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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.

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

IBM Big Data Hub

Anomaly detection simply means defining “normal” patterns and metrics—based on business functions and goals—and identifying data points that fall outside of an operation’s normal behavior. A machine learning model trained with labeled data will be able to detect outliers based on the examples it is given.

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The most valuable AI use cases for business

IBM Big Data Hub

For example, Amazon reminds customers to reorder their most often-purchased products, and shows them related products or suggestions. For example, a supply-chain function can use algorithms to predict future needs and the time products need to be shipped for timely arrival. Routine questions from staff can be quickly answered using AI.

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5 misconceptions about cloud data warehouses

IBM Big Data Hub

In addition, companies have complex data security requirements. For example, Marriott International built a decentralized hybrid-cloud data architecture while migrating from their legacy analytics appliances, and saw a nearly 90% increase in performance.

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

IBM Big Data Hub

That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues. For example, is the problem related to declining revenue or production bottlenecks?

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GUEST ESSAY: Recalibrating critical infrastructure security in the wake of evolving threats

The Last Watchdog

The recent Unitronics hack , in which attackers took control over a Pennsylvania water authority and other entities, is a good example. in different industries, including energy, manufacturing, and healthcare. For example, OT systems have become highly connected, making them an obvious target for hackers.

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Preparing for Litigation Before it Happens: eDiscovery Best Practices, Part Two

eDiscovery Daily

Ensuring compliance with laws and regulations that govern data, such as the Sarbanes-Oxley Act or HIPAA. IG will also almost always involve some form of unstructured data, that is, information that either is not in a fielded form in databases or is annotated or otherwise semantically tagged in documents. Manufacturing.

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