Remove Data Lake Remove Data Warehouse 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

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

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

AWS Big Data

Plan on how you can enable your teams to use ML to move from descriptive to prescriptive analytics. The AWS modern data architecture shows a way to build a purpose-built, secure, and scalable data platform in the cloud. Learn from this to build querying capabilities across your data lake and the data warehouse.

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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

Broadly, there are three types of analytics: descriptive , prescriptive , and predictive. The simplest type, descriptive analytics , describes something that has already happened and suggests its root causes. This data is gathered into either on-premises servers or increasingly into cloud data warehouses and data lakes.