Data Science Across Disciplines In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning, large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Biology, Geography, History, Mathematics/Statistics and Sociology. This course will use the R programming language. No prior experience with R is necessary.  BIOL 1230: Students enrolled in Professor Casey’s (Biology) afternoon section will use the tools of data science to investigate the drivers of tick abundance and tick-borne disease risk. To do this students will draw from a nation-wide ecological database.  GEOG 1230: In this section, we will investigate human vulnerability to natural hazards in the United States using location-based text data about hurricane and flood disasters from social media. We will analyze data qualitatively, temporally, and spatially to gain insights into the human experience of previous disasters and disaster response. We will present findings using spatial data visualizations with the aim of informing future disaster preparedness and resilience.  HIST 1230: In U.S. history, racial differences and discrimination have powerfully shaped who benefited from land and farm ownership. How can historians use data to understand the history of race and farming? Students will wrangle county- and state-level data from the U.S. Census of Agriculture from 1840-1912 to create visualizations and apps that allow us to find patterns in the history of race and land, to discover new questions we might not know to ask, and to create tools to better reveal connections between race, land, and farming for a general audience.    STAT 1230: In this course students will dive into the world of data science by focusing on invasive species monitoring data. Early detection is crucial to controlling many invasive species; however, there is a knowledge gap regarding the sampling effort needed to detect the invader early. In this course, we will work with decades of invasive species monitoring data collected across the United States to better understand how environmental variables play a role in the sampling effort required to detect invasive species. Students will gain experience in the entire data science pipeline, but the primary focus will be on data scraping, data visualization, and communication of data-based results to scientists and policymakers.  SOCI 1230:  Do sports fans care about climate change? Can sports communication be used to engage audiences on environmental sustainability? In this section of the course, students will use the tools of data science to examine whether interest in sports is associated with climate change knowledge, attitudes and behaviors, as well as other political opinions. Participants will use survey data to produce visualizations and exploratory analyses about the relationship between sports fandom and attitudes about environmental sustainability.

Casey Schine

Reviews and ratings for Casey Schine at Middlebury College.Casey Schine teaches DataScience Across Disciplines and more.  Explore top rated instructors and find the best for you.Is Casey Schine good ? Find out on MiddCourses.

This class was wonderful. I was completely new to coding, and it was a perfect introduction to R. I attended almost every single office hours, and my efforts were rewarded. While coding is inevitably frustrating, Professors Schine and Lyford went above and beyond to ensure we were supported all the way through. We had weekly homework assignments that built on one another and there was a final project to pull all of our knowledge together and tell a story of an argo asynchronous float in the Southern Ocean. If you need to take a stats class, I would highly recommend this course. 