Disentangling Data Science
Tag: visualization
Came across this awesome Youtube video that blew my mind. Definitely a handy resource if you want to explain the inner workings of neural networks. Have a look!
Reminded me of my other go-to resource when it comes to explaining neural nets, the playlists by 3Blue1Brown:
I’ll surely add these to the other neural network resources I’ve written about on my blog:
Looking for a custom typeface to use in your data visualizations? Google Fonts is an awesome databank of nearly a thousands font families you can access, download, and use for free.
If you’re into design, the website includes a blog featuring articles on font design.
Google Fonts among others provided the font for my dissertation cover so I definitely recommend it.
Past months, multiple companies shared their approaches to data visualization and their lessons learned.
Click the companies in the list below to jump to their respective section
The Financial Times (FT) released a searchable database of the many data visualizations they produced over the years. Some lovely examples include:
The BBC released a free cookbook for data visualization using R programming. Here is the associated Medium post announcing the book.
The BBC data team developed an R package (bbplot) which makes the process of creating publication-ready graphics in their in-house style using R’s ggplot2 library a more reproducible process, as well as making it easier for people new to R to create graphics.
Apart from sharing several best practices related to data visualization, they walk you through the steps and R code to create graphs such as the below:
The data team at the Economist also felt a need to share their lessons learned via Medium. They show some of their most misleading, confusing, and failing graphics of the past years, and share the following mistakes and their remedies:
Moreover, they share the data behind these failing and repaired data visualizations:
I could not resist including this (older) overview of the 52 best charts FiveThirtyEight claimed they made.
All 538’s data visualizations are just stunningly beautiful and often very
ingenious, using new chart formats to display complex patterns. Moreover, the range of topics they cover is huge. Anything ranging from their traditional background — politics — to great cover stories on sumo wrestling and pricy wine.
Saskia Freeke (twitter) is a Dutch artist, creative coder, interaction designer, visual designer, and educator working from Amsterdam. She has been creating an awesome digital art piece for every day since January 1st 2015. Her ever-growing collection includes some animated, visual masterpieces.
My personal favorites are Saskia’s moving works, her GIFs:
Saskia uses Processing to create her art. Processing is a Java-based language, also used often by Daniel Shiffmann whom we know from the Coding Train.
Maarten Lambrechts is a data journalist I closely follow online, with great delight. Recently, he shared on Twitter his slidedeck on the 18 most common data visualization pitfalls. You will probably already be familiar with most, but some (like #14) were new to me:
Even though most of these 18 rules below seem quite obvious, even the European Commissions seems to break them every now and then:
In a world where data really matters, we all want to create effective charts. But data visualization is rarely taught in schools, or covered in on-the-job training. Most of us learn as we go along, and therefore we often make choices or mistakes that confuse and disorient our audience.
Geckoboard
From overcomplicating or overdressing our charts, to conveying an entirely inaccurate message, there are common design pitfalls that can easily be avoided. We’ve put together these pointers to help you create simpler charts that effectively get across the meaning of your data.
Based on work by experts such as Stephen Few, Dona Wong, Albert Cairo, Cole Nussbaumer Knaflic, and Andy Kirk, the authors at Geckoboard wrote down a list of recommendations which I summarize below:
paulvanderlaken.com 