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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

This blog post discusses such a comprehensive approach that is used at Youtube. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. And we can keep repeating this approach, relying on intuition and luck.

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AI in Analytics: The NLQ Use Case

Sisense

In my previous blog , I wrote about Natural Language Query (NLQ, or search analytics for some), as one of the major topics that we, the AI group in Sisense, are working on. In this blog, I would like to expand on NLQ and discuss how this AI technology can be leveraged in our domain. Machine Intent vs. User Intent.

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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. The post New Format for The Bar Chart Reference Page appeared first on The Data Visualisation Catalogue Blog. Qu, H., & Sedlmair, M. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Readability and Precision in Pictorial Bar Charts.

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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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Some highlights from 2020

Data Science and Beyond

I’ve been working remotely with Automattic since 2017, so I was pretty covid-ready as far as work was concerned. I summarised this work in a post on the company’s blog , and discussed it in an interview with PublishPress. Remote work. Technical work. This aligns well with my long-standing interest in causal inference.

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Smarten Advanced Data Discovery is All the Buzz!

Smarten

Advanced Data Discovery ensures data democratization by enabling users to drastically reduce the time and cost of analysis and experimentation. Their findings include: Through 2017, the number of citizen data scientists will grow five times faster than the number of highly skilled data scientists.

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On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data.