About DS Active Learning Lab
About the Lab
The field of Data Science is really just starting to fully development across the landscape of higher education. As the field pushing into the undergraduate space rapidly over the next couple of years the need to develop high quality education norms and practices for this new field is critical. The goal of the Data Science Active Learning Lab is to support this process with practical and hands-on tools that teachers can pick up and use.
Active learning lab assessments
A Card Game for Decision Trees
This is a unplugged card game that allows students to better understand the intuition behind classification trees and how they are evaluated.
Balloon Targets for Probability Intro
This is a unplugged game that has students work in teams to determine the likelihood of popping a balloon in a carnival style game.
Entropy and Data Viz Design
Here are two additional assessments, one designed for understanding entropy through playing the game Mastermind and another highlighting how students can use their cell phone data for a visualization task.
kNN on a Board
Another unplugged gamified lab for learning kNN. Students use large data printed on dry erase boards to classify target data.
Worst Team in the NBA
This is a more advanced lab that using up to date NBA stats to force students to think about strategy and build technical skills when making choices about which players to target for a trade to help the worst team in the NBA (probably the Wizards)
Python Starter Modules
These materials created by Rafael Alvarado covering 3 basic topics: getting started, control structures and functions.