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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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How Data Analytics Tools Eliminate Business Owner Headaches

Smart Data Collective

Big data has the power to transform any small business. However, many small businesses don’t know how to utilize it. One study found that 77% of small businesses don’t even have a big data strategy. If your company lacks a big data strategy, then you need to start developing one today.

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An A-Z Data Adventure on Cloudera’s Data Platform

Cloudera

Company data exists in the data lake. Data Catalog profilers have been run on existing databases in the Data Lake. A Cloudera Data Warehouse virtual warehouse with Cloudera Data Visualisation enabled exists. Model building. Model training . Model deployment & serving.

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A Simple Data Capability Framework

Peter James Thomas

The objective here is to use a variety of techniques to tease out findings from available data (both internal and external) that go beyond the explicit purpose for which it was captured. When I first started focussing on the data arena, Data Warehouses were state of the art. Data Operating Model / Organisation Design.

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Big Data Hub

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.