Data meets science: Open access, code, datasets, and knowledge graphs for machine learning research and beyond

Data meets science: Open access, code, datasets, and knowledge graphs for machine learning research and beyond

A new interconnected ecosystem for research is shaping up, and machine learning is just the tip of the iceberg.

Science and data are interwoven in many ways. The scientific method has lent a good part of its overall approach and practices to data-driven analytics, software development, and data science. Now data science and software lend some tools to scientific research.

“To succeed at becoming a data-driven organization, your employees should always use data to start, continue, or conclude every single business decision, no matter how major or minor”.

That quote belongs to Ashish Thusoo, author of the DataOps book, founder of Qubole, and one of the people who built the data-driven culture in Facebook as early as 2007.

As we noted in our 2017 coverage of DataOps in conversation with Thusoo, to anyone with a science background, this should sound familiar. It’s the quintessence of the scientific method: developing hypotheses and putting them to the test with data.

Read the full article on ZDNet

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