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E20-065 - Advanced Analytics Specialist Exam for Data Scientists - BrainDump Information

Vendor Name : EMC
Exam Code : E20-065
Exam Name : Advanced Analytics Specialist Exam for Data Scientists
Questions and Answers : 66 Q & A
Updated On : October 18, 2018
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E20-065 exam Dumps Source : Advanced Analytics Specialist Exam for Data Scientists

Test Code : E20-065
Test Name : Advanced Analytics Specialist Exam for Data Scientists
Vendor Name : EMC
Q&A : 66 Real Questions

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EMC EMC Advanced Analytics Specialist

Can IBM Win The Storage struggle in opposition t Dell-EMC? | killexams.com Real Questions and Pass4sure dumps

No outcome discovered, are trying new keyword!The movement is aimed toward grabbing the market share of Dell/EMC publish merger ... IBM's aggressive aspect in analytics by means of IBM Watson will permit the enterprise to present customers an advanced real-time analytics ...

EMC partners With Hippo To benefit customer event management Foothold | killexams.com Real Questions and Pass4sure dumps

EMC and Hippo announced a partnership these days by which Hippo, an open supply internet content material management platform, will combine with EMC Documentum, EMC’s business content material administration utility.

as with all respectable offers, every accomplice features whatever thing. EMC fills in a missing piece in its content management arsenal without having to spend a lot of money. Hippo positive aspects access to a an awful lot broader commercial enterprise market and integration with different equipment in the EMC family unit, whereas retaining its independence.

As part of the settlement between the two companies, Hippo will develop into a part of the EMC enterprise companion software for technology join partners.

For EMC Documentum, which is an extended-based manufacturer attempting to combat disruption in the content management market, Hippo gives them a contemporary, open supply internet content management device designed to assist users manipulate client experience.

nowadays’s internet content management equipment deserve to do more than simply create, edit and maintain web content, they deserve to help create a customised digital experience. The theory is to carry content according to what you learn about visitors such as the equipment they are the use of and whether here's the first-time or ordinary visitor. These clues assist the gadget serve more individualized content for the correct monitor at the correct time in the correct approach, as adverse to treating every traveller as though they are travelling for the first time on a computing device laptop. At its superior, consumer experience management mimics the better of brick and mortar customer event (realizing your customers), whereas taking skills of being digital.

Chris McLaughlin, CMO for the business content material Division at EMC, says Hippo’s products will assist EMC clients convey contextually crucial, digital experiences across all channels. “It makes content a differentiator through measuring which content material matters and delivering surest, personalised digital experiences based on this perception. customers can create precise competitive potential by using supplying price through content and functions,” McLaughlin wrote in an e mail.

in the event you combine that with other EMC products like EMC Documentum for managing entry to that content and Pivotal Gemfire for advanced analytics, he sees a powerful mixture of equipment.

For Hippo, which is primarily based in Amsterdam, and has a US workplace in Boston, the deal gives them access to a plenty wider market, as smartly as the massive EMC marketing and earnings engine. And, in all probability more importantly it offers them business road cred.

“The indisputable fact that they are inclined to get into a partnership is a huge deal. It’s validation, a stamp of approval that our product is enterprise in a position,” Arje Cahn, Hippo CTO instructed TechCrunch. He said the business had that confidence all alongside, but the deal sends a message to the market.

Hippo become trying to find a way to extend its US presence with some type of partnership, although it didn’t always predict that to be with EMC. “We were actively engaged on [increasing our US market]. This came along. It’s not totally unintentional, however changed into a pretty good fit since it allowed us to try this.” He delivered, they are are a particularly decent match on account of what EMC companion Pivotal, which presents open source large information and cloud platform functions, brings to the deal.

history-hippo-cms

Cahn is thrilled with the potential of the partnership and what it brings Hippo, however he says that doesn’t mean his business is asking to get obtained. He say Hippo has an extended-time period product plan and simply wants to see the place the partnership takes them.

“We aren't targeting acquisition at this time.” he referred to.

Hippo was dependent in 1999, but changed into a functions business until 2008 when it started promoting the Hippo CMS. It launched the business version of the product in 2010. The business has bootstrapped all these years, and has got no funding to this element. consumers consist of Disney, Condé Nast and Randstad.


Dell EMC Unveils extensive Enhancements to Cloud-Enabled systems, Infrastructure, options and capabilities Portfolio | killexams.com Real Questions and Pass4sure dumps

Dell EMCDell EMC, the no 1 company of cloud infrastructure, is announcing a few enhancements to its cloud-enabled infrastructure solutions portfolio to help shoppers maximize the value of their IT ecosystem and multi-cloud environments. Coupled with innovation from throughout Dell applied sciences, Dell EMC is providing a vast portfolio of cloud options that caters to all types of purposes and creates a constant experience across cloud structures.

