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Machine Learning Interview Questions to Land the Perfect Data Science Job

Smart Data Collective

The Bureau of Labor Statistics reports that there were over 31,000 people working in this field back in 2018. You need to know a lot about machine learning to land a job. You will need to make sure that you can answer machine learning interview questions before you can get a job offer.

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Analytics Insights and Careers at the Speed of Data

Rocket-Powered Data Science

One CIO said it this way , “If CIOs invested in machine learning three years ago, they would have wasted their money. One of the primary drivers for the phenomenal growth in dynamic real-time data analytics today and in the coming decade is the Internet of Things (IoT) and its sibling the Industrial IoT (IIoT).

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Three Types of Actionable Business Analytics Not Called Predictive or Prescriptive

Rocket-Powered Data Science

What is the point of those obvious statistical inferences? In statistical terms, the joint probability of event Y and condition X co-occurring, designated P(X,Y), is essentially the probability P(Y) of event Y occurring. How do predictive and prescriptive analytics fit into this statistical framework?

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Glossary of Digital Terminology for Career Relevance

Rocket-Powered Data Science

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Examples: (1-3) All those applications shown in the definition of Machine Learning. (4) Industry 4.0

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Delivering Low-latency Analytics Products for Business Success

Rocket-Powered Data Science

This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative. I recently saw an informal online survey that asked users what types of data (tabular; text; images; or “other”) are being used in their organization’s analytics applications.

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Machine Learning Integration Options

Paul DeBeasi

Machine learning projects are inherently different from traditional IT projects in that they are significantly more heuristic and experimental, requiring skills spanning multiple domains, including statistical analysis, data analysis and application development. Four Options for Integrating Machine Learning with IoT.

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Predictive analytics helps Fresenius anticipate dialysis complications

CIO Business Intelligence

“IDH holds a potentially severe immediate risk for patients during dialysis and therefore requires immediate attention from staff,” says Hanjie Zhang, director of computational statistics and artificial intelligence at the Renal Research Institute, a joint venture of Fresenius North America and Beth Israel Medical Center. “As