Response to: ‘What’s the best way to teach visualization?’

Over on Fell In Love With Data, Enrico Bertini has started a really interesting conversation about the best way to teach data visualisation. This is in contrast to the majority of conversations that typically take place around how best to learn data visualisation.


Enrico has called out for other opinions and experiences with teaching visualisation and, clearly, this is an issue close to my heart and overlaps with another draft post I had started about training a few months. So, rather than just leave my response in Enrico’s comments box I thought I’d duplicate the comments I have about this here.

The first thing to to say is that I entirely agree with Enrico’s outline of his own teaching focus, I think this is entirely the right approach:

The course focusses mainly on visualization theory (mostly perceptual issues and visual encoding) and on the visualization design process. I have two main goals for my students: (1) make sure they can, for any given problem, explore a very large set of solutions (rather than focus on the first one that comes into their mind), (2) predict as much as possible what works and what does not work, that is, design and implement effective visualizations.

Here are some general thoughts and observations about some key dimensions of the demands of teaching:

Iterations: “Every teacher has big questions and doubts about best teaching practices“. Absolutely right. I have delivered nearly 60 events since November 2011 with about 50 different versions (to varying degree) of structure and material. Of course the changes made at the start of this experience were bigger than the tweaks I apply these days but I am still constantly evolving the way I feel is a best approach to teaching (with a big full-on refresh happening this Autumn). This is based on my own instinctive judgment about what ‘feels’ like it works well and doesn’t work as well, it is based on feedback from delegates and it is based on the ever-developing nature of the field, with a constant tap of brilliant new examples, resources and discourse.

Linearity: The way we teach inevitably has to follow a linear sequence. Whilst data visualisation tasks will always differ in their entry points and the amount of iteration between stages of thought, it is nevertheless possible to structure the teaching in a layered fashion that reflects the best-path for a logical workflow process. Building up the thinking from the requirements and preparatory stages, through the data analysis and message forming, through to the chart and design choices and finally to execution. Each stage informs the next. This reduces randomness in approach, removes the need to rely on gut feel and minimises the inefficiencies that can strike any analytical or creative pursuit. However, it should also accommodate uncertainty, creativity and experimentation: factors that are so important to embrace to facilitate innovation.

Craft vs. instructional: “In the end, they would think of information design as a natural language to communicate stories. Just as one speaks or writes, an information designer should be able to doodle visual stories“. This is a quote from Xaquín González Veira of the New York Times discussing his teaching experiences at the SVA in New York. I think it is a great expression to articulate the importance of teaching the craft, the ability to be flexible and capable of finding effective solutions to any given problem context. Of course there are many principles and rules we should impart but when this type of teaching becomes at all dogmatic or instructional I feel this doesn’t prepare a delegate/student sufficiently for the variety of tasks they may face.

Convictions: On the same note, I think it is important to help delegates/students to develop their convictions, help them move beyond ‘like or dislike’ towards ‘I think something is effective because X’ and ‘this doesn’t work because of Z’. If they see something that is ‘cool’, fine, but why is that the case and what are the attributes of the design that lead to that opinion. The difference between right or wrong has an increasingly blurred centre, so a personal conviction about your beliefs and design values is ever more important.

Choices: People are naturally more comfortable in any walk of life when they know their options. I try to achieve this by presenting delegates with a sense of all the key decisions they have to make, the choices that exist and the general guidance for how to make the choices. I also deliberately try to separate out discussions about data representation (chart choices) from presentation (the design execution, colour, interactivity, typography etc.).

Visualisation literacy: Enrico mentioned visual literacy and I would also make a separate point about visualisation literacy. There will be plenty of semantics about language and terms but I define this as the ability of someone to know how to read a chart or graph. I have recently introduced more emphasis and coverage on the need to remind (even train) people about the characteristics of charts and the attributes of graphic portrayal of data that should lead them to draw findings. For example, what are the visible attributes of a line chart that should reveal interesting aspects about some data plotted over time: the high points/low points, seasonality, steepness of a slope, intersections, consistency etc.

Practice: A suitable blend of practice is massively important because experience is the single most important way to develop a skill. There is of course a big difference between what can be practiced in, for example, a one day course (more modular exercises, conceptual ideas/sketching) compared to a full module or programme where there is more time and space to work on longer, more in depth tasks. In my short courses I try to get a blend between exercises that help refine our critical judgments (evaluating other works through specific lenses, explore data to familiarise with the physicality of this raw material and the potential curiosities/stories within, and then work on challenges to conceive potential solutions to a given brief.

Interactions: One of the great benefits of a classroom setting is the opportunity to share ideas and explore opinions between participants in group settings. You can cross-pollinate the unique perspectives each person brings to the table and learn much from each other (including respecting different opinions) working on group exercises.

Delegates/students: The unique convergence of skills and capability required to be the very best of visualisation designers makes the recipe of teaching a fascinating challenge. The different ingredients that go into this mirrors the diverse backgrounds that participants come from seeking to bolster their skills. Some are developers with strong computer science skills, some are designers with a flair for creativity, some are analysts or statisticians with strong maths and data skills, others are managers looking to coordinate others with greater know-how, others might just be customers or consumers of visualisation seeking a more sophisticated eye for effectiveness. The point here is that you never have a single demographic or neat set of characteristics. People are looking for different things and come from different starting points in their journeys. One-size will never fit all but you should certainly be able cast a wide enough net that grabs most.

Tools/technology: This is the hard one. Enrico is absolutely right in commenting on the great influence technology has on visualisation design. Any group of delegates/students will offer a diverse array of skills and capabilities. Some will be technology minded, code-hungry, others will be more concerned with concepts and knowledge. Some will have advanced skills in a given software, others less so. Furthermore, there are scores of different tools, applications and programmes to potentially use. The time constraints of a course setting (one day vs. programme) will also have a great bearing on how much can be covered. My general view is once again to reinforce my central theme: tools evolve, the craft stays the same.

What effective teaching isn’t about: Preaching, grandstanding, concentrating on narrow, singular issues, book-signings…