The increasing amount of data led to via mobility, synthetic intelligence (AI), analytics, cyber web of things (IoT), part computing, along with common workloads, is riding demand for all kinds of clouds, including public, managed and on-premises. In a survey performed by Forrester, commissioned by using Pivotal, a Dell applied sciences enterprise, and Microsoft, one hundred% of respondents are the use of distinct cloud infrastructure and/or cloud software structures. moreover, 99% trust a consistent, unified method to deployment, operation and use of mixed infrastructure-as-a-provider (IaaS) and platform-as-a-service platforms allows for more desirable IT service to enterprise. Cloud resolution-makers during this analyze additionally noted they search for integrated PaaS and IaaS with consistent identity and access controls, construction event, runtime environments and migration capabilities.

“As extra applications and conventional workloads move into a lot of clouds, the proliferation of cloud siloes will become inevitable, inhibiting agencies’ capacity to completely exploit business differentiators like data analytics and burgeoning AI initiatives,” talked about Sam Grocott, senior vp at Dell EMC. “Dell EMC’s focused innovation throughout its cloud-enabled portfolio of structures, infrastructure, consumption models and capabilities helps organizations evolve greater rapidly to a cloud operating model for the up to date facts center.”

accelerate IT and company transformation with Dell EMC cloud structures

organizations more and more are adopting an advanced mix of cloud platforms and are seeking for a constant consumer event to stability their enjoyable needs, expenses, risks and preferences. Dell EMC offers most reliable-of-breed, pre-engineered solutions to deliver a number of multi-cloud structures and reference architectures. This comprises IaaS with VMware for natural purposes; platform-as-a-carrier (PaaS) structure and containers with Pivotal for cloud-native applications; an enterprise-category public cloud platform with Virtustream; and, Azure functions on-premises with Microsoft Azure Stack. With hardware, utility integration, tooling and documentation mixed, companies can speed up time to results, simplify each day operations and achieve better tiers of effectivity and transparency.

at the moment, Dell EMC and VMware are simplifying and accelerating paths to the application-defined data middle (SDDC) and multi-cloud with Dell EMC VxRail and VxRack SDDC hyper-converged infrastructure (HCI) solutions. VxRack SDDC, pre-built-in and built to bring the newest know-how from VMware Cloud basis, offers the premiere infrastructure foundation for realizing a VMware multi-cloud. With new capabilities to suit the wants of customers’ own workloads, VxRack SDDC points the VMware SDDC application suite pre-built-in on a big range of VxRail hardware. It also elements automation and serviceability extensions in SDDC supervisor, with extensibility to a choice of public cloud suppliers.

With Dell EMC VxRail as a pre-engineered and pre-validated IaaS platform and infrastructure basis for PaaS, clients can more simply and quickly architect, implement and function an entire SDDC, including VMware NSX and the VMware vRealize Suite, with much less possibility. New alternatives of the VMware Validated Designs for VxRail now support dispensed multi-availability zones structure and multi-web page deployments with catastrophe restoration. This permits shoppers to further cut back chance and simplify operations and maintenance with a validated, standardized SDDC design in line with VMware’s most reliable practices. 

moreover, VMware Cloud meeting, the new utility-as-a-carrier (SaaS) primarily based cloud administration solution element of VMware Cloud features, will function entertaining integration with VxRail to pace time-to-price. once commonly accessible, Dell EMC VxRail could have a synchronized liberate and be the primary and most effective HCI appliance collectively developed to provide integrations that seamlessly work with VMware Cloud services.

Create constant operations and versatile deployment options with Dell EMC cloud-enabled infrastructure

today’s groups demand that infrastructure support bigger-cost IT capabilities, akin to AI or IoT, to produce enhanced enterprise outcomes. consequently, Dell EMC makes cloud-enablement infrastructure a essential factor of its improvements and investments. To help clients’ IT and cloud operations perpetually across on and off-premises cloud fashions, Dell EMC offers the following capabilities:

·         Cloud statistics Mobility allows for businesses to move statistics to and from cloud storage seamlessly, proposing the pliability to augment on-premises statistics storage with cloud-primarily based storage. Updates to the statistics domain Cloud Tier for long-time period retention assist extra cut back transactional overhead by means of expanding the thing dimension written to the cloud. Dell EMC also has better information storage utilization by means of tight integration with Virtustream Storage Cloud and Dell EMC Elastic Cloud Storage.

·         Cloud information insurance policy protects statistics living on a number of cloud systems. Dell EMC information area Cloud DR has been better for application-constant cloud disaster restoration in AWS, in addition to recuperation to VMware Cloud on AWS. Dell EMC Cloud image manager provides backup and recovery for public cloud workloads, and now presents coverage for Microsoft Azure. VMware vCloud Director points a new facts protection extension that now allows cloud provider suppliers to offer an integrated VMware and statistics insurance plan self-carrier answer to consumers. moreover, Dell EMC records insurance plan Suite become named an superior technology companion in the Amazon internet capabilities (AWS) companion community and a Microsoft companion Gold Competency in Cloud platforms.

·         Cloud manage enables environments to be managed from anyplace. Powered via machine learning, CloudIQ, a free cloud-based mostly software that permits clients to proactively music storage fitness, predict capacity shortages and become aware of performance anomalies, is now introducing support for PowerMax, VMAX, and XtremIO. This help is in addition to Dell EMC harmony and SC collection. New VMware integration offers virtual machine-stage efficiency and ability insights, and new cell apps give entry to CloudIQ from anywhere.

