In this new insideBIGDATA Guide to Scientific Research, the goal is to provide a road map for scientific researchers wishing to capitalize on the rapid growth of big data technology for collecting, transforming, analyzing, and visualizing large scientific data sets.
Today Cornell University announced a five-year, $5 million project sponsored by the National Science Foundation to build a federated cloud comprised of data infrastructure building blocks (DIBBs) designed to support scientists and engineers requiring flexible workflows and analysis tools for large-scale data sets, known as the Aristotle Cloud Federation.
In this video from the PyData Seattle Conference, Lorena Barba from George Washington University presents: Data-driven Education and the Quantified Student. “Education has seen the rise of a new trend in the last few years: Learning Analytics. This talk will weave through the complex interacting issues and concerns involving learning analytics, at a high level. The goal is to whet the appetite and motivate reflection on how data scientists can work with educators and learning scientists in this swelling field.”
This article is the second in an editorial series with a goal to provide a road map for scientific researchers wishing to capitalize on the rapid growth of big data technology for collecting, transforming, analyzing, and visualizing large scientific data sets.
Students from more than 20 prestigious colleges and universities recently tried their hand at “Big Data” analysis at seven different campuses around the country during DataFest, an annual month-long data-analytics competitive event sponsored by the American Statistics Association.
Statistics—the science of learning from data—is the fastest-growing science, technology, engineering and math (STEM) undergraduate degree in the United States over the last four years, an analysis of federal government education data conducted by the American Statistical Association (ASA) revealed.
In this special guest feature, Cristian Borcear of NJIT reflects on the evolution of technology and public policy in support of so-called “smart cities. ” Cristian Borcea is an Associate Professor and the Associate Chair of the Department of Computer Science at New Jersey Institute of Technology.
“The Hadoop framework has become the most popular open-source solution for Big Data processing. Traditionally, Hadoop communication calls are implemented over sockets and do not deliver best performance on modern clusters with high-performance interconnects. This talk will examine opportunities and challenges in optimizing performance of Hadoop with Remote DMA (RDMA) support, as available with InfiniBand, RoCE (RDMA over Converged Enhanced Ethernet) and other modern interconnects.”
FIELD REPORT Last week I attended the long-anticipated useR!2014 international conference at the UCLA campus, my alma mater. The four day event had something for everyone in attendance – all the brain cycles centered around the use of the R statistical environment. Since R is a primary tool for my work in data science and […]
When Stanislav Dusko Ehrlich – a world expert in microbiology and a pioneer of metagenomics – and his team set out to create their next generation biotech research platform, they needed a technology solution to support their stringent capacity and performance requirements for big data analytics.