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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

The top three items are essentially “the devil you know” for firms which want to invest in data science: data platform, integration, data prep. Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Rinse, lather, repeat.

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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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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

For fixed weights $alpha$, denote $hat e_{alpha, mathcal T_j}$ the ensemble trained using only data in $mathcal T_j$. Random forest with default R tuning parameters (Breiman, 2001). 2001): 5-32. Springer, Berlin: Springer series in statistics, (2001) Hahn, Jinyong. "On Statistical science (2007): 523-539.

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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.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

The problem with this approach is that in highly imbalanced sets it can easily lead to a situation where most of the data has to be discarded, and it has been firmly established that when it comes to machine learning data should not be easily thrown out (Banko and Brill, 2001; Halevy et al., In their 2002 paper Chawla et al.

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Data Science at The New York Times

Domino Data Lab

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Data science. Session Summary.

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Estimating the prevalence of rare events — theory and practice

The Unofficial Google Data Science Blog

There are many strategies we can use to estimate this quantity, and we will discuss each option in detail. But we find that the predicted score is often calibrated to the training data. It is worth comparing the two strategies — both estimate the conditional prevalence with fewer parameters. High Risk 10% 5% 33.3%

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