With recent major updates in two core packages, the tidyverse has substantially improved in the flexible options it offers for data wrangling. Here are five examples of what I mean.
I first heard of Learning Through Play when I sent my kids to pre-school, but now I realize it’s how all Data Scientists should learn
The rate at which data is being generated by business and the world at large is rapidly exceeding the speed at which leaders are equipping themselves to work with data.
Failure to follow these words can put patients’ well-being at greater risk, and can open individuals up to ethical and legal challenges about their decision-making. The three words are evidence based practice.
There was once a business executive who kept coming home troubled and distracted. His partner asked him one evening, “What’s the matter? Something’s worrying you.”. “There’s so much data”, he replied, “and I can’t understand it or get my head around it.” “You could try hiring a data scientist”, suggested his partner. So he did just that. Six months later he was still coming home troubled, and when his partner inquired again what was up, he replied: “There’s so much data, and I can’t understand what my data scientist is telling me about it”.
I struggled to keep it to ten last time, so here’s ten more for you
A great little learning exercise that illustrates the range and flexibility of the R language
The things they never tell you in statistics classes
With the right process, you can keep it in check
Put the time and effort in early and it will make you a great programmer later on