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Uncertainties: Statistical, Representational, Interventional

The Unofficial Google Data Science Blog

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Crucially, it takes into account the uncertainty inherent in our experiments. It is also a sound strategy when experimenting with several parameters at the same time.

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Leading infrastructure to accelerate electric power intelligence

CIO Business Intelligence

Carbon neutrality and carbon peak strategies are driving the adoption of new energy worldwide. However, new energy is restricted by weather and climate, which means extreme weather conditions and unpredictable external environments bring an element of uncertainty to new energy sources. HPLC can deliver 99.9%

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Decision-Making in a Time of Crisis

O'Reilly on Data

We know, statistically, that doubling down on an 11 is a good (and common) strategy in blackjack. But when making a decision under uncertainty about the future, two things dictate the outcome: (1) the quality of the decision and (2) chance. Mike had made the common error of equating a bad outcome with a bad decision.

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Real-time Data, Machine Learning, and Results: The Evidence Mounts

CIO Business Intelligence

By Bryan Kirschner, Vice President, Strategy at DataStax. In the new report, titled “Digital Transformation, Data Architecture, and Legacy Systems,” researchers defined a range of measures of what they summed up as “data architecture coherence.” About Bryan Kirschner : Bryan is Vice President, Strategy at DataStax.

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Systems Thinking and Data Science: a partnership or a competition?

Jen Stirrup

However, organizations can be supported by a synergistic approach by integrating systems thinking with the data strategy and technical perspective. Therefore, interacting with systems using minimal technical skills is very beneficial. Of course, the findings need to add value, but how do we measure this success?

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Businesses Discover the Importance of Merging Analytics and Content Marketing

Smart Data Collective

When you combine both, you get one of the most formidable and effective marketing strategies ever. Businesses worldwide, especially SaaS businesses, have discovered that smart, measurable content marketing is the key to achieving their business goals. What is a SaaS content marketing strategy? How can you get started, though?

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