Course Curriculum

The Art of Data Journalism is designed to be a course in three parts.

  1. A demonstration in class – live on the screen or with students pair programming – so they can see the concept working. Bonus points if you can tie it to a news event going on in that week.
  2. Students work through the tutorials for credit. I set up fill-in-the-blank quizzes in the campus learning management system where students fill in the same blanks in the tutorials into the LMS. Those quiz questions can grade themselves, and students get credit for doing the work.
  3. At the end of that quiz is a more open-ended set of problems. Students are given a dataset – ideally local to campus or the city – and series of questions worded like an editor would ask them. They have to write the code needed to answer the questions, code that all comes from that chapter or previous ones. For example, Chapter 3 is about grouping by and counting using nursing homes. How many nursing homes are there in each county in your state? You can test their ability to extend the idea by giving them campus police calls and asking how many calls went to each academic building. These questions are just a text box – no structure. And students turn in their notebooks, also for credit.

What You Need to Start

Technical Requirements

  • A computer (Windows, Mac, or Linux)
  • R and RStudio installed (free)
  • Internet connection
  • Basic computer literacy

Background Knowledge

  • No programming experience required
  • No statistics background needed
  • Basic math skills helpful
  • Curiosity about data essential

Tutorial structure

Foundations (Lessons 1-6)

  • Basic R programming concepts
  • Working with data frames
  • Introduction to the tidyverse
  • Understanding data types
  • Aggregating and summarizing data
  • Filtering and selecting data
  • Basic statistical concepts

Data Realities (Lessons 7-13)

  • Working with dates
  • Data cleaning basics
  • Working with text patterns
  • Reformatting data
  • Algorithmic cleaning tools
  • Joining datasets
  • Working with spreadsheets
  • Working with APIs

Visualization (Lessons 14-28)

  • Creating basic charts with ggplot2
  • Advanced visualization techniques
  • Making tables for publication
  • Adding annotations and context
  • Color theory and accessibility
  • Working with Datawrapper

Each lesson includes: - Guided exercises - Real-world examples - Practice problems - Code solutions

Going further

The Art of Data Journalism Tutorials are meant to be the backbone of a class, but there’s much more room in a data journalism course for learning.

  • Require students to request data from public agencies under your state’s public records laws.
  • Have students work in teams to come up with stories out of a dataset just released, such as annual school standardized test scores or Census updates.
  • Require students to write a story pitch memo based on a news event in your state combined with a relevant dataset about that event. Students have to acquire the data, analyze it and document a story that would go deeper on that issue using what they found.
  • It’s difficult if your classes are larger, but have students do their own data journalism story. Go through the entire process of reporting, acquiring data, analyzing it, reviewing your work and publishing.