Data in the wild
A Numbers General Education Course, Lewis & Clark College
Course Overview
Data in the Wild is a semester-long introductory course to quantitative reasoning, primarily using R to apply concepts to “real world” scenarios. This course is designed for first-year college students with no experience coding. Throughout this course, there are three interwoven narratives broken into four modules: a mission to Antarctica as the real world scenario, coding in R, and quantitative reasoning. We rely heavily on tidyverse for many functions, including plotting in ggplot.
Intended Audience
This course is intended for a general education, first-year student audience. This course is designed to equip students with basic quantitative skills for understanding our world.
Narrative Structure
The Antarctica Narrative: We begin module 1 with getting to know the team of students and their relevant skills to the mission. We learn about penguins and Antartic weather. We learn the basics of Rstudio and descriptive statistics. We then transition to module 2, with a sickness outbreak in the team. Students use their descriptive statistics knowledge from module 1 and learn how to create data visualizations to figure out the cause of the sickness. Students practice creating data visualizations using the same penguin data set as in module 1. In module 3, having identified the cause of the illness as aquaculture fish, the team attempts to find new food sources in Antarctica. Students learn about inferential statistics, and perform t-tests and ANOVAs to determine the optimal fishing location. Finally, in module 4, students learn regression analysis to choose how to build a road to the optimal fishing location.
We completed the course with group independent projects based on real Antarctica data, culminating in 1. data visualization(s) and abstract, and 2. oral presentation of the visualizations. Information on independent projects is located within the independent project folder.
Course Material Organization
The lessons are organized by module and week. Our course schedule is located in the extras folder.
We generally structured each module as follows:
Day 1: Lecture or discussion-based introduction to the quantitative and narrative content
Day 2: In-class code along session, homework to follow
Day 3: Small group work on practice and challenge problems using the Palmer Penguin data set
Repeat in-class code along and practice until all module topics addressed. Written reflection prompts were also used ~ once per module as homework. Written exam given at the end of the module, focusing more on core concepts of quantitative reasoning. We also interspersed in-class work days for the independent projects during the second half of the semester.
Github Material Organization
In each module, there is a readme file explaining the broad goals. Each week also has a read me file explaining the sequence of activities.