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Collaborating Authors

Data Mining


Data Science & Machine Learning Trends You Cannot Ignore

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Digital transformation has become the new mantra for companies to thrive in the digital age. Data science and machine learning are two major assets in the digital transformation era. Digital transformation has become a necessity for businesses. It is the way forward for all businesses, regardless of size and scope. However, it should be more than simply digitizing your processes.


Data Analyst, Go Live at Riskified - New York

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Riskified empowers merchants and shoppers to realize the full potential of eCommerce by making it safe, accessible, and frictionless. Our global team helps the world's most-innovative eCommerce merchants eliminate risk and uncertainty from their business. Merchants integrate Riskified's machine learning platform to create trusted customer relationships, driving higher sales while reducing costs. Riskified has reviewed hundreds of millions of transactions and approved billions of dollars of revenue for global brands and fast-growing businesses across industries, including Wayfair, Wish, Peloton, Gucci, and many more. As of July 29th, 2021, Riskified has begun trading on NYSE under the ticker RSKD.


Data Analyst, Leveraged Loans at PitchBook Data - New York City, United States

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At PitchBook, we are always looking forward. We continue to innovate, evolve and invest in ourselves to bring out the best in everyone. We're deeply collaborative and thrive on the excitement, energy and fun that reverberates throughout the company. Our extensive mentorship, education and training programs help us create a culture of curiosity that pushes us to always find new solutions and better ways of doing things. The combination of a rapidly evolving industry and our high ambitions means there's going to be some ambiguity along the way, but we excel when we challenge ourselves. We're willing to take risks, fail fast and do it all over again in the pursuit of excellence.


Data mining of Clinical Databases - CDSS 1

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This specialisation is for learners with experience in programming that are interested in expanding their skills in applying deep learning in Electronic Health Records and with a focus on how to translate their models into Clinical Decision Support Systems. The main areas that would explore are: Data mining of Clinical Databases: Ethics, MIMIC III database, International Classification of Disease System and definition of common clinical outcomes. Deep learning in Electronic Health Records: From descriptive analytics to predictive analytics Explainable deep learning models for healthcare applications: What it is and why it is needed Clinical Decision Support Systems: Generalisation, bias, 'fairness', clinical usefulness and privacy of artificial intelligence algorithms.


Principal Software Engineer, Big Data Infrastructure at Roblox - San Mateo, CA

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Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we're building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We're on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you'll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.


Data Analyst (Remote OR Hybrid) at Relativity - Warsaw

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Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.


Senior Data Analyst (US REMOTE) at Zip - New York City

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Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.


Lead Data Analyst - Infrastructure (Remote) at Canva - Sydney, New South Wales, Australia

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Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.


AI in Insurance Market: AI Revolutionizes Insurance Industry with Predictive Analytics and Automated Processes, Fueling Growth and Efficiency in the Market - Digital Journal

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The use of artificial intelligence (AI) in the insurance industry to improve the efficiency and accuracy of risk assessment and management. The insurance market is embracing the use of AI to enhance its operations and better serve its customers. From underwriting to claims processing, AI-powered solutions are being developed to streamline and automate various insurance processes. These solutions are expected to improve the accuracy and speed of risk assessment and management, leading to reduced costs and improved customer experiences. Drivers: Increasing adoption of digital technologies, rising demand for personalized insurance products, and the need to improve operational efficiency are some of the key drivers of the AI in insurance market.


Metadata driven development realises "smart manufacturing" of data ecosystems – blog 3 - Solita Data

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This is the third part of the blog series. The 1st blog focused on the maturity model and explained how the large monolith data warehouses were created. The 2nd blog focused on metadata driven development or "smart manufacturing" of data ecosystems. This 3rd blog will talk about reverse engineering or how existing data assets can be discovered to accelerate the development of new data products. Companies have increasing pressure to start addressing the data silos to reduce cost, improve agility & accelerate innovation, but they struggle to deliver value from their data assets. Many companies have hundreds of systems, containing thousands of databases hundreds of thousands of tables, millions of columns, and millions of lines of code across many different technologies. The starting point is a "data spaghetti" that nobody knows well.