— Insight —
In spite of all the success stories, many organizations don't get nearly the value they could from their data science efforts. This article from the Harvard Business Review explores the issues and, in particular, describes how communication disconnects between data science teams and decision-makers are often to blame. This is a good read with well-considered, specific suggestions.
Been Kim, a research scientist at Google, describes a new technique for understanding how much of a specific input went into a machine learning's decision-making process.
— Sponsored Link —
Explore the core dimensions of AI technologies from a business perspective in this online program from MIT Sloan and MIT CSAIL. 6-week online program. Learn more.
— Tools and Techniques —
Awesome introduction to TensorFlow that shows how to build it's core components from scratch. By the end of this tutorial, you'll have an intuitive understanding of TensorFlow's inner workings and will be able to use TensorFlow with confidence. This is very well written and easy to follow.
If you're looking for portfolio ideas, this is a great post about what really matters.
Nice tool for quickly exploring a dataset with SQL. Supports multiple data sources (e.g. PostgreSQL, MySQL, Redshift, etc), variables and email alerts. All queries are tracked and it works with your existing authentication system.
Vettery specializes in tech roles and is completely free for job seekers. Interested? Submit your profile, and if accepted onto the platform, you can receive interview requests directly from top companies growing their data science teams.
— Resources —
This collection of web-based projects introduces machine learning to kids! Projects are based on a widely used educational coding platform called "Scratch" which was designed at the MIT Media Lab for kids ages 8-16. Projects cover tasks in image recognition, text classification, and simple game mechanics.
— Data Viz —
This post from the Uber Engineering Blog introduces a new internal tool for debugging machine learning models. Called "Manifold," the tool leverages visual analytics to help machine learning practitioners optimize their models and identify trouble spots. This post describes the thinking behind Manifold's visual design and how it works. For anyone interested in data visualization, Uber's team is one of the most innovative around.
Nice introduction to the open-source Plotly library. Make great-looking, fully-interactive plots with "a single line of Python."