— Profiles —
One of the greatest minds in 20th Century statistics was not a scholar. He brewed beer.
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— Tools and Techniques —
This deep dive into the differences between Google Maps and Apple Maps shows how Google successfully creates data from data and uses that to maintain a massive lead.
Along with a review of the basics, this tutorial shows how NumPy is well-suited for RAM-sized general-purpose numerical data applications, such as manipulation, collection, and analysis. This is very well done and covers a lot of ground.
Part 3 of Jeroen Boeye's series on solar panel analysis is amazing. In this part, he models the expected output of the panels over the course of a year, compares that to the actual output, filters out noise caused by clouds, and then infers the location of nearby trees. This is a fantastic walk-through, including lots of code snippets, visualizations, and discussion.
For those times when you have a quick idea to try and need access to R while on the go, here's how to install and use R natively on your Android device.
This post on LinkedIn's Engineering Blog walks-through how they create their Salary Product in spite of limited data. It explores the design and architecture of their statistical modeling system and discusses how they handle the competing needs for user privacy, product coverage, and providing reliable compensation insights.
A reading protocol is a set of strategies that a reader must use in order to benefit fully from reading the text. Poetry calls for a different set of strategies than fiction, and fiction a different set than non-fiction... Mathematics has a reading protocol all its own, and just as we learn to read literature, we should learn to read mathematics...
— Blogs —
Last month, I announced that I'd link to blogs from Data Elixir readers and the response has been overwhelming. There are a lot of blogs out there! Rather than just list them all with links, I've decided to highlight a few every week so they don't get lost in a list. There's a wide variety, both in style and content, and hopefully, that will inspire and encourage some ideas. Here are the initial picks:
— Deep Learning —
Denny Britz's year-end review of the AI and Deep Learning landscape is one of the best reviews you'll find. This is a great overview of the space, including key technical achievements, projects, industry news, and lots of linked references.
— Career —
Hilary Mason received 24 requests for career advice over the holiday break. The theme: junior data science roles are hard to find and evaluate. If you're interested in developing a career versus having a job, the Twitter thread that resulted is pure gold.