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Data-Driven Interview Advice: How the Best Teams Screen Data Scientists

Insight

How will they interact with product, engineering, sales, or marketing? you’re looking for a collaborator who can work and communicate well with you and your team, as well as anyone else that interacts with your team. What to evaluate: How will this person contribute to your culture? Will they be a strategic thought partner?

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The AIgent: Using Google’s BERT Language Model to Connect Writers & Representation

Insight

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often Galbraith eventually opted to publish Cuckoo’s Calling through an acquaintance of sorts. but the publishing industry failed to see it. often without even looking at it. In other words, Galbraith had chops?—?but

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6 generative AI hazards IT leaders should avoid

CIO Business Intelligence

But these Guardian polls appear to have been published on Microsoft properties with millions of visitors by automated systems with no human approval required. Customers should have the option to opt out of interactions with generative AI, particularly in sensitive areas,” says Daga.

IT 133
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Defining data science in 2018

Data Science and Beyond

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. Numerous articles have been published on the meaning of data science in the past six years. In a recent article , Hernán et al. In addition, while I agree with Hernán et al.

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Web Analytics: An Hour A Day

Occam's Razor

I am thrilled to say that my book Web Analytics: An Hour A Day has been published and is now widely available. Bonus: Interactive CD: Contains six podcasts, one video, two web analytics metrics definitions documents and five insightful powerpoint presentations. Experimentation & Testing (A/B, Multivariate, you name it).

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Misadventures in experiments for growth

The Unofficial Google Data Science Blog

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. In fact, this blog has published posts on this very topic. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime.

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How a Discovery Data Warehouse, the next evolution of augmented analytics, accelerates treatments and delivers medicines safely to patients in need

Cloudera

Scale to provide 1,000s of researchers frictionless interaction with data. How can he make it easy to see statistics, and do calculations, on discovered commonalities, across structured and unstructured data? How can users drill down, in non-technical ways, to quickly interact with data that explains what correlations seem to matter?