A Schur Complement Cheeger Inequality

Last year we discussed the story of an article, “Variation in the β-endorphin, oxytocin, and dopamine receptor genes is associated with different dimensions of human sociality,” published in PNAS that, notoriously, misidentified what a peptide was, among other problems.

A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics

Fog computing serves as a computing layer that sits between the edge devices and the cloud in the network topology. They have more compute capacity than the edge but much less so than cloud data centers. They typically have high uptime and always-on Internet connectivity. Applications that make use of the fog can avoid the network performance limitation of cloud computing while being less resource constrained than edge computing. As a result, they offer a useful balance of the current paradigms. This article explores various aspects of fog computing in the context of big data. Big Data and Fog Computing

When: Thu, Dec 6, 2018 11 am PST, 2 PM EST, 7 PM GMT

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Hi, internet! DataCamp launches at least one DataCamp project a month that is topical for that specific month, and you (yes, you) should create one for January 2019. For example, in October, we launched a Halloween-related project called Explore 538’s Halloween Candy Rankings by Nick Solomon. In that project, DataCamp subscribers can explore a dataset that contains information about all different kinds of candy. Is it chocolaty? How does the cost compare to other candies? How many people prefer this candy over another? The project wraps up by using linear regression to predict candy popularity and logistic regression to predict whether a candy is chocolaty or not.

YOLO, tuberculosis, and candy: Enjoy these new, intriguing and overlooked datasets and kernels

The post Gold-Mining Week 13 (2018) appeared first on Fantasy Football Analytics.

Customer churn affects companies across industry, size, and geography. Retaining your existing customers is just as important (if not more so) than attracting and acquiring brand new customers. According to Harvard Business Review, the cost to acquire a new customer is anywhere from five to 25 times more expensive than retaining an existing one. Businesses today can use the power of AI to help determine which customers are more likely to churn, and what actions to take to keep them. Armed with this knowledge, retention efforts can be prioritized to focus on the most at risk customers.