When mulling over the best programming language to use for data science, Python and R ring a bell (very quickly). While there are a lot of languages like C, C++, Java, Julia, Perl, and Scala, it's protected to state that Python and R are the harbingers in data science.

While a great deal of data researchers will discuss the customary shortcomings like data wrangling in R or data representation in Python, ongoing improvements like Altair for Python or R have adequately reacted to these shortcomings.

So which one would it be a good idea for you to decide for your next data investigation venture?

R has been ruling this space for a long time now. This bodes well as this programming language was explicitly intended for analysts.

Here is a bit refreshed translation of my 2015 blog post. The post shows how to organize a personal academic library of unlimited size for free. This is a funny case of a self written manual which I came back to multiple times myself and many many more times referred my friends to it, even non-Russian speakers who had to use Google Translator and infer the rest from screenshots. Finally, I decided to translate it adding some basic information on how to use Zotero with rmarkdown.

A brief (and hopefully unnecessary for you) intro of bibliographic managers

Bibliographic manager is a life saver in everyday academic life. I suffer almost physical pain just thinking about colleagues who for some reason never started using one — all those excel spreadsheets with favorite citations, messy folders with PDFs, constant hours lost for the joy-killing task of manual reference list formatting. Once you start using a reference manager this all becomes a happily forgotten nightmare.