170 Quantitative and Data Analysis Skills in Geoscience Education: Supporting Student, Course, and Program Outcomes, Part I
Quantitative and data analysis skills are critical for geoscientists but often a barrier for students. This session includes strategies for skill development, from activities to geoscience-focused quantitative skills courses to departmental innovations supporting quantitative and analysis skills across degree programs.
Tuesday, 21 October 2025
8:00 AM - 12:00 PM
HBGCC, 301B
Comparing Two Number Sets: A Foundational Concept for Earth Science Education
Paper number: 170-2
Program: Technical Sessions
Time: 8:20 AM – 8:35 AM
Authors
Abstract
Comparing two number sets is a foundational component of the scientific process. Our ability to assess the similarities and differences between two number sets allows us to make judgments that lead to advancements in knowledge or application of that knowledge towards operational decisions. This comparison could be graphical, making a qualitative assessment about two lines or figures, but more often it must be quantitative. There are many ways to compare two number sets, though, and it can be a confusing task to choose the right process for a particular situation. A robust and methodical approach to comparing two number sets is, therefore, a critical skill with wide application across all natural science disciplines and career paths. This presentation focuses on a course taught in the University of Michigan’s Department of Climate and Space Sciences and Engineering that systematically tackles the issue of comparing two number sets. A textbook has been written to accompany this course. The course is taught with a hands-on approach via Python coding in Jupyter notebooks, conducted in parallel with textbook content. The art of comparing two number sets is comprehensively presented with numerous examples using real data sets from the Earth, ocean, atmosphere, space, and planetary sciences. Enough scientific context is provided to allow non-specialist students to appreciate the analysis and make a good assessment. The comparisons start with one data set against a known distribution (e.g., a Gaussian), then on to two data sets, and finally getting to the topic of data-model comparisons. The statistical toolkit covered in this course provide a solid foundation for any natural science discipline, as well as careers beyond academic research. Students give a presentation on their end-of-course term project. A growth mindset is used for learning evaluation and the course teaching goals reflect this approach within the content and assessment.
Geological Society of America Abstracts with Programs. Vol. 57, No. 6
doi: 10.1130/abs/2025AM-412721
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