·         Cloud records features – elements comprehensive statistics capabilities spanning all file kinds for applications running in the cloud, together with Isilon Cloud for Google Cloud Platform, Elastic Cloud Storage and Virtustream Storage Cloud. harmony Cloud version is the latest addition to this household, offering a fully-featured utility-defined unified storage deployed in the cloud. These capabilities allow users to without difficulty deploy Dell EMC cohesion block and file storage with VMware Cloud on AWS. additionally, users can leverage the flexibleness of cloud deployment to assist disaster recuperation and as-vital operational calls for, including check and construction, and facts analytics.

know public cloud-like flexibility with Dell’s cloud consumption models

With bendy cloud consumption solutions via Dell financial services (DFS), Dell EMC makes it handy for purchasers to purchase cloud-enabled infrastructure and achieve transformational goals .3For instance, consumers may also decide to leverage charge and flexibility benefits of consumption solutions to accommodate unpredictable usage of recent purposes and seasonal spikes that power buffer ability requirements.

Cloud Flex for HCI minimizes the possibility of moving to Dell EMC VxRail appliances or Dell EMC Cloud for Microsoft Azure Stack. It requires no up-entrance funding and has declining funds over time with out a responsibility after the first 365 days. Dell EMC also presents equipped skill, which gives on-demand storage and buffer means that scales to fit utilization wants, and Flex on Demand, which allows for clients to installation base skill now and pay for buffer ability because it is used. With the flexibleness to eat simplest what is needed as an operating cost, customers can increase efficiency and reallocate elements to innovation.

take advantage of cloud investments with Dell’s confirmed cloud consulting and know-how capabilities 

To support shoppers more instantly understand the cost of multi-cloud environments, Dell EMC also presents a big range of expert cloud consulting and know-how services. Dell EMC features consultants aid clients create roadmaps, profile functions, enforce cloud management and automation platforms and transition to a cloud working mannequin. consultants can be found to assist deploy, transition to, and optimize cloud technologies and provide comprehensive hardware and utility help 24x7 to aid ensure most advantageous device performance and cut downtime. With Dell EMC Managed features, purchasers can profit stronger efficiencies whereas professional advisors manage the cloud infrastructure for them. Dell EMC education functions can also deliver the multi-cloud expertise and validation customers need to correctly plan, design and control successful cloud infrastructures.

Empower customer alternative in one-cease with Dell EMC Cloud market

the brand new Dell EMC Cloud market brings the vigour of the Dell technologies household collectively in a single vicinity. The industry offers clients a choice of cloud platforms, superior cloud-enabled infrastructure capabilities, in addition to consulting and know-how capabilities, and consumption models in a self-service portal.

study extra information:

Xiaomi India creates a new GUINNESS WORLD facts achievement

WittyFeed's mother or father company Vatsana applied sciences diversifies its content material with six new brands

source: IANS


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Advanced Analytics Specialist Exam for Data Scientists

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NVIDIA Introduces RAPIDS Open-Source GPU-Acceleration Platform for Large-Scale Data Analytics and Machine Learning | killexams.com real questions and Pass4sure dumps

HPE, IBM, Oracle, Open-Source Community, Startups Integrate RAPIDS, Giving Giant Performance Boost to End-to-End Predictive Data Analytics

MUNICH, Germany, Oct. 10, 2018 (GLOBE NEWSWIRE) --  GTC Europe—NVIDIA today announced a GPU-acceleration platform for data science and machine learning, with broad adoption from industry leaders, that enables even the largest companies to analyze massive amounts of data and make accurate business predictions at unprecedented speed.

RAPIDS™ open-source software gives data scientists a giant performance boost as they address highly complex business challenges, such as predicting credit card fraud, forecasting retail inventory and understanding customer buying behavior. Reflecting the growing consensus about the GPU’s importance in data analytics, an array of companies is supporting RAPIDS — from pioneers in the open-source community, such as Databricks and Anaconda, to tech leaders like Hewlett Packard Enterprise, IBM and Oracle.

Analysts estimate the server market for data science and machine learning at $20 billion annually, which — together with scientific analysis and deep learning — pushes up the value of the high performance computing market to approximately $36 billion.

“Data analytics and machine learning are the largest segments of the high performance computing market that have not been accelerated — until now,” said Jensen Huang, founder and CEO of NVIDIA, who revealed RAPIDS in his keynote address at the GPU Technology Conference. “The world’s largest industries run algorithms written by machine learning on a sea of servers to sense complex patterns in their market and environment, and make fast, accurate predictions that directly impact their bottom line.

“Building on CUDA and its global ecosystem, and working closely with the open-source community, we have created the RAPIDS GPU-acceleration platform. It integrates seamlessly into the world’s most popular data science libraries and workflows to speed up machine learning. We are turbocharging machine learning like we have done with deep learning,” he said.

RAPIDS offers a suite of open-source libraries for GPU-accelerated analytics, machine learning and, soon, data visualization. It has been developed over the past two years by NVIDIA engineers in close collaboration with key open-source contributors.

