— In the News —
This in-depth study by the MIT Sloan Management Review shows how organizations from a wide range of industries around the world are innovating with analytics.
Everything is becoming "intelligent." Soon we will not only have smart phones, but also smart homes, smart factories and smart cities. Should we also expect these developments to result in smart nations and a smarter planet?
If you've tried to hire a data scientist recently, you know it's no easy task. In this post, the Wikimedia Foundation shares their approach for successfully attracting qualified candidates, interviewing them, and ultimately hiring a new member of their team. Includes lots of detail.
— Tools and Techniques —
Bare bones Python implementations of some of the foundational Machine Learning models and algorithms. Even the matrix operations are performed "by hand" to show how they work.
Facebook recently open-sourced its forecasting tool, Prophet, for producing reliable forecasts at scale. It's available in R and Python, is fast, and is used by Facebook in production for planning and goal setting.
Practical introduction for creating Python-based data science projects. Starts with project structure and continues with deployment and maintenance.
— Podcasts —
In this episode of Software Engineering Daily, Dave King from Exaptive explores the importance, challenges, and future of domain-specific data applications.
— Data Viz —
Step-by-step tutorial for creating a variety of data visualizations that show different aspects of home prices in San Francisco. Whether you're interested in real estate or not, this is a nice exploration of different ways to portray the same dataset.
ggraph is an extension of the ggplot2 API that supports relational data such as networks and trees. This article explores key features of the project with links to in-depth tutorials. This is very well done and highly recommended for people creating network based visualizations in R.