Insight. Applied.
As companies start to take data more seriously we are seeing more of them publishing data design guidelines. Three comprehensive examples are
Consider adopting one of these if your organisation is just starting to get into data visualisation—or if your existing charts aren’t up to scratch.
Pandas is a Python package for working with tabular data—as in a spreadsheet or database table. It provides similar functionality to R’s data frames.
As pandas is rich in features it can be difficult to remember all its operations and syntax, so Enthought have produced a visual guide to the package in 8 handy pages.
Andy Kirk at Visualising Data ran a Twitter poll about the relative accessibility of R and Python to non-developers.
59% said that R was more accessible.
Obviously, the poll is far from scientific, but the comments he received reflect my own experiences of teaching both languages—such as the significance of the RStudio IDE and the tidyverse packages in getting people off the ground.
BBC News are using the R ggplot2 package to create production-ready charts—which illustrates the flexibility and power of this package.
What’s particularly interesting is the approach they’ve taken to training their staff to use R. Initiatives include
sharing of tricks and tips through a living cookbook (knowledge base)
a six week course comprising six short introductions with links to online resources
a dedicated Slack channel for course participants to discuss issues or ask for help
an end-of-course project/challenge that exercises the skills developed over the six weeks
The strategies of spreading training over an extended period and embedding it in the workplace have been shown to increase retention and assist in moving from learning to delivering value to the business.
Accessible article from Cloudera describing 15 of the most common probability distributions and their relationships.