For the first time, it gives scientists the tools they need to run the entire data science pipeline on GPUs. Initial RAPIDS benchmarking, using the XGBoost machine learning algorithm for training on an NVIDIA DGX-2™ system, shows 50x speedups compared with CPU-only systems. This allows data scientists to reduce typical training times from days to hours, or from hours to minutes, depending on the size of their dataset.

Close Collaboration with Open-Source CommunityRAPIDS builds on popular open-source projects — including Apache Arrow, pandas and scikit-learn — by adding GPU acceleration to the most popular Python data science toolchain. To bring additional machine learning libraries and capabilities to RAPIDS, NVIDIA is collaborating with such open-source ecosystem contributors as Anaconda, BlazingDB, Databricks, Quansight and scikit-learn, as well as Wes McKinney, head of Ursa Labs and creator of Apache Arrow and pandas, the fastest-growing Python data science library.

“RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow,” McKinney said. “NVIDIA’s collaboration with Ursa Labs will accelerate the pace of innovation in the core Arrow libraries and help bring about major performance boosts in analytics and feature engineering workloads.”

To facilitate broad adoption, NVIDIA is integrating RAPIDS into Apache Spark, the leading open-source framework for analytics and data science.

“At Databricks, we are excited about RAPIDS’ potential to accelerate Apache Spark workloads,” said Matei Zaharia, co-founder and chief technologist of Databricks, and founder of Apache Spark. “We have multiple ongoing projects to integrate Spark better with native accelerators, including Apache Arrow support and GPU scheduling with Project Hydrogen. We believe that RAPIDS is an exciting new opportunity to scale our customers’ data science and AI workloads.”

Broad Ecosystem Support and AdoptionTech-leading enterprises across a broad range of industries are early adopters of NVIDIA’s GPU-acceleration platform and RAPIDS.

“NVIDIA’s GPU-acceleration platform with RAPIDS software has immensely improved how we use data — enabling the most complex models to run at scale and deliver even more accurate forecasting,” said Jeremy King, executive vice president and chief technology officer at Walmart. “RAPIDS has its roots in deep collaboration between NVIDIA’s and Walmart’s engineers, and we plan to build on this relationship.”

Additionally, some of the world’s leading technology companies are supporting RAPIDS through new systems, data science platforms and software solutions:

“HPE is committed to advancing the way customers live and work. Artificial intelligence, analytics and machine learning technology can play a critical role in uncovering insights that can help customers achieve breakthrough results and improve the world we live in. HPE is unique in the market in that we provide complete AI and data analytics solutions from strategic advisory to purpose-built GPU accelerator technology, operational support and a strong partner ecosystem to tailor the right solution for each customer. We are excited to partner with NVIDIA on RAPIDS to accelerate the application of data science and machine learning to help our customers drive faster and more insightful outcomes.”— Antonio Neri, CEO, Hewlett Packard Enterprise

“IBM has built the world’s leading platform for enterprise AI, regardless of deployment model. We look forward to extending our successful partnership with NVIDIA, leveraging RAPIDS to provide new machine learning tools for our clients.”— Arvind Krishna, senior vice president of Hybrid Cloud and director of IBM Research

“The compute world today requires powerful processing to handle complex workloads like data science and analytics — it’s a job for NVIDIA GPUs. RAPIDS is accelerating the speed at which this processing and machine learning training can be done. We are excited to support this new suite of open-source software natively on Oracle Cloud Infrastructure and look forward to working with NVIDIA to support RAPIDS across our platform, including the Oracle Data Science Cloud, to further accelerate our customers’ end to-end data science workflows. RAPIDS software runs seamlessly on the Oracle Cloud, allowing customers to support their HPC, AI and data science needs, all while taking advantage of the portfolio of GPU instances available on Oracle Cloud Infrastructure.”— Clay Magouyrk, senior vice president of Software Development, Oracle Cloud Infrastructure

Support from other leading innovators — including Cisco, Dell EMC, Lenovo, NERSC, NetApp, Pure Storage, SAP and SAS, as well as a wide range of data science pioneers — is appended to this press release.

AvailabilityAccess to the RAPIDS open-source suite of libraries is immediately available at http://www.rapids.ai, where the code is being released under the Apache license. Containerized versions of RAPIDS will be available this week on the NVIDIA GPU Cloud container registry.

About NVIDIA NVIDIA‘s (NVDA) invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. More information at http://nvidianews.nvidia.com/.

Additional Supporting Quotations

Anaconda - Scott Collison, CEO“NVIDIA has made the training and deployment of complex AI models scalable and economically viable. Today’s RAPIDS announcement by NVIDIA extends the same benefits to earlier data transformation stages of the data science lifecycle. Anaconda is proud to have helped NVIDIA develop these new capabilities, which will be available to the community of 7 million users of the Anaconda Distribution through our public package repository. We’ll also be including them in Anaconda Enterprise, which, combined with NVIDIA DGX, delivers a high-performance, proven solution for business. Anaconda Enterprise on NVIDIA DGX will enable IT organizations of all sizes to accelerate data science and AI workflows.”

