With the technology at our disposal today, we are trusting computers to do more and more everyday things for us. While some may be skeptical, there isn’t anything as such wrong with this as the more data we generate, the more precise the algorithms powering these computers will become. However,
Tag: Data Visualization
For those of us working with data, we are all too familiar with how difficult it can be to fully understand what we are presented with. No matter how complex, data can be difficult for us to digest and make sense of. Data visualization is an effective technique that can
In data science, the theory in practice is not always the same as reality. When working with data, it’s not uncommon to be presented with several complex problems. Fortunately, you are not alone and there are blogs, slack channels, and useful information to come to the rescue. Plenty of problems
Why we should choose representative samples with error in mind when we build data visualizations. A brief overview of uncertain bar charts and uncertain ranked lists.
The type of data samples that populate our visualizations can add uncertainty to our results. Some common data displays like bar and pie charts work better than others for making that uncertainty understandable. This article explores how to understand our data samples and create the most suitable graphs for visualizing what they represent.
In general, the goals of data science are to understand data and generate predictive models that help us make better decisions. For a more thorough overview of data visualization, see “Data visualization and The Truthful Art.”
An amazing book about data visualization that I can’t recommend enough is The Truthful Art by Alberto Cairo.
In The Truthful Art, Cairo explains the principles of good data visualization. He describes five qualities that should be your foundation when you work with data visualization: truthful, functional, beautiful, insightful, and enlightening. Cairo also gives some great examples of biased and dishonest visualization.
Before I dive into the “Five Qualities of Great Visualizations,” there’s another related concept that I want to cover: data-ink ratio, introduced by Edward Tufte in The Visual Display of Quantitative Information.