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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages.

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AI Has an Uber Problem

O'Reilly on Data

The race to the top is no longer driven by who has the best product or the best business model, but by who has the blessing of the venture capitalists with the deepest pockets—a blessing that will allow them to acquire the most customers the most quickly, often by providing services below cost. Venture capitalists don’t have a crystal ball.

Marketing 152
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Knowledge

Occam's Razor

Be data driven?" Six Rules For Creating A Data Driven Boss! Be data driven?" Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! Web Data Quality: A 6 Step Process To Evolve Your Mental Model. The Ultimate Web Analytics Data Reconciliation Checklist.

KPI 124
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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science. Introduction.

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. It helps you to amplify what’s proven to work, throw away what isn’t, and tweak the goal-posts when data indicates that they may be in the wrong place.

Metrics 156
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Measuring Incrementality: Controlled Experiments to the Rescue!

Occam's Razor

It is also important to point out that I am keeping the data simple purely to keep communication of the story straightforward. Here's the outcomes data for the control version of the experiment. Having read this post what might be the biggest downside to experimentation? For people in the control region, nothing changes.