BlazingDB - Rodrigo Aramburu, CEO"We are thrilled to be early contributors to the RAPIDS open-source software from NVIDIA, and have built BlazingSQL, a free to use version of our distributed GPU SQL engine, on RAPIDS. Our partnership with NVIDIA has provided immense value to us as a startup as we collaborated with the RAPIDS team, joined as key contributors to cuDF, and will continue to support the RAPIDS software as we build our vision of integrating Data Lakes with AI, all using SQL.”

Cisco - Kaustabh Das, vice president of Product Management, Data Center Group“Cisco and NVIDIA are collaborating on AI/ML software stacks on NVIDIA GPU-optimized Cisco UCS platforms to simplify and accelerate AI/ML workload deployment. We are excited to learn that, with RAPIDS, NVIDIA is expanding their GPU applicability with accelerated software stacks to address traditional machine learning and big data analytics. We look forward to the possibilities for our GPU-accelerated server portfolio, including the recently launched Cisco UCS C480 ML M5 Rack Server, a best in class, purpose-built server with eight NVIDIA V100 GPUs and NVIDIA NVLink interconnect.”

Dell EMC - Ravi Pendekanti, senior vice president of Product Management and Marketing, Servers & Infrastructure Systems“Dell EMC is committed to providing our customers with world-class IT infrastructures that enable them to gain real, competitive business advantage. We work with our ecosystem partners to ensure our customers have the latest data science tools available to help them transform data insights into business outcomes. Our goal is to combine the new GPU-accelerated open-source data science software from NVIDIA with our portfolio of NVLink-enabled Dell EMC PowerEdge servers to significantly accelerate the fields of machine learning and big data analytics.”

FASTDATA.io - Alen Capalik, founder and CEO"The RAPIDS open-source project launched by NVIDIA is going to revolutionize the data science pipeline. At FASTDATA.io, we're excited that our Plasma Engine — the first software to fully leverage NVIDIA GPUs for real-time processing of infinite data in motion — will play a part in that revolution."

Georgia Tech - David Bader, professor“Georgia Tech is excited to contribute to RAPIDS, an open-source playground for NVIDIA GPU-accelerated analytics. In this age of massive data, our contribution to the RAPIDS graph libraries will help data scientists gain meaningful knowledge from ever-growing datasets.”

Graphistry - Leo Meyerovich, co-founder and CEO“Graphistry, one of the first GPU cloud startups, has been quietly bringing new levels of visibility to sensitive F500 and federal teams that must comb through records in finance, cybersecurity, operations, and sales. As an early contributor to RAPIDS and a force behind Apache Arrow, Graphistry has taken a big bet on RAPIDS. The firm is already known for having redefined the visual compute fabric to be a real-time blending of browser and cloud GPUs, and is working with the RAPIDS team to add next-level tabular analytics to its existing graph GPU visual analytics core."

H2O.ai - Sri Ambati, founder and CEO“Machine learning is transforming businesses and NVIDIA GPUs are speeding them up. With the support of the open source communities and customers, H2O.ai made machine learning on GPUs mainstream and won recognition as a leader in data science and machine learning platforms by Gartner. NVIDIA's support of the GPU machine learning community with RAPIDS, its open-source data science libraries, is a timely effort to grow the GPU data science ecosystem and an endorsement of our common mission to bring AI to the data center. Thanks to our partnership, H2O Driverless AI powered by NVIDIA GPUs has been on an exponential adoption curve — making AI faster, cheaper and easier."

INRIA (scikit-learn) - Gael Varoquaux, director of Scikit-Learn Operations"NVIDIA is demonstrating real progress in accelerating data science with new productivity tools such as RAPIDS. Combining very fast computation in a high-language is a game changer for data-analytics teams. We are excited that NVIDIA has chosen to make RAPIDS compatible with scikit-learn. We believe that it can benefit our community and look forward to collaborating with NVIDIA."

Kinetica - Nima Negahban, co-founder and CTO“The RAPIDS suite of open-source libraries is a significant improvement in enabling data scientists to leverage the power of the GPU across their model development toolchain. RAPIDS can dramatically simplify and optimize training and improve model accuracy, without any significant logical redesign effort on the part of the data scientist. We’re excited to partner with NVIDIA in this journey to democratize AI — with NVIDIA driving model development and training and Kinetica driving operationalization and deployment of those models, enabling enterprises to gain maximum insight from their data.”

Lenovo - Kirk Skaugen, president of Data Center Group“Enterprise customers and academia continue to be challenged in working with and analyzing massive amounts of data as they develop and test new strategies. The new RAPIDS open-source software promises to accelerate workflows by running them end-to-end on NVIDIA GPUs. We believe this innovation and collaboration will make a significant impact for customers.”

MapR - John Schroeder, CEO“RAPIDS is a breakthrough announcement for data science and, more importantly, the ability to directly impact an organization with data science. MapR is supporting this effort by focusing on complementary data management and deployment activities to accompany the end-to-end RAPIDS data science training and model workflow.”

