Pilot Course in Data Visualization with a Multidisciplinary Approach: Technology+Agricultural Engineering

Contribution: A model for integrating data visualization into a multidisciplinary course. This innovative practice paper addresses the challenge of the need for data visualization literacy and the scarcity of time to introduce data visualization concepts and workflow in existing courses. Background: Data sensemaking is becoming a prerequisite for all data-driven activities, from engineering to agriculture and humanities. Students are encouraged to generate graphic visuals to support course deliverables (i.e., reports and presentations), interpret knowledge from data, and disseminate research results with little to no introduction to best practices for visually representing data. This article describes an innovative 16-week course introducing data visualization principles and best practices, visualization tools, GIS concepts, and hands-on experience with edge devices (i.e., NVIDIA Jetson, Raspberry Pi, and Arduino). The first eight weeks of the course were dedicated to data visualization principles and practices. The second half of the semester-long course introduced edge devices. Each student received a J2021-Edge AI Device with NVIDIA Jetson Xavier NX 8GB module. A USB WIFI emulator and a 256GB NVMe SSD were also provided with each device to enable wireless network connectivity and expand onboard storage, respectively. The edge device component of the course comprised several lab-based activities to introduce students to the Linux operating system, command line tools, virtual environments, Python programming language, sensor integration, communication protocols, machine learning, deep learning, computer vision, and application development. The rationale for the instructional approach was to allow students to make meaningful connections between data visualization concepts learned in class and their application to visualizing and analyzing data acquired using edge devices. Intended Outcomes: Students will demonstrate their knowledge and ability to apply data visualization principles to real-world data collected with edge devices, and the course serves as a model for developing interdisciplinary courses that benefit from basic knowledge of visualization concepts. Application Design: The course aims to meet the advanced data visualization needs of the College of Agriculture and Technology students. A higher education challenge grant from the United States Department of Agriculture-National Institute of Food and Agriculture has funded this effort. Findings: Students with little to no background in data visualization and the use of edge devices demonstrated the use of data visualization best practices and utilization of edge devices to complete a required semester project that included a research-quality paper formatted for conference submission.