5 Data Science Teaching Lessons Learned the Hard Way
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Description
Over the past decade I have developed, scrapped, redesigned and raised a number of environmental data science courses at UC Santa Barbara – and I have failed often along the way. In this talk, I reflect on 5 data science teaching lessons that I have learned (and continue to learn) the hard way, then recommend strategies to avoid (or at least feel less alone in experiencing) the failures that inspired them. From how to handle the firehose of “must-add” new tools to courses in such a quickly advancing field, to how to become a superhero at failing publicly during live-coding lessons, I share personal stories of failure and professional growth that have improved my teaching, my courses, and my work-life balance.
About Allison Horst
Allison Horst is an Assistant Teaching Professor at the Bren School of Environmental Science and Management at UC Santa Barbara, where she has been teaching math, statistics, and environmental data science courses since 2013. She studied engineering (BS in Chemical Engineering, and MS in Mechanical Engineering) before earning her PhD from the Bren School in 2012 for her research on the toxicity and interactions of engineered nanoparticles with soil microbes. She contributes to open educational resources in data science through software (e.g. the palmerpenguins R package), open workshop and course materials, and her library of original illustrations for data science and statistics education. From 2019 – 2020, she was RStudio’s first Artist-in-Residence. Allison co-founded the Santa Barbara Chapter of R-Ladies in 2018 and is an active participant in several data science communities of practice. She enjoys fly fishing and finding animal tracks with her dog, Teddy.