In out current era, even nontechnical people need to have some understanding of data science and statistics. What’s confusing is that so many terms—data science, big data, machine learning, statistics—get bandied about with little explanation. To understand how the pieces fit together, I highly recommend this article from the Win Vector blog.
The R programming language is another tool that is making headlines. From Google’s use of R for analysis to Microsoft buying IDE maker Revolution Analytics, R is all the buzz. What is it? Why use R? The R Bloggers website answers these questions and their email list provides a nicely curated flow of articles that will keep you up-to-date.
For folks who are programming already, the Quick-R page is very useful, especially when you are trying to find equivalent R code for a Stata routine.
As a former graphic designer and communications officer, I’ve spent a lot of time thinking about how to present research. As this nice overview article by the WSJ explains, in an era of Big Data, visualization is going to be a key area of inquiry for firms, researchers and the public.
A good introduction to data visualization can be found on Edward Tufte’s website and in his books (the Visual Display of Quantitative Information is well worth buying and his traveling seminar is also great).
For a more contemporary spin on these issues, the Flowing Data website and book are also worth a look. For true visualization geeks, the Parsons Journal for Information Mapping is a fantastic (and free) publication put out the the Parsons Institute.
If you are working on a project and need a resource to inspire creative approaches to visualization, I highly recommend the Visual Literacy project’s Periodic Table of Visualization Methods.