— In the News —
"There are a lot of people currently running data science teams at large organizations and the vast majority of them — I believe we are talking 80-90% — want to leave their jobs."
That's a problem. This article by Edward Chenard explores the issues and offers suggestions for addressing them. And if this topic strikes a chord with you, be sure to check out, Are You Setting Your Data Scientists Up to Fail? from the Harvard Business Review.
Starting on May 25th, a stringent new set of data privacy rules in Europe will govern what can and can't be done with personally identifiable information. This article in the New York Times takes a look at how Europe's "General Data Protection Regulation" will impact life on the Internet around the globe and how tech companies are getting ready.
The title says these picks are for "engineers" but check out the trailers before passing judgement on these. If you're looking for something worthwhile to stream tonight, these 2017 movies are all top choices for anyone doing interesting things in tech.
— Sponsored Link —
Everyone sets out to learn data science for different reasons—whether it is to make a career change, get a promotion, or out of curiosity. For one Springboard alumni, the choice to start the Data Science Career Track was out of intellectual curiosity, but it ended in the certainty that he wanted to become a data scientist. Check out what alumni Rounak has to say about Springboard’s Data Science Career Track, and how it gave him the skills and mentality needed to succeed in data science.
— Tools and Techniques —
This step-by-step guide for extracting information from text data is one of the best you'll find. It's very well written and it includes an interactive notebook, helpful graphics, and lots of links to other resources.
Kaylin Pavlik collected text reviews for 30,000+ beers and then analyzed them using R and tidytext. This is a fantastic writeup of her approach, including code, visualizations, and her discoveries along the way.
This latest article from Google's People + AI Research Group (PAIR) explores how finding ways to augment human capabilities, rather than just making machines "smarter," actually unlocks greater potential in the machines. If you're interested in human-computer collaboration, the entire series is great.
This roundup of top R packages that Joseph Rickert puts together is definitely worth spending some time with. This latest issue includes packages for a variety of uses, including computational methods, machine learning, visualizations, and statistics.
Here's a pure Python implementation of a neural-network based Go AI, using TensorFlow. It's not the official DeepMind project but it strives to replicate the results of the AlphaGo Zero paper from Nature and it looks like a great project to learn from.
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— Data Viz —
Data gaps are important and sometimes, they're more important than the data itself. Nathan Yau's latest post demonstrates a variety of ways to effectively show the gaps.
Claus Wilke's latest project is a data visualization book that will be published by O'Reilly Media. In the meantime, drafts of completed chapters are being posted online. This looks like a useful resource and if you typically work in R, the examples are all done with ggplot2.
— Career —
Great thread and growing fast.