What data viz skills do you want to build?


We’re conducting a short survey on data viz training needs within the global development community, and would love if you would share your ideas by answering four questions.

The survey results will be used to better curate content for the Data Viz Hub, including sharing details about remote and in-person training workshops, as we reinvigorate this website as a resource for data viz enthusiasts in the development sector.

Data Science Bootcamp: learning R and Shiny

From the Data Education DC team:
“Our sponsor, Data Society, has 2 new courses open as part of its online Data Science Bootcamp!
2. Interactive visualization and visualization applications using Shiny (also in R, using javascript D3 interactive libraries)
Want to learn how to manipulate data faster and create beautiful, impactful visualizations? These courses are for you, at an introductory rate of just $50 for both online courses! Start with a free trial today!
You will have access to:
1. Real data sets
2. Dynamic videos
3. Downloadable step-by-step instruction guides
4. R code templates
5. Community forums so you can ask all your questions
Stay tuned for many more courses coming in the next couple of months!”

Why NOT to use pie charts and other fun tips: An introductory course on Data Visualization

Have you ever seen an infographic that was beautiful, but didn’t have a clear message? 

Or wade through a bunch of tables but struggle to see any meaningful patterns and trends? 

Ever see a chart you thought was terrible, but couldn’t figure out how to fix it? 

The Global Health eLearning platform’s recently released introductory course on data visualization tackles it all in a 4 step process: identifying your audience and their context, finding the story in your data, building your visualization, and dissemination and use.

The course pulls from the current expertise in data visualization, from Tufte to Evergreen, but repackages it with a global health perspective and with a beginning learner in mind.  Global health projects worldwide are expected to make data-driven decisions, to monitor and evaluate their progress, and to report successes to governments and funding agencies.  Basic data visualization skills can enhance individual’s and organization’s abilities to analyze and present their data, leading ultimately to better data use and more efficient global health programming.

Through a realistic case study and a set of commonly used World Bank population indicators, the course introduces concepts such as numeracy, drafting data headlines, smart use of preattentive attributes, matching data stories to the appropriate graph type, and working with a diverse team of experts to develop more complex visualizations such as infographics, maps, and data dashboards.

Full of current global health examples, learners will see successful uses of data visualization in our field, and finish the course armed with practical guidelines and tools that can be used in their next data visualization project.


On spurious correlations

When this came through my inbox this morning, all I could think was, “How have I never found this blog before?” Harvard Business Review has a new article – “Beware Spurious Correlations” – that features the absurd (like an example of how iPhone sales correlates visually with U.S. deaths from falling down stairs) and the serious implications of what that means for those visualizing data and inferring unproven causal relationships.

The teaser from HBR:

“We all know the truism “Correlation doesn’t imply causation,” but when we see lines sloping together, bars rising together, or points on a scatterplot clustering, the data practically begs us to assign a reason. We want to believe one exists.

Statistically we can’t make that leap, however. Charts that show a close correlation are often relying on a visual parlor trick to imply a relationship. Tyler Vigen, a JD student at Harvard Law School and the author of Spurious Correlations, has made sport of this on his website, which charts farcical correlations—for example, between U.S. per capita margarine consumption and the divorce rate in Maine.”

Read the article for the full explanation, but all to say, be thoughtful about how you visualize your data – don’t fall prey to misleading with charts and graphs.

Applying human centered design to data visualization

Jeff Knezovich over at On Think Tanks posted some great reflections from his recent trip to the Cartanga Data Festival, breaking down why data viz isn’t just a science but also an art. Data science alone, with its emphasis on statistics, code, and often technology, can’t develop the kind of simple yet artful visualizations that we find on feature blogs like Information is Beautiful or in reports to Ministries of Health that effectively advocate for new health facilities.

One of the highlights of his post was insight into how he approaches data visualization training and design as a discipline that requires expertise in research, technology, design, and communication.  Jeff unpacks (with some great resource links!) the importance of design from a visual and graphical sense, but I would argue that data viz design requires a certain level of understanding of the human experience of interacting with information. Who is your audience? How do they interact with information? What is their level of numeric literacy? How much do they care about the information you’re trying to communicate?

