Remove 2001 Remove Risk Remove Software 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

Most algorithms in the news these days are calculated by software. 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. An Apgar score is a tiny story, easily made and compared.

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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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Data Science, Past & Future

Domino Data Lab

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. Then software adapts. Software is reactive to what happens in hardware in a lot of ways. Key highlights from the session include.

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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? In terms of teaching and learning data science, Project Jupyter is probably the biggest news over the past decade – even though Jupyter’s origins go back to 2001! In business terms, why does this matter ?

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Data Science at The New York Times

Domino Data Lab

In 2001, Bill Cleveland writes this article saying, “You are doing it wrong.” You can sleep at night as a data scientician and you know you’re not building a random number generator, but the people from product, they don’t want to know just that you can predict who’s going to be at risk.