It seems that the amount of intelligence needed to understand an idea is inversely proportional to its brilliance. My first conjecture: great ideas are grokkable, marginal ideas are not.
In this second part of the series, we explore how to be more effective in your visualizations. In this context, we defined effectiveness as a combination of clarity and engagement.
This is part one of a two-part series on building effective visualizations. In this post, we take a shallow dive into evaluating existing visualizations. In the next post, we’ll dive a little deeper as we explore techniques on how to improve them.
If you aspire to be a data scientist, you’re really aspiring to be a data wrangler. You see, 80% of your working hours will be spent wrangling the data. That’s on average. On some projects, you will spend more than 100% of your “working” hours with your lasso. I hope you enjoy that sort of thing.
Interactivity can provide whole new perspectives, facilitate more comparisons, and encourage exploration and discovery. See what stories are revealed in our new interactive NHL visualization.
People highly value style, even to their own detriment. That means we need to value style but execute without causing detriment. We need to find a way to ensure that we not only deliver data in a meaningful way but also seek to deliver it in a compelling/engaging style.
Over the past few months I've become increasingly fond of Edward Tufte's Slopegraphs for data visualization. They aren't just good for comparing across time, but also across two categories.
Recently a small data set was posted on a discussion board I read. 5 columns by 15 rows. This small bit of data yielded an immense number of opinions and visualizations.