— In the News —
There have been a lot of articles recently about the EU’s General Data Protection Regulation (GDPR), which goes into effect on May 25. This is one of the most useful guides I've come across. Includes discussion of user rights, where responsibility lies in an interconnected world, a glossary with human-readable summaries of the legalese, and linked references.
Professor Ajay Agrawal is a world-renowned expert in the business of artificial intelligence. In his new book, Prediction Machines, he posits that the real value of AI is that it lowers the cost of prediction. This article in the McKinsey Quarterly explores his ideas and how they can be used by business leaders to identify and prioritize AI opportunities.
— Sponsored Link —
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— Tools and Techniques —
This new xkcd comic will make you pine for R.
Quantitative methods can tell you the “what,” the “how much,” or “how often.” Qualitative methods can tell you the “why” or the “how.” The distinction is important and understanding how to use qualitative methods to find those insights can greatly improve your models.
Making predictive models "fair" requires more than just removing sensitive attributes from the training data. Training data is often deeply biased and it may require using techniques that help force models to make fair predictions. This post clearly describes the issues and shows how adversarial networks can help bring fairness to predictive models.
This SQL Tutorial from Mode is awesome and may be the best you'll find on the web. Includes lessons for all skill levels from beginner to advanced.
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— Data Viz —
Jonathan Corum is the science graphics editor at The New York Times and is the founder of 13pt LLC, an information design studio. He's not a data scientist, exactly. But he has a knack for transforming data-intensive subjects into graphics that are easy to understand. In this post, he explores his process for finding visual stories in the data. This is a great read.
— Career —
"data analyst," "business analyst," "business intelligence analyst," "statistician"... the list goes on and on. This short read from Nicholas Chamandy, the Scientific Director at Lyft, describes the importance of getting the titles right and why Lyft is rebranding its data science roles.
Nice collection of tips and resources for people starting out in AI careers.