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Create High-Impact Data Visualizations: Nine Effective Strategies

Occam's Razor

The sixteen examples neatly fall into nine strategies I hope you’ll cultivate in your analytics practice as you create data visualizations: 1: The Simplicity Obsession. Strategy 1: The Simplicity Obsession. Strategy 2: If Complex, Focus! Strategy 3: Venn Diagrams FTW! Strategy 4: Interactivity With Insightful End-Points.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. It is also a sound strategy when experimenting with several parameters at the same time. And sometimes even if it is not[1].)

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Rethinking the IT talent pipeline

CIO Business Intelligence

Across industries, companies are experimenting with more creative talent retention and acquisition strategies, including developing a pipeline of IT professionals that have unconventional backgrounds or are sourced from nontraditional applicant pools. Today, those two strategies are no longer enough. MSC Industrial Supply Co.

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Decision-Making in a Time of Crisis

O'Reilly on Data

We know, statistically, that doubling down on an 11 is a good (and common) strategy in blackjack. We saw this after the 2016 U.S. To do so, let’s stick with the example of the 2016 U.S. How can you say always ?!? Mike had made the common error of equating a bad outcome with a bad decision.

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Big Data Creates Massive Changes for the Game of Golf

Smart Data Collective

A study was held in 2016 that saw Big Data come into the scene. It promotes focuses on improving the efficiency of a golfer by increasing course strategy or difficulty, playability pace play or environmental impact. Statistical analysis can help in creating new ways to play, train, and watch the sport with advanced details.

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Belcorp reimagines R&D with AI

CIO Business Intelligence

It’s worth noting that each initiative carried its own unique complexity, such as varying data sizes, data variety, statistical and computational models, and data mining processing requirements. Follow a value-focused strategy. “Deliveries were made in phases, and complexity increased with each phase,” Gopalan says.

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What is Model Risk and Why Does it Matter?

DataRobot Blog

Recently, Stanford University released its 2022 AI Index Annual Report , where it showed between 2016 and 2021, the number of bills containing artificial intelligence grew from 1 to 18 in 25 countries. A purposeful MLOps strategy can provide exactly this.

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