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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. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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MY TAKE: What if Big Data and AI could be intensively focused on health and wellbeing?

The Last Watchdog

Towards this end it has begun sharing videos, whitepapers and reports designed to rally decision makers from all quarters to a common cause. And they’re nowhere near figuring out how to apply data analytics and machine learning to make higher uses, at scale, of their patients’ ever-expanding digital footprints.

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MY TAKE: The no. 1 reason ransomware attacks persist: companies overlook ‘unstructured data’

The Last Watchdog

Related video: Why it’s high time to protect unstructured data. But with no orderly internal framework, unstructured data defies data mining tools. Most human communication is via unstructured data; it’s messy and doesn’t fit into analytical algorithms. Ransomware target.

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Cryptojacking Coinhive Miners for the first time found on the Microsoft Store

Security Affairs

The removed apps are Fast-search Lite, Battery Optimizer (Tutorials), VPN Browsers+, Downloader for YouTube Videos, Clean Master+ (Tutorials), FastTube, Findoo Browser 2019, and Findoo Mobile & Desktop Search. The mining script then gets activated and begins using the majority of the computer’s CPU cycles to mine Monero for the operators.”

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

IBM Big Data Hub

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. It’s also necessary to understand data cleaning and processing techniques.

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Embeddable AI saves time building powerful AI applications

IBM Big Data Hub

With embeddable AI, you get a set of flexible, fit-for-purpose AI models that developers can use to provide enhanced end-user experiences—like, automatically transcribing voice messages and video conferences to text. Users can then mine the data using simple keyword searches to find the information they need.

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Part 1: OMG! Not another digital transformation article! Is it about understanding the business drivers?

ARMA International

Some technology trends such as real-time data analytics are on-going, while others are more recent, such as blockchain. Techopedia (2021) defines a CSP as “a software environment where users can collaborate as well as create and work on different types of content such as text, audio and video pieces. Content can be delivered via a CSP.