Remove 2012 Remove Measurement Remove Modeling Remove Optimization
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Rising Tide Rents and Robber Baron Rents

O'Reilly on Data

Instead of continuing to deploy their attention optimization algorithms for their users’ and suppliers’ benefit, the tech giants began to use them to favor themselves. By the end of 2012, it was up to 82%. Some of those innovations, like Amazon’s cloud computing business, represented enormous new markets and a new business model.

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The Rise of Unstructured Data

Cloudera

In other words, structured data has a pre-defined data model , whereas unstructured data doesn’t. . It facilitates AI because, to be useful, many AI models require large amounts of data for training. There is a plethora of efforts to produce models that can learn without labels or with few labels. The challenges of data.

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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., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

A naïve comparison of the exposed and unexposed groups would produce an overly optimistic measurement of the effect of the ad, since the exposed group has a higher baseline likelihood of purchasing a pickup truck. It should be noted that inverse probability weighting is not generally optimal (i.e., the curse of dimensionality).

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Google Analytics Tips: 10 Data Analysis Strategies That Pay Off Big!

Occam's Razor

Multi-channel attribution was the flavor of the month for every month in 2012. And just as in 2012 magic pills will be scarce, FUD will be plentiful, and vendors will promise the moon. After that if you can't resist the itch, go play with the, now free to everyone, Attribution Modeling Tool in GA. Mobile Devices Report.

Strategy 159
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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

For us, demand for forecasts emerged from a determination to better understand business growth and health, more efficiently conduct day-to-day operations, and optimize longer-term resource planning and allocation decisions. Selection and aggregation of forecasts from an ensemble of models to produce a final forecast.

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A Big Data Imperative: Driving Big Action

Occam's Razor

Avoiding big disappointment and the hows were on my mind as I prepared my keynote for Strata 2012 Big Data conference. The current flawed data org structure, its challenges, and the new optimal org structure to truly bring big action to big data. If we don't answer all those hows big data will be a big disappointment.

Big Data 127