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New live online training courses

O'Reilly Media - Ideas

Understanding Data Science Algorithms in R: Scaling, Normalization and Clustering , August 14. Real-time Data Foundations: Spark , August 15. Visualization and Presentation of Data , August 15. Python Data Science Full Throttle with Paul Deitel: Introductory AI, Big Data and Cloud Case Studies , September 24.

Course 67
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219+ live online training courses opened for June and July

O'Reilly Media - Ideas

Understanding Data Science Algorithms in R: Scaling, Normalization and Clustering , August 14. Real-time Data Foundations: Spark , August 15. Visualization and Presentation of Data , August 15. Python Data Science Full Throttle with Paul Deitel: Introductory AI, Big Data and Cloud Case Studies , September 24.

Course 51
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Apiumhub among top IT industry leaders in Code Europe event

Apiumhub

This year you will have 6 unique tracks: Cloud Computing: IaaS, PaaS, SaaS DevOps: Microservices, Automation, ASRs Cybersecurity: Threats, Defenses, Tests Data Science: ML, AI, Big Data, Business Analytics Programming languages: C++, Python, Java, Javascript,Net Future & Inspire: Mobility, 5G data networks, Diversity, Blockchain, VR.

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160+ live online training courses opened for May and June

O'Reilly Media - Ideas

Spotlight on Cloud: The Hidden Costs of Kubernetes with Bridget Lane , June 6. Spotlight on Data: Caching Big Data for Machine Learning at Uber with Zhenxiao Luo , June 17. Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , May 20.

Course 46
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Technology Trends for 2022

O'Reilly Media - Ideas

A quick look at bigram usage (word pairs) doesn’t really distinguish between “data science,” “data engineering,” “data analysis,” and other terms; the most common word pair with “data” is “data governance,” followed by “data science.” It’s clear that Amazon Web Services’ competition is on the rise.

Trends 111
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Where Programming, Ops, AI, and the Cloud are Headed in 2021

O'Reilly Media - Ideas

The biggest challenge facing operations teams in the coming year, and the biggest challenge facing data engineers, will be learning how to deploy AI systems effectively. It’s possible that AI (along with machine learning, data, big data, and all their fellow travelers) is descending into the trough of the hype cycle.