Teaching data visualization: Recommended readings and resources

I want to share the reading/resource list in my data visualization course; the list breaks into six sections: intro to data viz, choosing the right chart, designing a nice-looking visulization, communicating your message, tools/tips, and resources. This list will be a work in progress and all suggestions are welcomed.

Intro to data visualization

A Quick Illustrated History of Visualization: Data visualization has its roots in a long historical tradition of representing information using pictures in ways that combine art, science and statistics.

Why Data Visualization Matters? Data visualization reveals unnoticed information, especially in large data sets; gives answers faster; and helps journalists investigate cause-effect relationship.

Patterns for Information Visualization: in this long article, the author uses some hands-on examples to show how visualization helps people make decisions and work with data.

Data Visualization for Human Perceptions: excerpt of a popular book on data visualization; with historical info and case studies.

Storytelling: The Next Step for Visualization: this is a 12-page paper, in which the authors review the literature and history of presentation and storytelling in visualization, discuss examples, and outline a research program to develop storytelling as a visualization task of equal importance to exploration and analysis.

The 5 Most Influential Data Visualizations of All Time: a free Whitepaper by Tableau Software, citing examples from the 18th and 19th centuries.

Data Art vs. Data Visualization: Data art refers to visualizations that strive to entertain or to create aesthetic experiences with little concern for informing; data art is harmful when it masquerades as data visualization.

Choosing the right chart

Visualization Options Available: A detailed introduction of 21 popular chart types, in six categories, by Many Eyes.

Different Charts Tell Different Tales: Two of the most basic chart types are bar charts and line charts. While they are very similar in their use cases, they can also differ greatly in their meaning.

Choose a chart type: With so many chart types available, how do you know which is best for you? Keep in mind, the point is to get your message across in the most effective way. Check out this guide by Microsoft Office.

Chart types and data formats: Google has a tutorial on 17 chart types for users of Google Drive. Each chart comes with brief intro, required data format and a sample chart.

Two of the most basic chart types are bar charts and line charts. While they are very similar in their use cases, they can also differ greatly in their meaning. – See more at: http://datajournalismhandbook.org/1.0/en/delivering_data_6.html#sthash.ytO8c2BT.dpuf

An Economist’s Guide to Visualizing Data: a journal article that examines the wrong/right use of common charts.

Visual Math Gone Wrong: how a US Census Bureau visualization shows some good thinking but ultimately fails to do what it was designed to.

Using Indexed Charts When Understanding Change: Indexed charts are useful to (a) understand change with respect to a bench mark, (b) compare values which are vastly apart and (c) understand growth (or non growth).

Stacked Area Chart vs. Line Chart: The pros and cons of each chart and why there has been a debate about merits of these two charts.

Bar Charts for Nonprofit Data Nerds: A short powerpoint slide introducing typical uses of several bar charts.

The waterfall charts: what they are, an example use case, and how to create one.

The tree map: this article explains what a tree map is and does, how to create one; also has additional resources and further readings.

Present your data in a scatter chart or a line chart: Scatter charts and line charts look very similar, especially when a scatter chart is displayed with connecting lines. However, there is a big difference in the way each of these chart types plots data along the horizontal axis (which is also known as the x-axis) and the vertical axis (which is also known as the y-axis).

Top Ten Dos and Don’ts for Charts and Graphs: Tips by the Data & GIS Services at Duke University library. (Also has other data visualization resources; follow links at top of page)

Designing a good-looking visualization

Data Visualization: Clarity or Aesthetics? A diagram that shows how a graphic design can be clear/beautiful or confusing/ugly.

A color palette optimized for data visualization: If you don’t like the default colors in data visualizations such as bar chart or pie chart, I suggest you check out a color palette that is designed for use in data visualization.

The Dataviz Design Process: 7 Steps for Beginners: Steps 2, 3, 4, 5 in this post discuss various issues in the design of a visualization, e.g., reduce clutter, use color to emphasize key findings, write takeaway message in the title, etc.

Data Visualization Charts from the U.S. Congress Floor: The Good, the Bad and the Ugly: critiquing data visualizations used by members of Congress.

Visual Encoding: how to identify your data types and pick the relevant variables.

