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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Watsonx comprises of three powerful components: the watsonx.ai

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The Future of AI in the Enterprise

Jet Global

Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.

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The Future of AI in the Enterprise

Jet Global

Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

but to reference concrete tooling used today in order to ground what could otherwise be a somewhat abstract exercise. Adapted from the book Effective Data Science Infrastructure. Data is at the core of any ML project, so data infrastructure is a foundational concern. Along the way, we’ll provide illustrative examples.

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Constructing A Digital Transformation Strategy: Putting the Data in Digital Transformation

erwin

It improves IT and business data literacy and knowledge, supporting enterprise data governance and business enablement. It helps solve the lack of visibility and control over “data at rest” in databases, data lakes and data warehouses and “data in motion” as it is integrated with and used by key applications.