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

Teaching Data Analytics and GIS Fundamentals to Novice Undergraduate Researchers in a Seven-Day Bootcamp

Paper number: 170-11
Program: Technical Sessions
Time: 10:45 AM – 11:00 AM

Authors

Abstract

The summer research program at Hobart & William Smith Colleges prioritizes a first mentored research opportunity for STEM students early in their academic careers. Early research experience improves retention in STEM, but limited prior classwork means that students enter their first research opportunity with little background and few hard skills. We fast-tract the development of data analytic, data visualization (using the R statistical computing language in the positCloud/RStudio environment) and geographical information system (using ArcOnline) skills in a seven day, four hours per day bootcamp. The bootcamp introduces the data analytics life cycle and key skills at each step, along with concepts and skills in the presentation and analysis of geospatial data. Our daily schedule includes two hours of data analytics and two hours of GIS content, typically straddling a lunch hour. Each session integrates classical instruction, code-along instruction, and self-paced work within a project-based framework. Classical instruction (instructor lectures and presents computer-based demonstration) provides students with essential terms and concepts along with their context. The associated coding demonstration illustrates analytical workflow and results. Code-along instruction (students clone the instructor’s workspace and mimic demonstrated operations on project-associated data) allows students to practice immediately after initial exposure. Self-paced work time (students work with instructors available for individual coaching) makes space, typically 50% of session time, for students to work individually or in small groups on a data project. We choose project data to thematically fit the research students will conduct after the bootcamp. To conclude, students present an ArcOnline-based storymap that integrates their analysis, data visualizations and maps to draw conclusions. In addition to teaching skills, the project-based framework encourages students to consider what questions they can (and cannot) answer with available data, to frame analyses around questions/hypotheses, to draw inference from analyses and visualizations, and to practice clear, concise technical communication. These skills transfer directly to their larger research experience. Because time is limited, we tailor bootcamp topics within major themes (e.g., data cleaning or visualization) to the data with which students will be working. This selectivity means that the bootcamp is an introduction to, rather than a substitute for, semester-based classwork. An outline of topics will be available at the conference.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-413379

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