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The most practical causal inference book I’ve read (is still a draft)

Data Science and Beyond

In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. Now, I believe I’ve finally found a book with practical techniques that I can use on real problems: Causal Inference by Miguel Hernán and Jamie Robins. Hence, the book is full of practical examples.

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Bringing an AI Product to Market

O'Reilly on Data

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Fair warning: if the business lacks metrics, it probably also lacks discipline about data infrastructure, collection, governance, and much more.) Agreeing on metrics. Don’t expect agreement to come simply.

Marketing 362
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Do You Need a DataOps Dojo?

DataKitchen

We’ll also discuss building DataOps expertise around the data organization, in a decentralized fashion, using DataOps centers of excellence (COE) or DataOps Dojos. For example, some teams may recognize services revenue in the quarter booked, and others may amortize the revenue over the contract period. DataOps Technical Services.

Metrics 243
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AI Has an Uber Problem

O'Reilly on Data

The race to the top is no longer driven by who has the best product or the best business model, but by who has the blessing of the venture capitalists with the deepest pockets—a blessing that will allow them to acquire the most customers the most quickly, often by providing services below cost. I disagree.

Marketing 154
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Higher-ed CIOs embrace academia’s AI challenges

CIO Business Intelligence

One is knowledge of the emerging mega trends in technology — data, AI, and machine learning — and the other is understanding organizational culture needed to advance the technology goals and to inspire contributors,” he says. We’ve done a lot of experimentation on these adaptive tools that use AI,” says Ventimiglia.

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A Data Scientist Explains: When Does Machine Learning Work Well in Financial Markets?

DataRobot Blog

As a data scientist, one of the best things about working with DataRobot customers is the sheer variety of highly interesting questions that come up. For counterparty behavior prediction: some form of structured data which contains not only won trades but also unsuccessful requests/responses. For price discovery (e.g.,

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Keynote Takeaways From Gartner Data & Analytics Summit

Sisense

Every year there’s high anticipation to see what key message Gartner will present in the yearly Data & Analytics Summits. It’s always fun and insightful to be able to talk to so many CDOs, CIOs, data and BI professionals within 2.5 At Sisense we’ve been preaching for BI prototyping and experimentation for quite a while now.