NERSC - Rollin Thomas, Python data analytics lead"NERSC supports more than 7,000 researchers at universities, national labs and in industry. They increasingly want productive, high-performance ways of interacting with their data from complex science simulations or experimental and observational facilities like particle accelerators and telescopes. We look forward to working with NVIDIA to put new high-performance Python data analytics tools like RAPIDS in the hands of our users to accelerate their pace of discovery across many scientific disciplines."

NetApp - Octavian Tanase, senior vice president of ONTAP“Organizations must take advantage of new artificial intelligence capabilities to drive competitive advantage and accelerate digital transformation. The combination of RAPIDS powered by NVIDIA GPUs and NetApp’s AFF A800 cloud-connected all-flash storage will help customers confidently tap into growing data resources with virtually unlimited scalability and performance needed to feed, train and operate data-hungry AI applications.”

NumFOCUS - Andy Terrel, president of the board of directors"NVIDIA’s support of NumFOCUS represents an investment to the community. As two leaders in data science, we feel our work together will bring better tools to science and business alike."

OmniSci - Todd Mostak, CEO and co-founder“Data scientists use OmniSci on NVIDIA GPUs to accelerate data exploration and feature engineering when creating machine learning models. Now our users can interactively query and visualize data at scale in OmniSci, and then pipe the results into RAPIDS’ open-source libraries, enabling powerful end-to-end data science workflows. Together, NVIDIA and OmniSci make it much faster to build and iterate on models, resulting in increased accuracy and quicker time to deployment.”

Pure Storage - Matt Burr, general manager of FlashBlade"Our customers look to data for insights that separate them from the competition and deliver ever-increasing value for their end users. RAPIDS amplifies the impact of NVIDIA GPU acceleration and Pure Storage FlashBlade for data science and machine learning workflows to help more data scientists speed their training pipelines while maintaining optimal low-latency performance for faster time to results.”

Quansight - Travis Oliphant, NumPy and SciPy creator, co-founder and director of Anaconda, founder and CEO of Quansight"NVIDIA has long been a leader in accelerated tools for advanced analytics and has consistently offered freely available high-speed libraries for use by developers in the data-science community. I am thrilled to see their expanded open-source framework for data-science and their commitment to an end-to-end software and hardware solution. These innovations will enable a dramatic speed-up of the entire data-science workflow and unleash innovation across the broader open-source ecosystem."

SAP - Juergen Mueller, chief innovation officer“SAP has worked with NVIDIA closely over the past several years to take advantage of GPU acceleration for many SAP Leonardo Machine Learning-enabled solutions. We are furthering that collaboration now to explore the possibilities offered by RAPIDS, which promises to hypercharge data science pipelines on GPUs. This is an important step to accelerate data science and machine learning for data scientists as we bring intelligence to enterprises with SAP Leonardo and SAP HANA.”

SAS - Saratendu Sethi, head of Artificial Intelligence and Machine Learning“We are working closely with NVIDIA to contribute to the new GPU-accelerated data science library. We look forward to future SAS Viya offerings to take advantage of RAPIDS so that our customers can gain valuable insight from their data even faster.”

SQream - Ami Gal, CEO“The work NVIDIA has done on RAPIDS presents an exciting opportunity for dramatically speeding up the data science pipeline. By combining SQream DB’s capability of piping in very large amounts of data into the RAPIDS data science platform, we expect that data scientists will be able to run models faster and on more data than ever before.”

University of California, Davis - John Owens, professor and Gunrock project lead“We are delighted to be part of the RAPIDS community and look forward to working with NVIDIA and its partners in building the highest-performance, most comprehensive ecosystem for data analytics.”

For further information, contact:Kristin BrysonPR Director for Data Center AI, HPC and Accelerated ComputingNVIDIA Corporation+1-203-241-9190kbryson@nvidia.com

Certain statements in this press release including, but not limited to, statements as to: the benefits, impact, performance, and availability of the RAPIDS GPU-acceleration platform; the sizes of the server market for data science and machine learning and of the high performance computing market; the benefits and impact of NVIDIA’s collaboration with Ursa Labs; and Walmart’s relationship plans with NVIDIA are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company’s website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

© 2018 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, DGX and RAPIDS are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.


In Analytics Economy, Demand for Data Scientists Outpaces Supply, McKinsey Says | killexams.com real questions and Pass4sure dumps

As analytics and automation transform industry, demand for data-savvy employees is far outstripping the available supply, according to a new report from the McKinsey Global Institute, the research arm of global consulting firm McKinsey.

The implications for K-12 schools?

Teaching more statistical reasoning and quantitative skills to younger students will help them be better prepared for the tech- and data-driven jobs of the future, said Michael Chui, a partner at MGI, in an interview.

"The ability to understand what probability means is now a basic life skill," Chui said. "It has tremendous implications for us as workers and as citizens."

The McKinsey Global Institute report, titled "The Age of Analytics: Competing in a Data-Driven World," makes the following case about the nation's data-science talent pipeline: In response to employer demand, higher-education institutions have begun offering more degree programs in data science and analytics (the researchers counted 100 such master's-level programs in business analytics alone.) And students have "flocked" to such opportunities, with the number of degrees granted in such fields growing by 7.5 percent between 2010 and 2015.

