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Rising Tide Rents and Robber Baron Rents

O'Reilly on Data

In 2005, in “ What is Web 2.0? ,” I made the case that the companies that had survived the dotcom bust had all in one way or another become experts at “harnessing collective intelligence.” Some of those innovations, like Amazon’s cloud computing business, represented enormous new markets and a new business model.

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CBAP certification: A high-profile credential for business analysts

CIO Business Intelligence

It was first published in 2005 and is continuously updated. There must be the opportunity for students to interact with the material by asking questions or otherwise making the learning meaningful. There must be an opportunity to practice the task or objective being presented, and be assessed by the facilitator.

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The 1 Question Leaders Should Ask Before Returning to the Office

CDW Research Hub

In 2020, many Sirius clients were abruptly pushed into a remote operations model. How many social interactions are needed at a team level to drive cohesion that enhances retention? Nearly every executive has received this question in the last year. Some did so with flawless execution, and some wished it had gone so smoothly.

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Data Mining – useful or not?

Jen Stirrup

These propensity models are useful for understanding which customers are most likely to purchase a given set of products. This model can assist in decision making and in focusing marketing efforts. Microsoft offers Data Mining at no extra cost as part of SQL Server 2005 and 2008, which is geared towards the average Excel user.

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

We introduce you to Amazon Managed Service for Apache Flink Studio and get started querying streaming data interactively using Amazon Kinesis Data Streams. Also, a data model that allows table truncations at a regular frequency (for example, every 15 seconds) to store only relevant data in tables can cause locking and performance issues.

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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

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Experimenting to Win with Data

Sisense

Since 2005, Measuremen has helped its clients study and optimize facilities use. After that, says Vincent, Measuremen invites the client to a key insight meeting, “where we show the data … in an interactive session.” After engaging end users about their goals, it’s time to shape data models based on their responses.