Remove Data Processing Remove Metrics Remove Modeling Remove Prescriptive Analytics
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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Because data analysts often build machine learning models, programming and AI knowledge are also valuable. Deep learning algorithms are neural networks modeled after the human brain.

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Themes and Conferences per Pacoid, Episode 10

Domino Data Lab

We had data science leaders presenting about lessons learned while leading data science teams, covering key aspects including scalability, being model-driven, being model-informed, and how to shape the company culture effectively. Data science leadership: importance of being model-driven and model-informed.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

The following figure shows some of the metrics derived from the study. Unified customer profile Graph databases excel in modeling customer interactions and relationships, offering a comprehensive view of the customer journey. Plan on how you can enable your teams to use ML to move from descriptive to prescriptive analytics.

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The Gartner 2021 Leadership Vision for Data & Analytics Leaders Webinar Q&A

Andrew White

On January 4th I had the pleasure of hosting a webinar. It was titled, The Gartner 2021 Leadership Vision for Data & Analytics Leaders. This was for the Chief Data Officer, or head of data and analytics. Where does the Data Architect role fits in the Operational Model ? Decision modeling (one of my favorites).

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What Is Embedded Analytics?

Jet Global

Business End-User Benefits Embedding analytics into essential applications makes analytics more pervasive. As a result, end users can better view shared metrics (backed by accurate data), which ultimately drives performance. Pricing model: The pricing scale is dependent on several factors.