Still, demand for data scientists is growing much faster, by perhaps as much as 12 percent per year. 

One indicator of the resulting gap in the labor market: average wages for data scientists rose by about 16 percent between 2012 and 2014, compared to a 2 percent increase in wages overall. In a survey conducted by McKinsey earlier this year, business leaders also said finding and retaining analytics talent was far more difficult than in other areas.

The net effect, the McKinsey Global Institute concludes, is that the U.S. economy could be short as many as 250,000 data scientists by 2024.

"Providing people with the skills to get jobs is one goal of education," Chui said.

"At least in the medium term, if students gain data-science credentials and have the skills, they will be in demand."

'All the Math You Can Possibly Take'

The legitimacy of the so-called "skills gap" in STEM subjects (science, technology, engineering and math) has been much debated in recent years.  But when it comes to the specific field of data science, there is little doubt that "the recent 'big data' trend has sparked demand for data scientists in all areas, from health care to retail," the federal Bureau of Labor Statistics wrote in 2015.

Tech companies are certainly part of that equation. Just last week, for example, social-networking behemoth Facebook launched an initiative to help educate students, teachers, and the public about "artificial intelligence," a kind of blend of computer science , data science,, and neuroscience  in which algorithms are used to design "intelligent" machines that can recognize patterns and images, translate languages, and learn some kinds of logical reasoning. (A series of videos featuring Vann LeCun, the company's head of AI research, further explains the field.)

In a blog post, Facebook listed a number of tips for students to prepare for such fields. Chief among them: "Take all the math you can possibly take," including probability and statistics. (And while you're at it, the company recommends, make sure you take some computer science, and try to squeeze in engineering, economics, philosophy of knowledge, and the latest brain research, too.)

It's not just tech companies that are seeking such skills. The fields of transportation (think: self-driving cars), retail, advertising, and health care are among those already undergoing radical transformations. Manufacturing and the public sector (including public education) have also started dipping in their toes. 

Even those jobs that aren't replaced or rendered obsolete will be dramatically changed, McKinsey argues. The reason: as data-driven automation yields new advances in machines' ability to process natural language, recognize patterns, and even sense human emotion, everyone from administrative assistants to lawyers to industrial engineers will see core aspects of their daily work evolve or disappear.

The Importance of Statistics

As a result, the McKinsey Global Institute researchers argued, for every data scientist that employers hire, they will need even more "translators," able to connect new information to real-world business problems.

"These are the people who know enough about data science to be good consumers of data, but who also have enough domain knowledge to bring the resulting insights into organizations and effect change at scale," Chui said.

While Facebook makes the case for more math of all kinds, Chui is particularly keen on making sure students are taught the building blocks of data science at an ever-younger age (one instructional strategy: games involving dice for elementary students).

Three years ago, he outlined his argument in a piece published by Business Insider. 

US students are shuttled along a familiar path in mathematics: first stop algebra, then geometry and trigonometry, and finally, the ultimate destination, calculus.

This time-honored curriculum seems increasingly out of touch in a world that is flooded with noisy and voluminous data. The majority of students need to be immersed in the more practical discipline of statistics, which has greater relevance for the jobs being generated by a digital economy.

The key to making sense of all the data now at our disposal is statistics. At leading companies, decisions once driven by HiPPOs (the Highest-Paid Person's Opinion) are increasingly made by conducting experiments that draw on the core skills of statisticians.

Rather than relying on gut instinct, businesses now find ways to test hypotheses and use statistical methods to analyze the results, applying the classic scientific method to decision-making.

A quick look at the total number of students taking related Advanced Placement exams highlights the ways in which K-12 schools have been relatively slow to respond to such changes: 

  • More than twice as many students still take the AP Calculus exam than the AP Statistics exam each year. In 2016, for example, 433,146 students took the former, compared to 206,563 who took the latter.
  • The number of students taking AP Statistics has increased by more than 34 percent over the past five years. While that's faster than the growth in the number of students taking AP Calculus (20 percent), it pales in comparison to the 122 percent increase in the number of students taking the AP Computer Science exam over the same period.
  • Chui would like to see those numbers change. Human beings are naturally pretty bad at understanding things like probability, he said. But in a rapidly changing labor market, in which machines will perform not only rote labor and calculations, but many more advanced tasks, humans are going to need such higher-order conceptual skills more than ever.

    "The two questions I ask people are, 'When was the last time you had to take an integral, and when was the last time you had to make a decision based on a large, incomplete, and inconsistent set of data?'" he said.

    "Usually, the answers are 'a long time ago' and 'since breakfast.'"

    See also:

    Follow @BenjaminBHerold Follow @EdWeekEdTech for the latest news on ed-tech policies, practices, and trends.


    What IBM looks for in a data scientist | killexams.com real questions and Pass4sure dumps

    Job seekers sometimes ask how IBM defines “data scientist.” It’s an important question since more and more would-be data scientists are fighting for attention in an increasingly lucrative labor market.

