Sat.Nov 21, 2015 - Fri.Nov 27, 2015

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The hardest parts of data science

Data Science and Beyond

Contrary to common belief, the hardest part of data science isn’t building an accurate model or obtaining good, clean data. It is much harder to define feasible problems and come up with reasonable ways of measuring solutions. This post discusses some examples of these issues and how they can be addressed. The not-so-hard parts Before discussing the hardest parts of data science, it’s worth quickly addressing the two main contenders: model fitting and data collection/cleaning.

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Is Your Organization “VDI Ready”?

Nutanix

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Is Your Organization “VDI Ready”?

Nutanix

20
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Is Your Organization “VDI Ready”?

Nutanix

20
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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.