Tag: data scientist

Feed: IBM Big Data & Analytics Hub - All Content; Author: karan-sachdeva; The business that gets there first won’t necessarily win digital and AI game. It will be the one that ingrains digital and AI in its business as much as possible. Starting from applying intelligent data science where it matters most and progressively using it in every aspect of the business. In the modern banking environment, consumers are well informed. They expect intuitive, engaging and informative experiences when they bank. Banks need solutions that can help them delight their customers with personalized experiences, empower their workforce to provide differentiated ... Read More

Feed: SAS Blogs. Author: Joao Oliveira. IDC’s April 2017 white paper Data Age 2025 posits that in 2025 the digital economy will multiply by 10 the volume of data now generated, attaining something like 163 zettabytes. Life-critical data - data captured from devices having a direct impact on human life, for example: autonomous cars, remote monitoring patient devices, power grids / smart grids for electric, gas, drinking water... - will grow from the current 10 percent (of 16 ZB) to 20 percent, meaning more than 32 ZB. And IDC estimates that one-quarter of that 163 ZB , more than 40 ... Read More

Feed: Teradata Blog. Do your closets at home ever get cluttered? Is your garage or storage locker stuffed with yesterday’s projects, previous years’ tax returns, books you own but haven’t read, and other belongings that you don’t use but just can’t bear to give away? Just like cramming that exercise bike you’ve stopping pedaling into your garage, you may have turned to Amazon Simple Storage Service (S3) or Microsoft Azure Blob storage as places to keep big stores of data your business has collected but that you haven’t completely figured out what to do with.Unlike your garage, however, Amazon S3 and ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  Advanced analytics and AI are the fourth great lever available to create organic improvement in corporations.  We’ll describe why this one is different from the first three and why the CEO needs the direct help of data scientists to make this happen.   If you’re a CEO or any other flavor of top executive leading a large company (or if you’re a data scientists trying to help one) then the mission to create continuous improvement is obvious.  Ultimately you probably think about this in terms of EBITDA or ... Read More

Feed: SAS Blogs. Author: Rhett Scheel. Krankenversicherung und neue Technologien – geht das zusammen? Auf alle Fälle! Und das User-Group-Treffen „Analytik in der Krankenversicherung“, das kürzlich in Leipzig stattfand, hat es unter Beweis gestellt. Diese von den Gesundheitsforen Leipzig ausgerichtete Veranstaltung ist ein sehr informatives Forum, auf dem sich analytische Fachexperten aus der Gesetzlichen Krankenversicherung (GKV) und der Privaten Krankenversicherung (PKV) über die zeitgemäße Nutzung analytischer Verfahren zur Beantwortung geschäftlich relevanter Fragestellungen in der Krankenversicherung austauschen können. Hier treffen sich Fachexperten mit analytischer Praxiserfahrung. Data Scientists aus den Krankenversicherungen stellen ihre Projekte selbst vor, diskutieren die dabei erzielten Erfolge, die ... Read More

Feed: R-bloggers. Author: business-science.io - Articles. The culmination of the previous weeks has been preparation for machine learning modeling. At this stage, we have defined the business problem, we’ve explored and understood how the data relates to the business problem, and we’ve preprocessed the data in preparation for modeling. This week will focus on the following topics: Introduction to automated machine learning, predictive modeling with H2O Apply automated machine learning to the Attrition problem Develop high-performance models and learn how automated machine learning works Develop a visualization of the models to communicate the best and worst performing models Here is ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. By Ajit Jaokar and Cheuk Ting Ho. Version One: Release date: Oct 24. Exclusively on Data Science Central with free access. Introduction Enterprise AI: An applications perspective takes a use case driven approach to understanding the deployment of AI in the Enterprise. Designed for strategists and developers, the book provides a simple and practical roadmap based on application use cases for AI in Enterprises. The authors (Ajit Jaokar and Cheuk Ting Ho) are data scientists and AI researchers who have deployed AI applications for Enterprise domains. The book is ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Paul Black. Harvard Business Review has regareded data scientist as the sexiest job of the 21st century. In this article, with the assistance of Octoparse V7, we aggregated the resources and tools that you may need to become a data scientist.1. Learning resources: Courses, Degrees/Certificates, Books; 2. Tools: Data Extractors, Data Analytics, Reporting. 3. Data Science Competitions/Programs 20 Online Courses about Data Science 1. Data Science Specialization Creator: John Hopkins University This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Rafael Knuth. Microsoft Excel has been around for over 30 years now, and chances are it's not going to change in the foreseeable future. In fact, Excel is facing immense competition from challengers such as Google Spreadsheets and well-funded start-ups like Airtable, which are both going after Excel's massive user base of approximately 500 million worldwide. However, making a dent in the enterprise space is huge challenge. It's nearly impossible to rip and replace Excel along with Office 365 and the massive underlying infrastructure, which is serving ever-growing workloads. Not that ... Read More

Feed: All - O'Reilly Media. Author: Ben Lorica, Roger Chen. This is a keynote from the Artificial Intelligence Conference in London 2018. You can also see other highlights from the event. Ben Lorica is the Chief Data Scientist at O'Reilly Media, Inc. and is the Program Director of both the Strata Data Conference and the Artificial Intelligence Conference. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, ... Read More