    The first step is to distinguish between what we see as true data scientists and other professionals working in adjacent roles (for instance, data engineers, business analysts, and AI application developers). To make that distinction, let’s first define what we mean by data science.

    At its core, data science is applying the scientific method to solve business problems.

    You can further expand on the definition by understanding that we solve those business problems using artificial intelligence to create predictions and prescriptions and to optimize processes.

    The definition demonstrates that to achieve the true potential of data science, we need data scientists with very particular experiences and skills — specifically, we need people with the experiences and skills required to run and complete data science projects:

    1. Training as a scientist, with an MS or PhD2. Expertise in machine learning and statistics, with an emphasis on decision optimization3. Expertise in R, Python, or Scala4. Ability to transform and manage large data sets5. Proven ability to apply the skills above to real-world business problems6. Ability to evaluate model performance and tune it accordingly

    Let’s look at those qualifications in the context of our definition of data science.

    1. Training as a scientist, with a Masters of Science or Doctorate

    This is less about the degree itself and more about what you learn when you get an advanced degree. In short, you learn the scientific method, which starts with the ability to take a complex yet abstract problem and break it down into a set of testable hypotheses. This continues with how well you design experiments to test your hypotheses, and how you analyze the results to see whether the hypotheses are confirmed or contradicted. A determined person can learn these skills outside of academia or via the right mix of online training and practice — so there’s some flexibility around having the actual degree — but direct experience applying the scientific method is a must.

    Another advantage of an advanced degree is the rigor of the peer review process and publishing requirements that the degree programs impart. To get published, candidates have to present their work in a way that allows others to review and reproduce it. You must also provide evidence that the results are valid and the methods are sound. Doing so requires a deep understanding of the difference between probabilistic and deterministic factors as well as the value and curse of the correlation. It’s possible to get an abstract sense of those values, but there’s no substitute for the negative and positive reinforcement from mentors or the rejection or acceptance of journals and reviews.

    2. Expertise in machine learning and statistics, with an emphasis on decision optimization

    Applying the scientific method to business problems lets us make better decisions by predicting what will happen next. Those predictions are the product of artificial intelligence and more specifically machine learning. For a true data scientist, the core technical skillsets of machine learning and statistics are simply non-negotiable.

    In addition, decision optimization (aka operations research) is a fast-growing aspect of data science. Indeed, the goal of data science is to help make better decisions by probabilistically estimating what’s likely to occur in the future. Carefully applying decision optimization lets data scientists prescribe or determine the next best action for the best business outcome.

    3. Expertise in R, Python, or Scala

    Being a data scientist doesn’t require you to be as good at programming as professional developers, but the ability to create and run code that supports the data science process is mandatory — and that includes the ability to use statistical and machine learning packages in one of the popular data science languages.

    Python, R, and Scala are the fastest-growing languages for data science, along with Julia, another upcoming language in the space, though Julia isn’t yet fully mature. Like Python, R, and Scala, the core of Julia is open source. But it’s important to note that the reason to use these languages isn’t that they’re free, but for the innovation and the freedom to take them where you want to go.

    4. Ability to transform and manage large data sets

    The fourth skill is sometimes called big data. Here, the ability to use distributed data processing frameworks like Apache Spark is key. The true data scientist will know how to pull data sets together from multiple sources and multiple data types with the help of his or her data science team. The data itself might be a combination of structured, semi-structured, and unstructured data living on multiple clouds.

    The data management process consists of finding and collecting the data, exploring the data, transforming the data, identifying features (data elements important in the prediction), engineering the features, and making the data accessible to the model for training. A priority for any data scientist will be streamlining this process, which can easily eat up 80 percent of their time.

    5. Proven ability to apply the skills above to real-world business problems

    Fifth on the list is a soft skill set. It’s the ability to communicate with non-data scientists in order to make sure that data science teams have the data resources they need and that they’re applying data science to the right business problems. Mastering this skill also means ensuring that the results of data science projects — for instance, predictions about the probable evolution of the business — are fully understood and actionable by business people. This requires good storytelling skills, and in particular, the ability to map mathematical concepts to common sense.

    6. Ability to evaluate model performance and tune it accordingly

    To some, this sixth skillset is an aspect of the second skillset: expertise in machine learning in general. We wanted to call it out separately because, all too often, it’s what distinguishes a good data scientist from a dangerous one. Data scientists who lack this skill can easily believe that they’ve created and deployed effective models when in fact their models are badly over-fit to the available training data.

    Be a true data scientist

    If you want to be a true data scientist — as opposed to an aspiring data scientist or a data scientist in title only — we encourage you to master each of these six competencies. A data scientist is fundamentally different from a business analyst or data analyst, who often serve as product owners on data science teams, with the important role of providing subject matter expertise to the data scientists themselves.

    That’s not to say business analysts, data analysts, and others can’t transition to become true data scientists — but understand that it takes time, commitment, mentoring, and applying yourself again and again to real and difficult problems.

    Seth Dobrin is vice president and chief data officer at IBM Analytics.

    Jean-François Puget is an IBM distinguished engineer in machine learning and optimization.



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