— In the News —
Data science doesn’t just help women advance their careers, it secures their footing at the top. The result is better-performing businesses, accelerated social change and a global economy that works better for all of us.
Fascinating story about how a retired data guy is tackling unsolved murders, using data that nobody’s bothered with before.
This week, the world lost Hans Rosling, a data visionary whose TED Talk, The Best Stats You've Ever Seen, quickly became a classic in 2006 and has since had more than 11 million views. This post by Robert Kosara highlights Rosling's achievements and includes links to key talks and projects.
— Sponsored Link —
Get a data science job or your money back with Springboard's new data science career track. It's the first flexible, online bootcamp of its kind to offer personalized mentoring from data science experts, individualized career coaching, and a job guarantee. Apply now to see if you qualify.
— Tools and Techniques —
This series by Tony Ojeda provides a framework for exploring data with Python. In this part, the goal is to create additional categorical fields that will make data easier to explore and allow it to be viewed from new perspectives.
StreamAlert is a real-time data analysis framework with point-in-time alerting. It's unique in that it’s serverless, scalable to TB’s/hour, has automated infrastructure deployment and it’s secure by default. This is an open source framework from the Engineering & Data Science team at Airbnb.
Part three of a popular series from the Machine Learning at Berkeley organization. This part explores neural networks: what they are, how they work, and how to train one. This series relies a lot on visuals to describe concepts, rather than math or code.
Google recently added a new feature to its Maps for Android application that offers predictions about parking availability. This involves a lot of challenges, including variable parking availability, lack of real-time info, and multi-level parking structures. This post on the Google Research Blog describes their approach.
Fun exploration of applying deep learning to Chess. This is easy to follow and includes a GitHub repo of code.
— Resources —
This collection has some great picks with books in a variety of categories including analytics, interviews, distributed tools, Python, R, SQL, NoSQL, machine learning, AI, data visualization, and math.
— Career —
Great Reddit discussion about what a data science career really looks like.