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Data Science, Past & Future

Domino Data Lab

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

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How to Use Apache Iceberg in CDP’s Open Lakehouse

Cloudera

Exploratory data science and visualization: Access Iceberg tables through auto-discovered CDW connection in CML projects. Our imported flights table now contains the same data as the existing external hive table and we can quickly check the row counts by year to confirm: year _c1. 1 2008 7009728. 8 2001 5967780.

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Marketing as a strategic business partner: mixing theory, research and #data

Jen Stirrup

So, organizations need a cohesive strategy which aligns all methods of communication to ensure consistency (Porter, 2001). 2001, Strategy and the Internet. Harvard business Review , (2001): 63-78. After all, the Internet can break a business very quickly! Journal of Marketing Management, vol. Levitt, T. & & Levitt, I.M.

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Themes and Conferences per Pacoid, Episode 5

Domino Data Lab

In Paco Nathan ‘s latest column, he explores the theme of “learning data science” by diving into education programs, learning materials, educational approaches, as well as perceptions about education. He is also the Co-Chair of the upcoming Data Science Leaders Summit, Rev. Learning Data Science.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Protein classification with imbalanced data. The post ML internals: Synthetic Minority Oversampling (SMOTE) Technique appeared first on Data Science Blog by Domino. References.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. Introduction.