When working with data, should you use Python or R? Why not use both? By being language agnostic, you now have access to two great open source languages. This makes it easier to find and use already built modules/packages, which improves the speed, quality, and impact of your results.
Python is an incredibly powerful language capable of handling all aspects of the data science workflow:
So, why the heck would you introduce R into this framework? Because R is also an incredibly powerful language capable of handling the entire data science workflow. So, why not just use R? A better question is: why not use both? The two languages can be used together to streamline this process. In this talk, you will learn how Python and R can be used to complement each other and produce higher quality results faster.
I'm Kelli-Jean, and I am a SF-based data scientist with a background in statistics and mathematics. I am currently a data scientist at Turo, a peer-to-peer car sharing marketplace. My primary focus has been on improving the search experience.
In my free time, I enjoy hiking, climbing, and hanging out with dogs :).