Remove Data Lake Remove Modeling Remove Prescriptive Analytics Remove Structured Data
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

Hence the drive to provide ML as a service to the Data & Tech team’s internal customers. All they would have to do is just build their model and run with it,” he says. That step, primarily undertaken by developers and data architects, established data governance and data integration.

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

AWS Big Data

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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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

Analytics acts as the source for data visualization and contributes to the health of any organization by identifying underlying models and patterns and predicting needs. Broadly, there are three types of analytics: descriptive , prescriptive , and predictive. Visualizations: past, present, and future.