Writing With Data

Course Description

Data literacy is becoming an increasingly important skill to have: the world is full of data, and almost everything you interact with can be quantified or was created through some means of quantification and/or research. Your computer operates through algorithms, and the work you do on a laptop helps build new algorithms so Microsoft or Apple can develop better products. A can opener has undergone both quality testing and has evolved into a more efficient model through product research. Endangered animal species are counted by scientists and put into categories to raise awareness for further study. Something you buy on Amazon ended up there via lists of successful and unsuccessful product sales. But how is data translated into information—that is, what stories can be told by a data set, and what stories are left out?  Google, Apple, and Amazon are not the only businesses that are built around the existence of data. Even outside of a career in programming or software development, other professional roles require that you know how to write and think about the implications of data at least generally. Writing-focused professions like technical writing or journalism often require their writers to review large data sets and to digest them into organized information that tells a story.

In this course, we will explore the power of data not only as an economical force, but also as a rhetorical one. That is, what kinds of stories do datasets tell? How do those stories persuade us? How can we tell those stories accurately and in ways that are unbiased while still being informative? To do this work, we will need to learn what data are (or is!), what data can do, why it can be dangerous, and how we can work with it for social good. If this sounds like something that cannot be accomplished in one semester—you’re right! This course will then serve as an introduction to both data analysis and some of the basic technical skills needed for technical and professional writing about data... Building on introductory practices in finding, collecting, cleaning, and visualizing data, the class will culminate in a final group project that analyzes a large data set and tells a story about it through a digital medium.

NOTE:  This course welcomes but requires no prior programming/technical knowledge.

Objectives

You will learn about and discuss:

  • A brief history of data, how it can be used for oppressive or negative outcomes, and how it can be used for good.

  • The difference between data and information and; how to think about data and information critically and thoughtfully.

  • A survey of career/job opportunities related to writing and data.

Goals

You will practice techniques for:

  • The Agile method for project management

  • Cleaning a messy dataset via OpenRefine.

  • Using GitHub to manage your data projects.

  • Building CSV files and spreadsheets (via Excel, Google Sheets, etc.).

  • Designing visualizations (via Tableau, Cytoscape or RStudio).

  • Writing about data in a succinct, engaging, and informative way.

Major Assignments

You will do the following work in this class. Because this class uses contract grading (sample contract available below), traditional “percentages” for the work described do not apply:  

1.     Attendance and Participation

This is a workshop-style course, so your attendance and participation in class are critical to your success in this course and to the success of the course as a whole. You should come to class prepared to participate in small group and class discussions. Because the vast majority of the learning in this class will occur within the classroom, you are required to attend class regularly. Attendance will be taken during each class period. Absences will only be excused in situations following university policy.

2.    Workshops

Students will have the opportunity to work with various digital tools and on their projects, and this will include small in-class assignments due at the end of each workshop for a completion grade.

3.    Reading Responses

Students must complete four reading responses of at least 400 words that engage the reading.

4.    Final Project: This includes project proposal, executive summary, data visualizations and analysis (your story).

Students will collaborate on a final project (aka The Data Report), which will be the bulk of the work in the class and thus the bulk of your final grade. This project may live online or exist as a PDF (though you should provide adequate reasons for either decision). It will include:

  • A project proposal & group contract/charter (to be turned in earlier in the semester but also included in this packet): What data set will you use, what is each person in your group responsible for? What roadblocks do you anticipate and how will you keep everyone accountable for their work?

  • A brief executive summary: What is included in your report? Who is your audience and what is the context? What can be found in your report? What is NOT shown?  You can make up a fake company to pitch this to, or target a real company, as long as you argue that it is important to someone. 

  • The Data: Include the original data set and a cleaned data set. 

  • Data Visualizations: Two to three data visualizations made in Tableau or Cytoscape. 

  • Your Story: What do the data suggest to your audience? What can you say about it? Include a story for each visualization, and one big “so what.” 300 words per story, minimum.

 
 
 

Readings + Course Material

Nussbaumer Klafic, Cole. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley, 2015.

Nussbaumer Klafic, Cole. Storytelling with Data: Let’s Practice! Wiley, 2019. 

Tactical Tech Collective. “Visualizing Information for Advocacy” https://www.visualisingadvocacy.org/node/638.html 

 

Excerpts from:

Noble, Safiya. Algorithms of Oppression. NYU Press, 2018. (pp. 15-30)

O’Neil, Cathy. Weapons of Math Destruction. 2016. (Chapter 5, Civilian Casualties: Justice in the Age of Big Data).

Stephens-Davidowitz, Seth. Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are. Harper-Collins, 2017. (Chapter 3, Data Reimagined).