This course is broken up into eleven modules that cover the gamut of data-driven storytelling. Each module contains different kinds of learning materials, including brief videos to introduce concepts, external resources that elaborate on those concepts, and tutorials and activities that allow you to apply skills associated with those concepts. The course also includes different data challenges that allow you to demonstrate your mastery of the course content.
Each module contains roughly three to four hours of learning materials, with the activities and challenges adding to that total. While it is possible to consume several modules within a single week, I would discourage you from doing so. Take your time and let the material sink in.
While the modules are designed to be consumed in a linear fashion, this isn't necessary. You're welcome to remix the curriculum to suit your needs as the material also contains several refreshers for earlier content. You're also encouraged to go off-script and tackle on datasets and challenges not included in this course.
Module 1: Data in Journalism
This module introduces journalistic data-driven storytelling, clarifying what is unique about it and what the process of producing such stories typically looks like. This serves as a brief foundation for what is to come over the next ten modules.
- What is Data Journalism?
- Why Data and Journalism Need Each Other
- The Data-Driven Storytelling Process
- Diakopoulos, N. (2018). Ethics in Data-Driven Visual Storytelling.
- Webster, M. (2016). What is a "Data State of Mind"?
Module 2: Introduction to R and RStudio
This module covers different kinds of data you might encounter as well as two key tools used by data journalists to analyze data: the R programming language and the RStudio development environment.
- Data Structures and Formats
- Types of Data
- Herzog, D. L. (2016). Data defined.
- RStudio. (2016). RStudio IDE Cheatsheet.
- Creating an R Notebook
- Intro to R
Module 3: Evaluating Sources and Exploratory Analysis
This module introduces best practices for how to find datasets and ascertain whether they come from trustworthy sources. You'll also begin to analyze data using descriptive statistical methods. This module also contains your first data challenge, where you can begin to put the skills you're developing into practice.
- Finding Data Online
- Evaluating Data Sources
- Making Sense of What's In a Dataset
- Webster, M. (n.d.). Finding Data.
- Zamith, R. (2015). Open Records Request Example.
- Exploring a Dataset
- Exploratory Data Analysis in R
- Data Challenge #1
Module 4: Exploratory Visualization and Calculations
This module introduces more statistical concepts to ensure that you are performing analyses that fit your data, and that those analyses are not over-generalized. You'll also begin to explore your dataset in a visual fashion, which can help you identify values of interest that you might otherwise miss.
- Measurement Scales
- Describing Central Tendencies and Spread
- Samples and Censuses
- Correlation, Causation and Change
- VCE Further. (2011). Pearson's Correlation Coefficient.
- Exploratory Data Visualization in R
Module 5: Data Extraction and Cleaning
This module covers tools and methods used to extract structured data from non-structured files and documents. Unfortunately, this process is often imperfect (and even structured files might come with mistakes), so you'll begin to "clean" those data to remove mistakes that might impact your analysis. You'll also take on your second data challenge at this point, ensuring that you can translate a basic analysis of a dataset into a news brief.
- Extracting Data from PDFs
- Working with Messy Data
- Cleaning Data with OpenRefine
- Working with Multiple Datasets
- Importing and Cleaning Data with Excel
- Schacht, K. (2019). A web scraping toolkit for journalists.
- Extracting and Cleaning Data
- Data Challenge #2
Module 6: Story Ideas and Interviewing Data
This module focuses on one approach to data-driven storytelling: Starting with data and generating interesting, comprehensive story ideas from them. You'll also begin to apply more advanced grouping and data transformation functions in your data analysis.
- Generating Story Ideas from Data
- Herzog, D. L. (2016). Identifying and Obtaining Data.
- Singer-Vine, J. (n.d.). Data Is Plural — Structured Archive
- Exploratory Data Analysis in R (Part 2)
Module 7: Humans in Data-Driven Storytelling
This module focuses on how to interview human sources and integrate them into a data-driven story. The module also highlights the advantages of human sources, and how they can be incorporated alongside quantitative information within a news story.
- Humans in Data-Driven Stories
- Adams, S. (2001). Interviewing Techniques.
- Scanlan, C. (2013). How Journalists Can Be Better Interviewers.
Module 8: Telling Stories
This module covers the elements of a story story, including the development of a compelling lead paragraph, the translation of complex quantitative information into something more easily digested by a general audience, and different approaches to writing with numbers. You'll also have your third data challenge alongside this module, and begin to write your first full-length data-driven story.
- The Value of a Strong Lead
- Making Data Comprehensible
- Narrative Elements for Data-Driven Stories
- Cohen, S. (n.d.). Writing With Numbers.
- No Author. (n.d.). Example Inverted Pyramid Story.
- Breaking Down Stories
- Data Challenge #3
Module 9: The Visual in Data-Driven Storytelling
This module introduces basic design principles and how to select the chart that best conveys an important observation from your data analysis. The module also introduces ideas for how to assess the effectiveness of existing data visualizations and points to different visual style guides used in the field.
- Types of Charts
- The Working Parts of a Chart
- Chart Design Principles
- Yau, N. (2013). Visualizing with clarity.
- Few, S. (2017). Data Visualization Effectiveness Profile.
- Dallas Morning News. (2005). Graphics Stylebook.
- Urban Institute. (n.d.). Data Visualization Style Guide.
- Breaking Down Data Visualizations
Module 10: Creating Uni- and Bi-Variate Charts
This module covers how to produce simple, interactive charts that are useful for conveying observations from descriptive statistical analyses. It also shows how you can create a properly formatted CSV file that can be imported into popular data visualization tools.
- deltaDNA. (2015). Plotting in R.
- Cairo, A. (2016). Basic Principles of Visualization.
- Yau, N. (2013). Exploring data visually.
- Creating CSV Files and Charts (Part 1)
- Creating CSV Files and Charts (Part 2)
Module 11: Cartography and Journalism
This module introduces basic principles for designing maps and visualizing spatial phenomena. The module also covers the creation of two different types of maps: choropleth and symbol maps. Finally, you'll be completing your final data challenge, which requires you to produce two interactive data visualizations, in this module.