I always liked data crunching, databases and data topics in general. Also, most of the software development knowledge I have I didn't learn in the official school curriculum, but rather from books, online courses and real-world experience.<p>Now, having that in mind, how realistic is for a guy in mid-30s, a very good software developer (enthusiastic about functional programming, if that matters) to pick up enough knowledge about data science to be able to actually take the data-scientist role in some company?<p>Edit: Rephrase the question.
> It makes me sad when brilliant software engineers open up Excel to make a line graph!<p>Why do people get religious about tech.. funny, let him/her use excel for god sake its a great tool :)
> public work is not a waste of time<p>This is the best line in the entire (interesting) post.<p>Doing some kind of public work will make your next job hunt so much easier. Whether that is SO answers, a blog, or a github profile, showing bests telling.
> For example, if you visit mostly Python and Javascript questions on Stack Overflow, you’ll end up getting Python web development jobs as advertisements<p>But what if you are an excellent C++ developer, who needs some assistance with Python and Javascript?
I just read your R course lessons and I find it very well explained, I enjoyed the lessons about data.table and ggplot2.<p>The beta distribution: In the free book "think stats Probability and Statistics for Programmers" there is a chapter about how the beta distribution can be used as a prior to model an unknown probability and how Bayes' Theorem allow us to update that prior with a posterior distribution that is also a Beta distribution, that important property is called the self-prior property of the beta. Since the two parameters of the beta in that intuitive explanation are just the number of experiments (battings) and the number of successes (runs) that example constitutes a very intuitive and clear way to explain what is Bayesian Statistics. I think that you would enjoy the think stats book, it is aimed for programmers and it tries hard to enhance intuition.<p>I also enjoyed how you describe the atmosphere in your office, it seems that you work in a lovely place in which statistics is a well respected tool and people try to explore and innovate in a fun way without excessive pain.
Nice post, I enjoyed it.
> For that, I might look at another source of data, Stack Overflow Careers profiles, and see which technologies tend to be used by the same developers<p>> <a href="http://varianceexplained.org/images/network2.jpeg" rel="nofollow">http://varianceexplained.org/images/network2.jpeg</a><p>This shows "git" and "github" in a separate cluster from "C++" and "Python"? I don't understand this. These tools are used regardless of what other technologies are being used. For example, there are many Python and many C++ projects on github.