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Data Science at The New York Times

Domino Data Lab

Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. This post provides distilled highlights, a transcript, and a video of the session. Session Summary.

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Unlock The Power of Your Data With These 19 Big Data & Data Analytics Books

datapine

The argument of the book is based on the premise that everybody lies, even when answering an anonymous survey. The rate at which data is generated has increased exponentially in recent years. To put this into perspective, 40,000 search queries are performed per second via Google alone – this equates to 3.46 trillion each year.

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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. This month, let’s explore learning data science. What are some good learning materials available to the public?

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. 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. Introduction. What’s a Foo?

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5 key areas for tech leaders to watch in 2020

O'Reilly on Data

O’Reilly online learning contains information about the trends, topics, and issues tech leaders need to watch and explore. Current signals from usage on the O’Reilly online learning platform reveal: Python is preeminent. In 2019, as in 2018, Python was the most popular language on O’Reilly online learning. Security is surging.

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Edge Prediction in a Social Graph: My Solution to Facebook's User Recommendation Contest on Kaggle

Edwin Chen

A couple weeks ago, Facebook launched a link prediction contest on Kaggle, with the goal of recommending missing edges in a social graph. I love investigating social networks , so I dug around a little, and since I did well enough to score one of the coveted prizes, I’ll share my approach here. What does the network look like? Distributions.

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