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

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box

Bill Schmarzo - Dell EMC

This thought process highlights an important behavioral trait of the best data scientists; the best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance. You try them all!

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The Third Modern Data Management Summit: Making Data Work!

Reltio

The third Modern Data Management annual summit ( #DataDriven19 ) held on February 26-27 2019 attracted more than 400 business and IT professionals getting together in San Francisco to witness the future of data management, share success stories and learn best practices. This year’s theme was “ Organize Master Data.

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Interview With a Crypto Scam Investment Spammer

Krebs on Security

pw has been registered and abandoned by several parties since 2014, but the most recent registration data available through DomainTools.com shows it was registered in March 2020 to someone in Krasnodar, Russia with the email address edgard011012@gmail.com. A DIRECT QUOT The domain quot[.]pw

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Big Data Hub

As organizations collect larger data sets with potential insights into business activity, detecting anomalous data, or outliers in these data sets, is essential in discovering inefficiencies, rare events, the root cause of issues, or opportunities for operational improvements.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While AGI remains theoretical, organizations can take proactive steps to prepare for its arrival by building a robust data infrastructure and fostering a collaborative environment where humans and AI work together seamlessly. NLP techniques help them parse the nuances of human language, including grammar, syntax and context.