing plants using water as a growing medium. Hands-on learning activities include growing and maintaining plants in an indoor hydroponic growing facility, a bato bucket system, and vertical hydroponic gardens. Learners will discover the scientific concepts behind growing plants in hydroponic systems and become experts in alternative growing methods.

Introduction to AI 10156121

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.

Introduction to Python 10156102

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.

Introduction to R 10156114

Introduce learners to the foundational concepts of data programming in the open-source language R. Learners explore ways to visualize data, examine data structures, and model the relationship between variables using commands in R. Learners incorporate problem-solving and critical thinking skills as they write commands to support their work with data analysis.

Introduction To The Horticulture Industry 31001100

Explores the scope of the horticulture sector, including career opportunities. Familiarizes learners with the Agriculture Center of Excellence and standard operating procedures. Learners will begin to develop a strategic college plan. Learners will be exposed to the various opportunities available throughout their program of study.

Merchandising Produce Applications I 31001120

Introduces the principles and practices of produce merchandising for roadside stands and farmers markets. Covers methods for assessing produce quality, grading standards, and their application in marketing channels. Explores creative visual merchandising techniques and the relationships between customer behavior, effective product display, and product purchase. Examines the structure and success factors of community-supported agriculture (CSA) businesses. Students actively participate in operating farmers market stands using produce grown during the course, applying merchandising concepts in real-time.

Pest and Disease Management 10001105

Researches the latest science-based recommendations for battling garden and field pest problems. Throughout this course, students will learn to identify pests and plant diseases. Learners will determine what corrective action should be taken, if any. Integrated pest management concepts are covered in this course.

Python for Data Analytics 10156103

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.