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Python Overtakes R, Natural Disasters in Data, Inside Google Brain, & The Rise of Data Comics [DSR #101]
My guess is that a bunch of you are going to be at Looker’s JOIN event in San Francisco next week. I’ll be there—shoot me an email and let’s hang out :)
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Focus on Python
Here’s the survey question:
Did you use R, Python (along with their packages), both, or other tools for Analytics, Data Science, Machine Learning work in 2016 and 2017?
For the first time since the survey began, Python came out ahead of R at 41 to 36%. This fact by itself is not that interesting, but the article shows the answer to this survey question plotted over the course of four years, from 2014-2017, and the upward trend for Python is quite consistent.
The point here isn’t that one language is in some global sense better than the other—each language has strengths and weaknesses. It is useful to note, however, that an increasing number of practitioners are, on balance, finding that Python fits their needs better. Each language is an ecosystem, and ecosystem adoption is a feedback loop.
Whether you’re currently learning Python or have used it for years, these exercises will teach you language quirks that you probably didn’t know.
Focus on Natural Disasters
Hurricane Harvey, which dumped an estimated 27 trillion gallons of water on Texas and Louisiana, looks to be one of the most damaging natural disasters in U.S. history.
After absorbing the many tragic photos from Houston, I was really looking for a great data-driven exploration of the topic of natural disasters more broadly. What are the 100-year trends happening here? The Economist delivered: these three charts, all from the same article, do a great job of telling a century’s worth of natural disaster trends concisely.
If you’re interested in a data-driven look at Harvey specifically, FiveThirtyEight has a truly excellent piece on it.
Other awesome articles
Ever wonder what the folks at Google Brain do all day? Here’s an inside look. Seems like it’s kind of like academia, just a lot more GPUs.
The amount of private research funding going into AI is rather stunning, and this narrative just makes that fact more visceral. Brain may be the Bell Labs of this generation.
When I started, I honestly didn’t have any particular skills or capacity which would have made data science a good career choice.
If this sounds like you, this post is a must-read. It’s not about what hard skills to acquire, it’s about the challenges you’ll face and the mentality you’ll need to make it through. Excellent post.
Awesome overview of recommendation approaches, including excellent advice on the implementation process. If this is an area you’re unfamiliar with, this post is a great intro.
I’ve been interested in the intersection of data and comics recently. Turns out I’m not the only one. This project is completely focused on storytelling using data plus the graphical language of the comic. Check it out—whether or not you’re much of an artist, this is something that you’re going to see more of.
How do you deploy your models to production? Excellent overview of different approaches, including recommendations and code.
Giving a talk is a great way to force yourself to meet a deadline, meet people in a community, and to give back. This article is great advice to make that first talk go smoothly.
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The internet's most useful data science articles. Curated with ❤️ by Tristan Handy.
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