Abstract: With the advent of post-bachelorette opportunities within data analysis and sciences, there is a clear need for students to understand the basics of data science. Data science knowledge gain includes core student understanding in statistics, programming, and data visualization.
Data visualization itself has increased as a desired skillset in job postings by 1500% in the past decade. Data science has often most been taught in computational departments, however, only 18% of all undergraduates majoring in computer science were women in 2015, as compared to ~40% of undergraduates in the geosciences. The geoscience field provides a unique opportunity to teach the basics of data science both with and for geospatial visualization. In addition, given the surge of publically available geoscience data not only are these skillsets essential to undergraduates pursuing outside employment, but also to training the next generation of computational geoscientists in data analysis techniques. Within this work, we present an upper level undergraduate/graduate course in geosciences focused on gaining skills in statistics, programming, and data visualization.
The lab portion of this course is taught through the use of Jupyter notebooks for Python programming, which provide a new way to interact with programming and visualization and to build knowledge. All labs are available online for student and public use. Specifically in notebooks students can both run programs and see outputs and errors, allowing iterative learning and a growth centered course structure. Courses taught by faculty embracing a growth mindset have been shown to lessen achievement gaps of underrepresented minority students in STEM. A growth mindset is defined as the ideal that ability is malleable, and can be gained through various interventions, as compared to a fixed mindset where ability is stagnant. We present the implementation and evaluation of this course through a growth mindset lens.
Azari, A. R., Liemohn, M. W., and Swiger, B. M. “Jupiter with Jupyter: Lessons from Teaching Data Visualization and Statistics in Geosciences”, AGU Fall Meeting. December, 2019. San Francisco, CA.