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Revolution Analytics R Download Extra Quality

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<div>This acquisition will help customers use advanced analytics within Microsoft data platforms on-premises, in hybrid cloud environments and on Microsoft Azure. By leveraging Revolution Analytics technology and services, we will empower enterprises, R developers and data scientists to more easily and cost effectively build applications and analytics solutions at scale.</div><div></div><div></div><div></div><div></div><div></div><div>revolution analytics r download</div><div></div><div>Download File: https://t.co/YFcR8pYcmt </div><div></div><div></div><div>As a data scientist and longtime R enthusiast, I am incredibly excited about Revolution Analytics technology and employees joining Microsoft. I firmly believe that we are at the threshold of a revolution in information technology driven by the use of statistics and scientific analyses on big data. We will provide an update when the acquisition transaction is closed.</div><div></div><div></div><div>Revolution Analytics (formerly REvolution Computing) is a statistical software company focused on developing open source and "open-core"[2] versions of the free and open source software R for enterprise, academic and analytics customers. Revolution Analytics was founded in 2007 as REvolution Computing providing support and services for R in a model similar to Red Hat's approach with Linux in the 1990s as well as bolt-on additions for parallel processing. In 2009 the company received nine million in venture capital from Intel along with a private equity firm and named Norman H. Nie as their new CEO. In 2010 the company announced the name change as well as a change in focus. Their core product, Revolution R, would be offered free to academic users and their commercial software would focus on big data, large scale multiprocessor (or "high performance") computing, and multi-core functionality.</div><div></div><div></div><div>REvolution Computing was founded in New Haven, Connecticut in 2007 by Richard Schultz, Martin Schultz, Steve Weston and Kirk Mettler. At the time Martin Schultz was also the Watson Professor of Computer Science at Yale University.[5][6] Adding parallel computing to R allowed the company to net large gains in speed for many common analytics operations and early clients like Pfizer took advantage of REvolution R to see large performance gains using R on computing clusters.[7] While the improvements to core R were released under the GNU General Public License (GPL), REvolution provides support and services to customers of their commercial product and had considerable early success with life sciences and pharmaceutical companies.[8][9] A year later the company opened an additional office in Seattle.[10]</div><div></div><div></div><div>Unlike analytics products offered by SAS Institute, R does not natively handle datasets larger than main memory. In 2010 Revolution Analytics introduced ScaleR, a package for Revolution R Enterprise designed to handle big data through a high-performance disk-based data store called XDF (not related to IBM's Extensible Data Format) and high performance computing across large clusters.[16] The release of ScaleR marked a push away from consulting and services alone to custom code and a la carte package pricing.[17] ScaleR also works with Apache Hadoop and other distributed file systems and Revolution Analytics has partnered with IBM to further integrate Hadoop into Revolution R.[18][19] Packages to integrate Hadoop and MapReduce into open source R can also be found on the community package repository, CRAN.[20][21]</div><div></div><div></div><div>Ben Shields: It comes down to one word: competition. For as long as sports have been played, teams and athletes are looking for a competitive edge. And it just so happens that today, and well into the future, data and analytics are going to be a source of a competitive edge for teams.</div><div></div><div></div><div></div><div></div><div></div><div></div><div>Paul Michelman: Shields says that there are three main areas in which sports analytics are succeeding and that have clear transferability to other businesses. The first is data strategy.</div><div></div><div></div><div>While strategy development will always require creative and thoughtful executives to set aspirations and make bold choices, analytics tools can give you an edge. Advanced analytics can be used to accomplish the following:</div><div></div><div></div><div>Advanced analytics can also enhance strategic planning by unearthing growth opportunities that would otherwise be hard to spot, be they attractive industry segments and acquisition targets, ideas for new products or services, or even new applications for existing offerings. The most effective of these algorithms use sophisticated network analysis and natural-language processing to parse and find connections among hundreds of disparate text data sources such as company descriptions, patent filings, M&A data, and academic papers.</div><div></div><div></div><div>In a world of increasing uncertainty, companies need to be dynamic in how they set and manage their strategic plans. That requires combining no-regret moves that work in any condition and can be executed immediately with a few bigger, bolder bets that would be executed once the executive team is comfortable a conducive scenario is unfolding. By using advanced analytics to track emerging trends, you can trigger these contingent moves before your competitors do.</div><div></div><div></div><div>Relative to most corporate functions, strategy has not yet captured the benefits of advanced analytics, missing out on potentially critical insights. By tapping these technologies to complement the creativity of your team, you can materially improve your strategic outcomes.