On Ramps for Data Science: Visualizing Voting for Trump

Presenter Information

Ella Foster-MolinaFollow

Submission Type

75-minute Hands-on Workshop

Abstract

This hands on interactive workshop using R, a common statistical programming language, demonstrates one way to introduce students to data science. Participants will use visualizations of voting patterns in the United States to learn the power of data science as well as some tools to implement data science skills. Discussions include tips for guiding students through programming for the first time, interpreting data, and understanding how data literacy can improve their understanding of the world.

Start Date

5-23-2019 9:00 AM

End Date

5-23-2019 10:15 AM

Description

Framework:

The goal is to demonstrate the value of digital literacy, specifically in data science, through hands on use of the statistical programming language R, visualizations of voting patterns, and discussions of blending technology with face-to-face teaching.

Expected outcomes:

1. Learn programming skills in R.
2. Discover common pitfalls of teaching students to program, including apprehension over software and the regularity of idiosyncratic errors in programming.
3. Blend digital and interactive learning.
4. Examine the value of using data visualizations to entice students into data science. Data visualization is not only beautiful, but it allows students of all levels to analyze patterns in the data. This encourages critical thinking surrounding data, and shows many of the important irregularities in data that broad overviews can obscure.

Outline of session activities:

This session will have the option to participate in the programming component, which will require they have a laptop with R and R Studio. Ideally at least 5 people will participate in order to demonstrate the practical reality of teaching students to program. All participants will be able to follow along with the visualizations being created, see the way R Studio works, and see the programming occur on the projected screen.

1. Ensure everyone who is programming has R Studio (or help people install it) and the required files for the sessions.
2. Run basic visualizations in R using Anscombe’s quartet to highlight the value of visualizing data rather than simply summarize it.
3. Describe the data being visualized. Discuss various hypotheses for patterns that participants expect to see.
4. Create voting maps, 2-D and 3-D scatterplots.
5. Discuss the resulting patterns.
6. Discuss the format of the session. What worked? What didn’t? What was surprising?

Plan for interaction:

All participants will analyze patterns in voting data, and discuss theories for the patterns they see. At the end of the session, all participants will be invited to discuss how the workshop blended digital and face-to-face teaching, what worked, and ways to improve blending technology and teaching. Participants who choose to program real time will interact with the data using R.