What in the data??
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In the age of AI, data literacy is one of the most critical skills we can teach our students. As a STEM educator, data analysis is a constant thread in both my professional and personal life. I find myself analyzing data on a daily basis, whether I am calculating and tracking student grades or conducting rigorous comparative research to find the best cat food for my pets. Data analysis is a useful skill that can be applied to many different things. This summer, I have been putting these skills to the test in my fellowship at KIPAC where I am working with space data from CHIME. I had never worked with astrophysical data before this, but I quickly realized that the core of what I am doing is actually very familiar. The fundamental principles of analyzing data don't change. I am simply taking the analytical techniques I already know and learning how to write them into Python code to study the stars. This realization that data skills are completely transferable is the foundation of the lesson plan I have developed. Designed as a flexible framework adaptable for any STEM classroom, this lesson teaches students the basic steps of how to access public data, analyze it, and create clear visual graphics. The primary focus is on data visualization, guiding students to transform large groups of abstract numbers into helpful charts. By learning to create these visuals, students will be empowered to interpret complex information, recognize trends, and draw their own evidence-based conclusions.
