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In 2019, I was listed as the #1 Top DataScience Blogger to Follow on Twitter. And then there’s this — not a blog, but a link to my 2013 TedX talk: “ Big Data, Small World.” Rocket-Powered DataScience (the website that you are now reading).
Analytics: The products of Machine Learning and DataScience (such as predictive analytics, health analytics, cyber analytics). A reference to a new phase in the Industrial Revolution that focuses heavily on interconnectivity, automation, Machine Learning, and real-time data. They cannot process language inputs generally.
2) MLOps became the expected norm in machine learning and datascience projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.
Modern data architecture best practices Data architecture is a template that governs how data flows, is stored, and accessed across a company. Modern data architectures must be designed to take advantage of technologies such as AI, automation, and internet of things (IoT).
Such technologies include Digital Twin tools, Internet of Things, predictive maintenance, Big Data, and artificial intelligence. Unfortunately, this is not implemented in most cases, which leaves you with massive data amounts that are not useful. Additionally, data collection becomes a costly process.
The demand for real-time online data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list. It’s an extension of datamining which refers only to past data.
Transforming Industries with Data Intelligence. Data intelligence has provided useful and insightful information to numerous markets and industries. With tools such as Artificial Intelligence, Machine Learning, and DataMining, businesses and organizations can collate and analyze large amounts of data reliably and more efficiently.
Best for : the new intern who has no idea what datascience even means. An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”.
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