</div><div></div><div></div><div>Though its Revolution R products, the company aims to make the power of predictive analytics accessible to every type of user & budget. They provide free and premium software and services that bring high-performance, productivity and ease-of-use to R -- enabling statisticians and scientists to derive greater meaning from large sets of critical data in record time.</div><div></div><div></div><div>Data science, machine learning, and analytics have re-defined how we look at the world. The R community plays a vital role in that transformation and the R language continues to be the de-facto choice for statistical computing, data analysis, and many machine learning scenarios.</div><div></div><div></div><div>With Alteryx, this process is revolutionized. Through our workflow, you can experience firsthand how Alteryx extracts, processes, and performs data analysis in a fraction of the traditional time. Not only does it provide you with the answers you seek, but it also offers insights into player consistency, performance against specific teams, and much more.</div><div></div><div></div><div>Microsoft has struck a deal to acquire Revolution Analytics, a commercial provider of software and services for R, the open-source programming language for developing statistical analysis and predictive analytics applications.</div><div></div><div></div><div>"This requires high-performance computation that is 'close' to the data and scales with the business' needs over time. At the same time, companies need to reduce the data science and analytics skills gap inside their organizations so more employees can use and benefit from R. This acquisition is part of our effort to address these customer needs," he said.</div><div></div><div></div><div>"We're excited the work we've done with Revolution R will come to a wider audience through Microsoft," Smith said. "Our combined teams will be able to help more users use advanced analytics within Microsoft data platform solutions, both on-premises and in the cloud, with Microsoft Azure. And just as importantly, the big-company resources of Microsoft will allow us to invest even more in the R Project and the Revolution R products. We will continue to sponsor local R user groups and R events, and expand our support for community initiatives."</div><div></div><div></div><div>Microsoft customers will be able to use Revolution Analytics' advanced analytics products within Microsoft on-premises data platforms, on Microsoft's Azure cloud platform and in hybrid cloud environments, Sirosh said.</div><div></div><div></div><div>The underlying problem is that very few major users of R also use Oracle. R is primarily used by academics (Excel, MySQL), finance types (Postgres), and more cutting-edge analytics teams. Oracle is used by old businesses that value reliability over innovation, the exact opposite of what most R uses are looking for. So this explains why support for Oracle has fallen away, in my view.</div><div></div><div></div><div>Eric T. Peterson is author of Web Analytics Demystified, Web Site Measurement Hacks and The Big Book of Key Performance Indicators and a long-time member of the web analytics community. He frequently presents on web analytics and is often cited in articles about digital measurement. In the past Mr. Peterson has worked with well-known brands like Microsoft, HP, Cisco, Best Buy, Disney, LEGO, CBS and CBS News, and ESPN. More recently, Mr. Peterson has founded The Analysis Exchange, a completely new way to gain experience with digital measurement.As an employee of Analytics Demystified Eric is a member of the Digital Analytics Association (DAA), an Adobe Business Partner, and a Google Analytics Certified Partner.</div><div></div><div></div><div>The 9.0 release of Alteryx Analytics adds several new tools to our predictive analytics workbench, but much of the development effort for this release has been devoted to improvements that are under the hood. Chief among these has been the integration with Revolution Analysis to provide the ability to scale predictive analytics to large data volumes via the use of their Revo ScaleR technology. Several of the things we did to make the integration with Revolution Analytics possible also have benefits in other areas, including enhancing the scoring using open source R tools, and developing a scalable infrastructure that we will be able to leverage in future releases.</div><div></div><div></div><div>Revolution Analytics just released a new version of its suite of R-based statistical analysis tools Revolution R Enterprise 4.3. Significant new features include the ability to import data files from SPSS, a new predictive analytics algorithm for big data and an installer for Windows. Revolution R already supported importing from SAS files.</div><div></div><div></div><div>Against this backdrop, some pension schemes are changing the way they approach resourcing, too. A number of larger schemes have scaled up internal capabilities both in terms of in-house company teams and pension scheme executives on the trustee side. One benefit of having larger internal teams is that schemes can use external consultants effectively and efficiently. Particularly so in areas such as idea generation, solving challenges, reviews, or helping to scale up resources for larger projects, rather than for more operational work that can easily be handled in-house, for instance. Central sources of data and analytics provide control and can help facilitate this new model of working.</div><div></div><div> 31c5a71286</div>

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Revolution Analytics R Download Extra Quality Nancie Fazzari <fazzarinancie@gmail.com> - 2024-01-24 16:56 -0800

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