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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., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

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At a loss for data project ROI? Evaluate it like a product

CIO Business Intelligence

In 2006, British mathematician Clive Humby proclaimed, “Data is the new oil.”. Increasing numbers of West Monroe clients are asking the firm to help them through data monetization exercises: ideation, testing the feasibility of components, and laying out a roadmap for creating data products, Laney says.

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Excellent Analytics Tip #8: Measure the Real Conversion Rate & "Opportunity Pie"

Occam's Razor

Mostly because short term goals drive a lot of what we do and if you are selling something on your website then it only seems to make logical sense that we measure conversion rate and get it up as high as we can as fast as we can. So measure Bounce Rate of your website. Even though we should not obsess about conversion rate we do.

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Kick Butt With Internal Site Search Analytics

Occam's Razor

I had first written about the wonders of site search analysis in a June 2006 post: Are You Into Internal Site Search Analysis? 3: Measure Internal Site Search Quality. #4: 5: Life Is About Results: Measure Outcomes! It is obvious to you how the above report can also help you measure the quality of your internal search results.

Analytics 101
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What’s the Difference: Quantitative vs Qualitative Data

Alation

From product development to customer satisfaction, nearly every aspect of a business uses data and analytics to measure success and define strategies. Measures of central tendency. Test hypotheses in order to draw conclusions about populations (for example, the relationship between Lifetime Value and Annual Revenue).

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Cloud gets introduced: Amazon AWS launched in public beta in 2006. Mobile gets introduced: the term “ CrackBerry ” becomes a thing in 2006, followed by the launch of the iPhone the following year. data to train and test models poses new challenges: The need for reproducibility in analytics workflows becomes more acute.

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Public cloud vs. private cloud vs. hybrid cloud: What’s the difference?

IBM Big Data Hub

Internet companies like Amazon led the charge with the introduction of Amazon Web Services (AWS) in 2002, which offered businesses cloud-based storage and computing services, and the launch of Elastic Compute Cloud (EC2) in 2006, which allowed users to rent virtual computers to run their own applications.