My team has been exploring human centered design (HCD) methods through our work on the Innovations for Maternal, Newborn,, and Child Health Initiative*. At the core, HCD focuses on developing an empathy with the beneficiaries of a program. In visualization design, identifying an audience for your visualization and keeping them at the center of your design process is key to creating something that makes information meaningful.

Applying these principles of design need not be onerous or feel intimidating for data visualization designers (though the facilitation guides and experts in this space can go deep in more involved program design). Next time you’re crafting something visual from a data set, think about these three things:

  1. Who am I creating this for? As yourself this question repeatedly throughout the design process, not just at the very beginning. Understand both what they say they need from your analysis, but also their latent needs and expectations. If you’re working on a more complex project, like developing a dashboard, creating personas for your different users could be very helpful.
  2. Prototype (sketch!), test, and iterate. Don’t be afraid to ask for feedback from your users or at the very least your colleagues throughout the design process. And don’t be afraid to make changes!
  3. How will my audience use this product? How will your audience feel when they see your graph, chart, infographic, video, or dashboard? How they will interpret and use the data analysis you’ve presented? These considerations are key to ensuring your visualizations are used to promote evidence-led decision making.

Have you deliberately applied principles of human centered design in your data viz design? Share your experiences & learning in the comments!

*The Innovations Initiative is led by Concern Worldwide and funded by the Bill & Melinda Gates Foundation. JSI serves as the global research partner for the project.

A Round Up of Thought Provoking Viz from #ttdatavis

Direct from Jeff Knezovich via the Evidence-Based Policy for Development Network (EBPDN):

I’m pleased to let you know that earlier this week, at the Cartagena Data Festival in Colombia, On Think Tanks launched the 2014–15 compilation of the #ttdatavis competition. The compilation, and the competition more widely, aims to inspire think tanks and similar organisations by showcasing real world examples of impactful data visualisation. It also contains useful resources and ‘how tos’ to support think tanks to develop their own visualisations.

This year’s compilation is available as for free download as an interactive eBook (408 MB), which is also available in the iBooks store, as well as a downloadable PDF (100 MB). It includes 46 entries, which emerged from 31 think tanks spanning 19 countries around the globe.

The topics of the visualisations cover a lot of ground. The second round of our competition coincided with the COP20 climate negotiations in Peru, which meant we had quite a few focused on climate change and the environment.

Think tanks may have similar goals and objectives, but this competition clearly demonstrates the wide array of approaches think tanks have toward meeting those goals. We saw both static and interactive visualisations, to be sure.

But beyond that, some took a clear message-driven approach while others developed tools that let the user understand the data more clearly. And while some sought to tell stories about their research, others used visualisations to increase explain government actions (or proposed actions) pushing greater transparency and accountability. And others found success by combining otherwise disparate data sets.

It’s a broad collection that any think tank can find inspiration in.

Following last year’s successful competition, we made several changes to how it was organised this year. Most importantly, we divided the rounds based on the type of entry. The first round was open to static visualisations. The second round was for interactive visualisations. And the third and final round was for the best example of a data visualisation as part of a communication strategy.

We also opened up the competition to any think tank around the globe.

The final resources section includes ‘how tos’, which combine video, images and text, on three main areas: data collection, data cleaning and manipulation and data visualisation. Tools explored include Import.io, Google Drive and Google FusionTables, Excel, Tableau and CartoDB.

Global Health Mini U Resource Round Up #2

For those who have been eagerly awaiting resources that we used in our session, here they are, along with a few bonuses!

From our activities:

To learn about how storyboarding can help you visualize data as a team, check out the this summary from Amanda Makulec (JSI). Participants in the session talked about how the process helped to underscore the importance of defining your audience up front in order to drive what you pick as your key messages, and that the exercise was a great way to collaborate with a team to create a common vision for a visualization product.

For more on simple data viz best practices, check out this Building Your Viz Checklist from Erica Nybro (DHS Program) that can help you evaluate your visualization. It’s amazing how much you can improve a graph or chart with a few simple tweaks like decluttering by deleting excess lines and tick marks or using color strategically.

Bonus! And in case you’re interested in more resources, check out our Love Your Data and Free Viz Tools handouts from earlier presentations. For a more detailed deep dive on viz, check out the Data+Design eBook.

A space for communicators, evaluators, and other viz enthusiasts to connect, learn, and share resources.