Remove 2001 Remove Management Remove Risk Remove Statistics
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Speed up queries with the cost-based optimizer in Amazon Athena

AWS Big Data

Starting today, the Athena SQL engine uses a cost-based optimizer (CBO), a new feature that uses table and column statistics stored in the AWS Glue Data Catalog as part of the table’s metadata. By using these statistics, CBO improves query run plans and boosts the performance of queries run in Athena.

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Reclaiming the stories that algorithms tell

O'Reilly on Data

In 2001, just as the Lexile system was rolling out state-wide, a professor of education named Stephen Krashen took to the pages of the California School Library Journal to raise an alarm. The report has pages of careful caveats, but in the end it treats these risk-adjusted ratios as a good measure of a surgeon’s performance.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

That’s a can-o-worms that exposes problems with Silicon Valley product management culture not entirely comprehending the real-world issues of MLOps. Consider the following timeline: 2001 – Physics grad students are getting hired in quantity by hedge funds to work on Wall St. Roll the clock out to Sci Foo.

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Data Science, Past & Future

Domino Data Lab

He was saying this doesn’t belong just in statistics. It involved a lot of interesting work on something new that was data management. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. Tukey did this paper. It’s a great read.

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Data Science at The New York Times

Domino Data Lab

I do not want a product manager being the information bottleneck between people who are supposed to do some research and develop a product that is useful and somebody who’s going to be the end user. In 2001, Bill Cleveland writes this article saying, “You are doing it wrong.” We can monetize that.”

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Themes and Conferences per Pacoid, Episode 5

Domino Data Lab

What are the projected risks for companies that fall behind for internal training in data science? Let me ask a question: as a manager, do you outsource that training? Over the years, people I’ve helped mentor in data science have become team leaders, managers, and executives. In business terms, why does this matter ?