In this lab, I learn new things on how to code in python and R. These two languages are pretty popular among data scientists. In particular, I don't know why there is a growing popularity of big data and AI recently and Montreal becomes one of the hubs after Facebook pumped in money to our institutes here.
Now after coding your analyses, you want to present them in a nicely fashion. One way is to use iPython notebook which is now popularly known as Jupyter (Julia-Python-R). While the installation is straightforward, the additional installation of libraries can be tricky. Yeah this is the downside of using Linux/Mac system, you gotta know what you are doing. For example, I remember I couldn't get ezANOVA to work in Jupyter. It is very handy to also learn how to customize this Jupyter notebook. The following link is useful to integrate multiple languages.
https://vatlab.github.io/sos-docs/
There are some drawbacks of using Linux operating system from a Windows/Mac user perspective (at least to me!) One of the most dangerous commands to use is "rm" which basically remove files/folders without putting them to the recycle bin. Others, such as "cp" and "mv" are equally dangerous if not used carefully.
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