Content to stay tuned to

There is lots of content that you can look at to stay tuned: job announcers, blogs, podcasts, social media, and academic journals. Through this section, expect to meet sources for all these kinds of content. It's up to you to decide which ones you will benefit from.

When it comes to methodologies, packages, and algorithms, a great place to see what the market is looking for is the R-users website: https://www.r-users.com/.

Jobs are announced on this web page. Announcers will tell visitors which kind of professional they are looking for along with which packages they wish them to already have mastered and much more useful information: what the company does, tasks related to the job, and so on.

Consistently tracking R-users and Kaggle will respectively give you the heads-up of what the top-performing practices/methodologies are and which abilities, skills, and libraries are most valued by organizations. Kaggle also has a blog where top scorers share how they were able to get there. Blogs are yet another source of knowledge.

Consider creating a blog of your own. Posting the learning process is not that hard, and it may help your career take off.

For those who wish to maintain blogs of their own, my tip is blogdown. There is great free material from which you can learn from: https://bookdown.org/yihui/blogdown/.

Whenever the topics R and blog come together, it's natural to think about R-bloggers. While I was writing this, the blog had over 750 contributors and countless tutorials. It's frequently updated, so you can keep up with what is going on in the R-world while learning a little something now and then: https://www.r-bloggers.com/.

News are also very likely to come up from RStudio's blog, so, here is a link to it: https://blog.rstudio.com/

RStudio is one of the reasons why R is so popular, not because they are disclosing the novelties but because they are the ones to bring them to life in the very first place. RStudio's folks are steadily improving the functionalities of R, either by maintaining important packages, developing new ones, or developing powerful tools, such as developing an absolutely incredible IDE.

My favorite blog by far is Bob Rudis's blog: https://rud.is/b/.

His work is amazing. There is another great blog that I highly recommend; it's called Simply Statistics or Simply Stats for short: https://simplystatistics.org/.

This blog is owned by three amazing guys: Jeff Leek, Roger Peng, and Rafa Irizarry. All of them are seasoned, capable biostatics professors. Not every post will be about R; nonetheless, expect to see lots of interesting things such as Peng's comments on an episode of Law and Order, or how Irizarry doesn't like violin plots.

Data science is not exactly new; statisticians were doing it well before R. While knowing how to code is crucial, lacking statistics will most likely destroy you, seriously.

If you're the kind of person who likes R, gaming, or sports, you may like to check my blog, ArcadeData, R-Cade-Data. I and my partner, Mr. Ricardo A. Farias, use data science to talk about games, sports, and e-sports: http://arcadedata.org/.

Building a routine to periodically check the blogs that you like is great. Yet another way to keep up with the blogs is to stay tuned to important R people in social media. Usually, there is a lot to learn from simply following good folks. I would never guess that I would learn as much from tweets as I did.

Here is a list of 10 R personalities (on Twitter):

  • Bob Rudis (@hrbrmstr)
  • Hadley Wickham (@hadleywickham)
  • Mara Averick (@dataandme)
  • Dr. Alison Hill (@apreshill)
  • Renee M. P. Teate (@BecomingDataSci)
  • Thomas Lin Pedersen (@thomasp85)
  • Jesse Maegan (@kierisi)
  • Jeff Leek (@jtleek)
  • Roger D. Peng (@rdpeng)
  • Rafael Irizarry (@rafalab)

You can find some of us there as well: 

  • Vitor Bianchi Lanzetta (@vitorlanzetta)
  • Ricardo Anjoleto Farias (@R_A_Farias)

R users usually adopt the hashtag #rstats; consider deploying it the next time you talk about R on Twitter.

Do you enjoy listening to podcasts? You can listen to them and learn. These podcasts will talk about data science in general and eventually mention R, but they are all good:

Perhaps the best idea is to try a few episodes from each of these podcasts, selecting the ones that you like the most and then building a routine to frequently listen to those.

YouTube channels are also great to learn new things and answer any questions you may have: 

The last channel in the list, Linus Tech Tips, is not exactly about data science, but if you like, the idea of building and maintaining your own gear (PC) or a server all by yourself, Linus has your back, especially if you are a scientist by day and a gamer by night.

The last source of knowledge I want to talk about is academic journals. There is no rule, but you could expect novelties and breakthroughs to come first from journals. The following is a list of five journals I recommend reading:

Normally, people don't read all the articles from a journal. Checking the issues and picking one or two articles of your liking is more doable. Also, reading a scientific production can be hard sometimes if you're not used to it, but, believe me, it's only a matter of getting into the habit of doing so.

So far, we have discussed things such as distinctions between data scientists' roles, how to get data, how to discover what the organizations are looking for, which kind of materials can help you keep updated and learn even more. The paragraphs ahead are about to introduce the usage of Stack Overflow, a powerful platform responsible for uniting portfolio, clarification, and jobs all in one.

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