— In the News —
How Google used artificial intelligence to transform Google Translate and how machine learning is poised to reinvent computing itself. This is a worthwhile and very popular long-read from the New York Times.
Outright manipulation may be unlikely but there are subtler things the administration could still do.
Deep Learning has been a key topic in the Machine Learning community for the past few years. This article takes a look at some of the most important advancements in 2016 and how they're being used. There's a lot here and it's a great starting point for further exploration.
— Sponsored Link —
Mode is a SQL editor, Python notebook, and visualization builder all rolled into one. Explore data with SQL and pass results instantly into a Python notebook for further exploration and visualization. Pick and choose output cells to present to others, or send the whole notebook—you can even share with people who don't have a Python environment set up.
— Tools and Techniques —
Simple and practical advice for managing your data science projects.
Numpy is essential for doing numerical computing with Python. This collection of simple examples demonstrates how to use each of numpy's functions.
Nice overview of Scala from a data engineering perspective. Covers where things are with Scala, where things are going, reasons to use it, and reasons to avoid it. If you use Scala now or have been curious about Scala, this is definitely worthwhile.
— Podcasts —
Great interview with DJ Patil, the U.S. Chief Data Scientist. "Remarkable things happen in the 4th quarter. Don't think that we're done."
— Data Viz —
In May, Lisa Charlotte Rost wrote a popular set of articles that compared as many data visualization applications, libraries, and languages as possible. Here, she takes a higher-level view and compares all the tools against each other.
Designers from Accurat Studio provide a peek behind the scenes and explain how they developed a data visualization rooted in an analysis of myths across space and time.