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A data scientist like a master chef should fully understand the ingredients (data) of their craft before any processing which includes going back to the source and origins of the data.
Learning R, like learning anything else, is a process from which you get what you put into it. In this post, I will argue that learning R should be treated like learning a spoken (written) language, i.e., as a continuous process without a truly defined end. Hereafter I will refer to spoken (written) languages as "spoken languages".
Sometimes after an event you need to issue certificates to the participants. How do you normally produce several certificates automatically? There are different ways to do it and here we share our approach after a recent community event. We used R to automatically generate the certificates using R markdown and LaTeX language.
In this post I have shared some of my thoughts on how data science education can be strengthened. I discussed integrating real life data science practices into learning and development activities especially by engaging subject matter experts and better integration of statistical methods. If you want real global reach then integrate access to Majority World and austerity affected countries into your offerings. By doing so you will also develop.
This time DSup brings you a full-day event! An informal and relaxed event, sharing stories from different speakers, to help students in the transition from academia into their professional life. Find more about this project on our webpage and save your spot for this amazing joint meetup!
Data Science Thinking https://medium.com/@d_spiegel [Articles] R programming https://rweekly.org/ http://r4ds.had.co.nz/ [Free online Book] Python programming https://jakevdp.github.io/PythonDataScienceHandbook/ [Free online Book] Other https://www.datascienceatthecommandline.com/ [Free online Book]