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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

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New Format for The Bar Chart Reference Page

The Data Visualisation Catalogue

Journal of Experimental Psychology: Applied, 4 (2), 119–138. Skau, D., & Kosara, R. Eurographics Conference on Visualization (EuroVis). An Evaluation of the Impact of Visual Embellishments in Bar Charts. Harrison, L., & Kosara, R. Eurographics Conference on Visualization (EuroVis) , 34. Bar charts and box plots.

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Drug Discovery Needs AI To Discover More Treatments

Smart Data Collective

In a report on the failure rates of drug discovery efforts between 2013 and 2015, Richard K. To bring costs down and encourage further experimentation, artificial intelligence can study hundreds or thousands of patient records in search of the biomarkers the drug intends to target.

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

accounting for effects "orthogonal" to the randomization used in experimentation. For example in ads, experiments using cookies (users) as experimental units are not suited to capture the impact of a treatment on advertisers or publishers nor their reaction to it. To see this, imagine you want to study long-term effects in an A/B test.

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The ethics of data flow

O'Reilly on Data

Facebook asked Cambridge Analytica to delete the data back in 2015, but apparently did nothing to determine whether the data was actually deleted, or shared further. Once data has started flowing, it is very difficult to stop it. Data flows can be very complex. What might that responsibility mean?

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.

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HPE Looks to Edge-to-Cloud Strategy for Growth in 2018/2019

Hurwitz & Associates

The strategy evolved from earlier corporate moves to streamline HPE’s business, following the split of the traditional HP business in 2015, creating an HP business focused on PCs and printers – and HPE, focused on enterprise infrastructure. Consumption models are changing.