Data Science


Using Machine Learning To Find Employees Who Can Scale With Your Business 7wData

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Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They're not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers' convenience first and candidates last. These outmoded approaches to recruiting aren't designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.


Product Management Tips for Data Science Projects - by Rich Mironov

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Data science has traditionally been an analysis-only endeavor: using historical statistics, user interaction trends, or AI machine learning to predict the impact of deterministically coded software changes. For instance, "how do we think this change to the onboarding workflow will shift user behavior?" This is data science (DS) as an offline toolkit to make smarter decisions. Increasing, though, companies are building statistical or AI/Machine Learning features directly into their products. This can make our applications less deterministic – we may not know exactly how applications behave over time, or in specific situations – and harder to explain.


Data science could help Californians battle future wildfires -- GCN

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A major wildfire spread through Colorado, and I spent long hours locating shelters, identifying evacuation routes and piecing together satellite imagery. As the Fourmile Canyon Fire devastated areas to the west of Boulder, ultimately destroying 169 homes and causing US$217 million in damage, my biggest concerns were ensuring that people could safely evacuate and first responders had the best chance of keeping the fire at bay. I spent it sitting comfortably in my home in Bloomington, Indiana, a thousand miles away from the action. I was a volunteer, trying to help fire victims. I had created a webpage to aggregate data about the fire, including the location of shelters and the latest predictions of fire spread.


Microsoft shows off hybrid cloud management and cloud analytics tools at Ignite

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Microsoft's Ignite event traditionally attracts more from the developer ranks, but the technologies on display are increasingly of relevance to CIOs developing cloud strategies today. At Ignite 2019 in Orlando last week, Microsoft unveiled a new approach to analytics and data warehousing, Azure Synapse Analytics, and a new way to run Azure data services in anyone's cloud, Azure Arc. Get the latest cloud computing insights by signing up for our newsletter. With Azure Synapse Analytics Microsoft takes its Azure SQL Data Warehouse and turns up the volume to handle petabytes of data in its cloud. Some of the features -- such as dynamic data masking and column- and row-level security to provide granular access control -- are already generally available, while others -- notably integrations with Apache Spark, Power BI and Azure Machine Learning -- are still in preview.


Defining AI, ML, and Predictive Analytics for Non-Techies

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Obviously, people define AI and machine learning in many different ways. In 2019, it is still unclear what AI is capable of and what the exact definition is. Artificial Intelligence: A non-human system that shows human-like intelligence. AI is an umbrella term, which includes machine learning and other techniques. Examples include playing the computer on a video game or talking to Siri.


Defining AI, ML, and Predictive Analytics for Non-Techies

#artificialintelligence

Obviously, people define AI and machine learning in many different ways. In 2019, it is still unclear what AI is capable of and what the exact definition is. Artificial Intelligence: A non-human system that shows human-like intelligence. AI is an umbrella term, which includes machine learning and other techniques. Examples include playing the computer on a video game or talking to Siri.


West Africa boot camp seeks artificial intelligence fix for climate-hit farmers - Reuters

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DAKAR (Thomson Reuters Foundation) - Data analyst Fabrice Sonzahi enrolled in a course on artificial intelligence (AI) in Dakar, hoping to help struggling farmers improve crop yields in his home country of Ivory Coast. He is part of an inaugural batch of students at a new AI programming school in Senegal, one of the first in West Africa. Its mission is to train local people in using data to solve pressing issues like the impact of climate change on crops. The Dakar Institute of Technology (DIT), which opened in September, is running its first 10-week boot camp with nine students in partnership with French AI school VIVADATA. "I am convinced that by analyzing data we can give (farmers) better solutions," said Sonzahi, 30.


Global Big Data Conference

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Machine intelligence, artificial intelligence and machine learning are not different terms for the same thing. They're related but distinct from each other. It's important for us all to recognize the nuanced differences among them, so we can do a much better job of figuring out which one, or which combination of them, is best for a specific undertaking. If, for example, you think you're using machine intelligence but are really just using automation, you're missing out on the potential MI has to offer. My States Title colleague Andy Mahdavi and I put together a list designed to make the distinctions as clear as possible.


Global Big Data Conference

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Qualified data providers include category-leading brands such as Reuters, who curate data from over 2.2 million unique news stories per year in multiple languages; Change Healthcare, who process and anonymize more than 14 billion healthcare transactions and $1 trillion in claims annually; Dun & Bradstreet, who maintain a database of more than 330 million global business records; and Foursquare, whose location data is derived from 220 million unique consumers and includes more than 60 million global commercial venues. For qualified data providers, AWS Data Exchange makes it easy to reach the millions of AWS customers migrating to the cloud by removing the need to build and maintain infrastructure for data storage, delivery, billing, and entitling. Enterprises, scientific researchers, and academic institutions have been using third-party data for decades to conduct research, power applications and analytics, train machine-learning models, and make data-driven decisions. But, as these customers subscribe to more third-party data, they often have to wait weeks to receive shipped physical media, manage sensitive credentials for multiple File Transfer Protocol (FTP) hosts and periodically check for updates, or code to several disparate application programming interfaces (APIs). These methods are inconsistent with the modern architectures customers are developing in the cloud.


Global Big Data Conference

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Qualified data providers include category-leading brands such as Reuters, who curate data from over 2.2 million unique news stories per year in multiple languages; Change Healthcare, who process and anonymize more than 14 billion healthcare transactions and $1 trillion in claims annually; Dun & Bradstreet, who maintain a database of more than 330 million global business records; and Foursquare, whose location data is derived from 220 million unique consumers and includes more than 60 million global commercial venues. For qualified data providers, AWS Data Exchange makes it easy to reach the millions of AWS customers migrating to the cloud by removing the need to build and maintain infrastructure for data storage, delivery, billing, and entitling. Enterprises, scientific researchers, and academic institutions have been using third-party data for decades to conduct research, power applications and analytics, train machine-learning models, and make data-driven decisions. But, as these customers subscribe to more third-party data, they often have to wait weeks to receive shipped physical media, manage sensitive credentials for multiple File Transfer Protocol (FTP) hosts and periodically check for updates, or code to several disparate application programming interfaces (APIs). These methods are inconsistent with the modern architectures customers are developing in the cloud.