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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Prescriptive analytics: Prescriptive analytics predicts likely outcomes and makes decision recommendations. Diagnostic analytics: Diagnostic analytics helps pinpoint the reason an event occurred. Descriptive analytics: Descriptive analytics evaluates the quantities and qualities of a dataset.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app. A manufacturer developed powerful, 3D-printed sensors to guide driverless vehicles. Data science, machine learning and IBM Practicing data science comes with challenges.

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Three Types of Actionable Business Analytics Not Called Predictive or Prescriptive

Rocket-Powered Data Science

Decades (at least) of business analytics writings have focused on the power, perspicacity, value, and validity in deploying predictive and prescriptive analytics for business forecasting and optimization, respectively. How do predictive and prescriptive analytics fit into this statistical framework?

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

4) Predictive And Prescriptive Analytics Tools. Business analytics of tomorrow is focused on the future and tries to answer the questions: what will happen? Hotels try to predict the number of guests they can expect on any given night in order to adjust prices to maximize occupancy and increase revenue.

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What Is Embedded Analytics?

Jet Global

The industries that are users of embedded analytics are interesting. The Business Services group leads in the usage of analytics at 19.5 And Manufacturing and Technology, both 11.6 The sample included 1,931 knowledge workers from various industries, including financial services, healthcare, and manufacturing.