Hello, Habr!

About a month ago, I had a feeling of constant anxiety. I began to eat poorly, sleep even worse, and constantly read to a ton of news about the pandemic. Based on them, the coronavirus either captured, or liberated our planet, was either a conspiracy of world governments, or the vengeance of the pangolin, the virus either threatened everyone at once, or personally me and my sleeping cat…

Hundreds of articles, social media posts, youtube-telegram-instagram-tik-tok (yes, I sin) content of varying degrees of content quality did not lead me to anything but an even greater sense of anxiety.

But one day I bought buckwheat decided to end it all. As soon as possible!

Introduction

I happen to live in Montreal, in my condo on the edge of McGill Ghetto. Close to Saint Laurent Boulevard or the Maine as locals call it, with all it's attractions — bars, restaurants, night clubs, drunken students. And once upon a time, on a particular lively night, listening to the sounds of McGill frosh students drunkenly heading home after hard night of studying. I thought, that it might be a good idea to move into my own house, a little bit further away from the action.

Data science is going to revolutionize this world completely in the coming years. The tough question among data scientists is that which programming language plays the most important role in data science? There are many programming languages used in data science including R, C++, Python.

In this blog, we are going to discuss two important programming languages namely Python and R. This will help you choose the best-fit language for your next data science project.

Python is an open-source, flexible, object-oriented and easy-to-use programming language. It has a large community base and consists of a rich set of libraries & tools. It is, in fact, the first choice of every data scientist.

When you think about Jupyter Notebooks, you probably think about writing your code in Python, R, Julia, or Scala and not .NET. Today we are excited to announce you can write .NET code in Jupyter Notebooks.

Try .NET has grown to support more interactive experiences across the web with runnable code snippets, interactive documentation generator for .NET core with dotnet try global tool, and now .NET in Jupyter Notebooks.

In this article I want to show in detail how you can use R in Microsoft SQL Server to get data from Google Analytics (and generally from any API).

The task — we have MS SQL server and we want to receive data in DWH by API

We will use googleAnalyticsR package to connect to Google Analytics (GA).

This package is chosen as an example due to its popularity. You can use another package, for example: RGoogleAnalytic.
Approaches to problem solving will be the same.

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.