Remove 2006 Remove Measurement Remove Modeling Remove Testing
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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. Collibra. “At

ROI 115
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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. As the story goes, the general history of DG is punctuated by four eras: “Application Era” (1960–1990) – some data modeling, ?though There are models everywhere. credit cards).

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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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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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Building a Named Entity Recognition model using a BiLSTM-CRF network

Domino Data Lab

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. How to build a statistical Named Entity Recognition (NER) model.

Modeling 111
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Public cloud vs. private cloud vs. hybrid cloud: What’s the difference?

IBM Big Data Hub

Today, these three cloud architecture models are not mutually exclusive; instead, they work in concert to create a hybrid multicloud—an IT infrastructure model that uses a mix of computing environments (e.g., on-premises, private cloud, public cloud, edge) with public cloud services from more than one provider.