This module examines the open loop response of a small motorized cart with a voltage applied to the motor. This module has 2 components: The individual data collection and analysis, and then drawing conclusions based on the aggregated class data.

This module allows students to explore data on lyme disease cases, deer population, and land use and environmental factors for four different states, Connecticut, Maryland, New Hampshire, and Massachusetts using various data analysis techniques. Six Canvas quizzes with mainly short answers and a few multiple choice questions guide students through a Google Colab application.

This module allows students to engage with the wind energy power equations and explore other considerations in the siting of a wind farm. Students work through three block assignments in Google Colab, beginning with the wind power equations and culminating in considerations in siting a wind farm.

This module reinforces underlying statistical concepts in the process of building a data analysis pipeline. Students practice statistical concepts to gain an understanding of the airline data in Part 1, then the data is used to implement machine learning models in Part 2. The final deliverable is a slide deck, in which students act as consults for the Phoenix Sky Harbor Airport using insights gained from supervised machine learning analysis of the relationship between airline carrier delays and passengers per flight.

In this module students learn and apply the systematic steps that anthropologists may take to make deductible inferences about historical societies given the observations of fossil (foot print) records. Students first collect data on their own footprints using a sandbox built by DIFUSE, then analyze aggregated data from the entire class, and finally use their insights to make inferences about social behavior of historical populations.

This course module is a two-step assignment in which students collect data on shots taken during a provided basketball game video using the two main data collection methods used in research on primate behavior, focal bout sampling and instantaneous scan sampling. The class data is then aggregated and visual representations are created and discussed. The goal of the module is for students understand the respective strengths and weaknesses of the two data collection methods.

This module consists of six assignments in which students learn and then apply air quality dispersion modeling using an R-based programming module, with the help of the package ‘openair’ and open-sourced air quality datasets of cities in Germany.

This course module consists of four assignments in which students explore different categories of factors that could affect self-rated health in Texas counties. Students use a linear regression and a heat map to explore these relationships.

This module consists of two assignments. The first guides students through modeling simple ODEs in Matlab, and the second, longer assignment, guides students through modeling the Glucose Insulin System in Matlab with Euler’s Method. The students are then expected to explore this model by optimizing one parameter for a given set of data using the least squares method.

This course module is a web-app accompanied by a short-answer-based assignment to guide students through it. Students will use spatial data to visualize human-environment relationships and analyze those relationships through data visualization, plotting, and linear regression analysis.

This course module consists of Jupyter notebooks designed to introduce students to basic functional R commands/procedures whilst tying in key statistical content. It aims to give novice students competence in R and challenge experienced students.

This course module is a series of group exercises and one problem set designed to introduce students to the way Astrophysicists manipulate data and perform analyses in Python with an emphasis on data visualization and plot interpretation.