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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself.

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Responses to Negative Data: Four Senior Leadership Archetypes.

Occam's Razor

Through their words and actions, ESLs can quash data's learn to improve spirit, or they can nurture that spirit and deliver a transformative, positive impact on the company's culture + profit. Archetype #3: How they react: Their trigger instinct in face of factual negative data is to make excuses. To provide context.

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Visualizing COVID-19 Data Responsibly: An Interview with Amanda Makulec

Depict Data Studio

They host monthly meet-ups, which have included hands-on workshops, guest speakers, and career panels. Data Visualization Society. Amanda went through some of the top considerations, from data quality, to data collection, to remembering the people behind the data, to color choices. DataViz DC.

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Viral, Social, Sentiment, Mobile: 4 Delightful Web Analytics Solutions

Occam's Razor

In this case measuring "Personable": Engaged in other people's well-being and at peace with expressing your own uncertainty about the world. Or for that matter Trip Advisor or Amazon or any site that hosts reviews and ratings? A really simple UI and reporting layer, even your mom will understand the data.

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Measuring Validity and Reliability of Human Ratings

The Unofficial Google Data Science Blog

Editor's note : The relationship between reliability and validity are somewhat analogous to that between the notions of statistical uncertainty and representational uncertainty introduced in an earlier post. Measurement challenges Assessing reliability is essentially a process of data collection and analysis.