— In the News —
AI-powered apps will be a gold rush for entrepreneurs. Here's a survey of the landscape.
How much of what data scientists do could be automated? This is a nice overview of the problems and opportunities.
Great exploration of a postdoc's path to a non-academic job. By meticulously tracking how she spends her time, Alex Smolyanskaya provides valuable insights into the differences between academia and industry.
— Sponsored Link —
Sebastian Raschka's new book, Python Machine Learning, has just been released. I got a chance to read a review copy and it's just as I expected - really great! It's well organized, super easy to follow, and it not only offers a good foundation for smart, non-experts, practitioners will get some ideas and learn new tricks here as well. As if that weren't enough, Data Elixir Readers save 50% by using code PML50 through October 15.
— Tools and Techniques —
Fantastic Reddit AMA with Hadley Wickham. There are lots of useful insights, links, and discussions here. Highly recommended.
Which is better: a baseball player who's achieved 4 hits in 10 chances or one with 300 hits in 1000 chances? This post isn’t really about baseball. It's about a very useful statistical method for estimating a large number of proportions, called empirical Bayes estimation. This is a fantastic explanation.
Here's a gentle introduction to neural nets with an interactive visualization to help show how they learn.
Great Data Journalism tutorial by Lena Groeger. This flows very well and includes many useful references and examples.
— Resources —
Nice list of 150+ blogs, organized across eight different categories: machine learning, Hadoop, R, Python, business intelligence, data visualization, statistics, and a general data science bucket.
This set of Notebooks is written for scientists and engineers who want to use Python for exploratory computing, scripting, data analysis, and visualization. Each notebook covers a specific topic and includes exercises, data, and an accompanying video.