Stepping up to Big Data with R and Python…

Stepping up to Big Data with R and Python: A Mind Map of All the Packages You Will Ever Need by Abhijit Dasgupta.

From the post:

On May 8, we kicked off the transformation of R Users DC to Statistical Programming DC (SPDC) with a meetup at iStrategyLabs in Dupont Circle. The meetup, titled “Stepping up to big data with R and Python,” was an experiment in collective learning as Marck and I guided a lively discussion of strategies to leverage the “traditional” analytics stack in R and Python to work with big data.

[images omitted]

R and Python are two of the most popular open-source programming languages for data analysis. R developed as a statistical programming language with a large ecosystem of user-contributed packages (over 4500, as of 4/26/2013) aimed at a variety of statistical and data mining tasks. Python is a general programming language with an increasingly mature set of packages for data manipulation and analysis. Both languages have their pros and cons for data analysis, which have been discussed elsewhere, but each is powerful in its own right. Both Marck and I have used R and Python in different situations where each has brought something different to the table. However, since both ecosystems are very large, we didn’t even try to cover everything, and we didn’t believe that any one or two people could cover all the available tools. We left it to our attendees (and to you , our readers) to fill in the blanks with favorite tools in R and Python for particular data analytic tasks.

See the post for links to preliminary maps of the two ecosystems.

I like the maps but the background seems distracting.


Comments are closed.