Communicating your message

Using Visualizations to Tell Stories: Using a series of examples, an instructor at Duke University explains how data visualization can be effective for feature stories, where it can go deeper into a topic and offer a new perspective.

data visualization can be effective for both breaking news – quickly imparting new information like the location of an accident and the number of casualties – and for feature stories, where it can go deeper into a topic and offer a new perspective, to help you see something familiar in a completely new way. – See more at: http://datajournalismhandbook.org/1.0/en/delivering_data_4.html#sthash.h3sWuNy2.dpuf
Sarah Cohen, Duke University
Sarah Cohen, Duke University

Examples of data journalism: Favorite examples by contributors to Data Journalism Handbook.

How the data sausage gets made: A software developer in New York Times newsroom explains a data reporting project from start to finish.

Using Data Visualization to Find Insights in Data: Four steps in analyzing a data set for story ideas: document initial insights, transform data, visualize, analyze/interpret.

Getting Started in Data Journalism: Experienced data journalist Steve Doig shared some tips on getting started in data journalism at the International Journalism Festival in Perugia, taking data “from idea to story.”

Visualization as the Workhorse of Data Journalism: Editors at the Washington Post share six tips for using visualizations to start exploring a dataset.

Basic Steps in Working with Data: Three key concepts you need to understand when starting a data project: (a) Data requests should begin with a list of questions you want to answer, (b) Data often is messy and needs to be cleaned and (c) Data may have undocumented features.

Start With the Data, Finish With a Story: A journalist explains techniques used in digging for a story in EU Commission’s Financial Transparency System.

3 ingredients of effective data visualization: audience, message, the right chart: The purpose of data visualization is to convey messages, not to awe audiences with spectacular visuals.

16 useless infographics: If it’s an image that displays and explains information quickly and clearly, it’s an infographic. The Guardian Data Blog collected some that are head-craning, eye-squinting, eyebrow-raising nightmares that leave you more confused than before you clicked ‘next’. The result is an exciting gallery of infographics that tell you nothing.

Visualization as the Workhorse of Data Journalism
Visualization as the Workhorse of Data Journalism

Tools and tips

Data visualization DIY: Our Top Tools: Data editors at UK Guardian introduce the free data tools they use for their day-to-day work.

List of data tools: A collection of popular digital tools for data visualization and infographics; curated by Journalism Tools.

List of data tools: a collection of tools that staff at Datavisualization.ch work with regularly.

Prepare data for analysis and visualisations: Best practice and tips for creating clean raw data optimised for data analysis and visualisations.

Excel tip sheet: A tutorial on sorting and filtering data in Excel.

A Five Minute Field Guide: Looking for data on a particular topic or issue? Not sure what exists or where to find it? Don’t know where to start? Check out nine proven methods introduced in this article.

How to create a side by side bar chart in Excel: Watch this tutorial video, as well as relevant tutorial videos in the playlist to the right of this video.


Data visualization resources and ideas: Media companies like the New York Times, The Guardian, ProPublica, La Nacion in Argentina and the Texas Tribune have set up vast data-driven journalism archives, dataviz tools and interactives on their websites. These are great sources of ideas and inspiration.

Guide to Internet search: Peruse and then explore this excellent list of international search tools and useful websites compiled by NPR’s data maven Margot Williams, an expert at tracking people, assets, prisoners, planes and just about everything else worldwide.

30 Places to Find Open Data on the Web: A list of resources for finding data; curated by visual.ly.

European Union Open Data Portal: The EU Open Data Portal features a growing range of data produced by the institutions and other bodies of the European Union. Data are free to use, reuse, link and redistribute for commercial or non-commercial purposes.

Global data journalism resources: ijnet.org curated a list of resources that includes data sets, tutorials, examples of great work and more.

Related posts:

About Mu Lin

Dr. Mu Lin is a digital journalism professional and educator in New Jersey, United States. Dr. Lin manages an online marketing company. He also manages MulinBlog Online J-School (www.mulinblog.com/mooc), a free online journalism training program, which offers courses such as Audio Slideshow Storytelling; Introduction to Social Media Marketing; Writing for the Web; Google Mapping for Communicators; Introduction to Data Visualization; Introduction to Web Metrics and Google Analytics.
This entry was posted in Data journalism. Bookmark the permalink.

3 Responses to Teaching data visualization: Recommended readings and resources

  1. Pingback: Data Science: 50+ korisnih linkova za učenje - dusanmilosevic.com

Leave a Reply