Author: Kurt A Cagle
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What Computer Languages Should You Be Learning for Data Science?
Computer languages over the years tend to rise and fall in popularity, depending upon what the job market looks like, what’s the hot technology du jour, and what needs it fulfills. There was a time in the not-so-distant past when Ruby on Rails was the must-have language out there, yet Ruby now seldom cracks the top 20 languages in most people’s surveys. I can even remember a time when LISP was the dominant language in the artificial intelligence space, though you’re more likely today to find LISP only as faint echoes in languages like Erlang and Clojure.
If you look through older articles on DSC you’ll find plenty of fodder about whether R or Python is the better language to learn, though by the numbers Python looks to be eclipsing R finally in the great language religious wars. However, the reality is that in the analytics space, your language choice is becoming less and less relevant.
This is partially due to the fact that most language vendors (and database vendors) are increasingly incorporating analytics libraries and extensions into their respective products and communities, especially in those places where you have the ability to compile these libraries into high-performance code.
These issues and more are covered in this week’s digest. This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.
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