These articles cover the fundamentals: data, techniques, statistics, etc.
Visualization techniques encode data into visual shapes and colors. We assume that what the user of a visualization does is decode those values, but things aren’t that simple.
Our mental model of a dataset changes the way we ask questions. One aspect of that is the shape of the data (long or wide); an equally important issue is whether we think of the data as a collection of rows of numbers that we can aggregate bottom-up, or as a complete dataset that we can slice top-down to ask […]
The shape of a dataset is hugely important to how well it can be handled by different software. The shape defines how it is laid out: wide as in a spreadsheet, or long as in a database table. Each has its use, but it’s important to understand their differences and when each is the right choice.