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E20-007 Data Science Associate

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E20-007 exam Dumps Source : Data Science Associate

Test Code : E20-007
Test denomination : Data Science Associate
Vendor denomination : EMC
exam questions : 198 real Questions

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EMC Data Science Associate

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Ben-Gurion university, Bayer and Netafim to collaborate on research optimizing drip irrigation | real Questions and Pass4sure dumps

graphic: Assisted by using Ben-Gurion school of the Negev scientists at the Jacob Blaustein Institutes for barren region analysis, the neighborhood will travail to calibrate digital prediction fashions for optimized software of crop... view greater 

credit score: Netafim/BGU

BEER-SHEVA, Israel...February 7, 2019 - Bayer, Netafim and BGN applied sciences, the technology switch company of Israel's Ben-Gurion tuition of the Negev (BGU), introduced a 3-year research collaboration. The announcement become made the day past at the Fruit Logistica 2019 exchange honest and exhibition in Berlin.

The task will fuse leading soil analysis, digital prediction rig and state-of-the paintings drip know-how developed with the aid of Netafim to establish most advantageous practices for the usage of drip irrigation as a birth rig for Bayer Velum best in Israel. Velum leading combats nematodes (roundworm) and harmful ailments affecting potatoes, tomatoes, cucumbers, citrus, and brassicas.

"This collaboration agreement with industry leaders such as Bayer and Netafim is a testament to BGU's leading position within the container of agriculture," stated Netta Cohen, chief govt officer of BGN technologies. "determined in the coronary heart of the Negev barren region, the analysis at the tuition's Jacob Blaustein Institutes for barren region analysis on the Sede Boqer campus focuses on water, power, agricultural, and ecological options for vehement barren region and drylands circumstances, which are increasingly becoming extra significant with environmental alterations comparable to international warming."

as a result of clinical and environmental considerations, utilize of chemical crop protection is being confined, leading to loss of inevitable products. Farmers are actually in quest of sustainable agricultural solutions to preserve crop yields.

Bayer and Netafim hold addressed this problem with a brand novel innovative answer named DripByDrip. The thought uses Netafim's drip irrigation techniques to deliver Bayer's chemical and organic crop insurance policy items.

DripByDrip applies the lively materials exactly to the plant, leading to greater efficacy, lessen amounts of crop insurance policy compound and lesser environmental affect.

Assisted by means of BGU scientists from the Blaustein Institutes, the group will travail to calibrate digital prediction models for optimized utility of crop insurance way compounds by passage of drip irrigation. This includes laboratory and container experiences evaluating the conduct of Bayer Velum major in soils and flowers below common agricultural conditions in arid regions.

research may breathe performed at the Blaustein Institutes and Netafim facilities in Israel. It may breathe led by using BGU Prof. Shimon Rachmilevitch, Dr. Marc Rist of the Bayer Crop Science Division, and Dubi Raz, corporate agronomy director at Netafim.

Holger Weckwert, portfolio management section supervisor for insecticides solutions at the Bayer Crop Science Division, referred to, "applying crop insurance policy compounds the utilize of drip irrigation accurately ambitions the principal vegetation, and is an environmentally friendly, efficient and low-priced contribution to sustainable agriculture. They look forward to taking section with leading BGU scientists."

"Drip irrigation gives you water, fertilizers and crop insurance plan, together with biologicals, at once to the roots of the crops," says Netafim's Dubi Raz. "This way enables farmers to solemnize crop protection products in a extra targeted way using Netafim's drip irrigation expertise. Following their a success lengthy-time period partnership with Bayer, we're completely satisfied to now welcome BGU's scientists in helping us in optimizing the software of crop coverage compounds by the utilize of drip irrigation."


About Bayer

Bayer is a world commercial enterprise with core abilities in the lifestyles science fields of fitness permeate and agriculture. Its products and functions are designed to capitalize people and help their first-rate of lifestyles. on the identical time, the community goals to create value through innovation, augment and inordinate earning power. Bayer is dedicated to the ideas of sustainable construction and to its sociable and ethical obligations as a company citizen. In fiscal 2017, the neighborhood employed around 99,800 individuals and had income of EUR 35 billion. Capital costs amounted to EUR 2.four billion, R&D fees to EUR four.5 billion. For greater information, fade to http://www. bayer. com .

About BGN technologies

BGN applied sciences is the expertise switch enterprise of Ben-Gurion college of the Negev in Israel. BGN brings technological improvements from the lab to the market and fosters research collaborations and entrepreneurship amongst researchers and students. so far, BGN has established over 100 startup corporations within the fields of biotech, excessive-tech and cleantech, in addition to initiated leading know-how hubs, incubators and accelerators. over the final decade, BGN applied sciences has concentrated on creating long-term partnerships with multinational establishments similar to Deutsche Telekom, Dell-EMC, IBM, PayPal, Bayer, ICL, and Adama, securing value and augment for Ben-Gurion college as smartly as the Negev location. For more assistance, talk over with the BGN technologies web page.

About Netafim

Netafim is the global leader in precision irrigation for a sustainable future. With 29 subsidiaries and 17 manufacturing flowers worldwide, Netafim offers innovative, tailor-made irrigation and fertigation solutions to thousands and thousands of farmers, enabling smallholders to massive-scale agricultural producers in over 110 nations to grow extra with much less. headquartered in 1965, Netafim pioneered the drip revolution, making a paradigm shift towards precision irrigation. nowadays, that specialize in end-to-conclusion solutions from the water supply to the basis zone, Netafim promises turnkey irrigation and greenhouse tasks supported by using engineering, task management and financing features. Netafim is furthermore leading the passage in digital farming, integrating true-time monitoring, evaluation and automatic handle into one state-of-the-artwork system. In 2017, Mexichem, a leading employer of imaginative options in the constructing and infrastructure, facts communications, irrigation and chemical compounds industries acquired eighty p.c of Netafim. Kibbutz Hatzerim holds the final 20 p.c. learn extra at http://www. netafim. com

About American buddies, Ben-Gurion tuition of the Negev

American acquaintances, Ben-Gurion tuition of the Negev (AABGU) plays a essential office in sustaining David Ben-Gurion's imaginative and prescient: growing an international-classification establishment of training and analysis in the Israeli wilderness, nurturing the Negev neighborhood and sharing the school's talents in the neighborhood and world wide. As Ben-Gurion college of the Negev (BGU) appears forward to turning 50 in 2020, AABGU imagines a future that goes past the walls of academia. it is a future the region BGU invents a brand novel world and inspires a imaginative and prescient for a better Israel and its subsequent generation of leaders. along with supporters, AABGU will assist the school foster excellence in instructing, research and outreach to the communities of the Negev for the subsequent 50 years and past. contend with imaginative and to gain learning of greater.

AABGU, headquartered in ny, has 9 regional workplaces right through the us. For greater tips contend with https:/ / aabgu. org/ .

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Doubling down on data science, Databricks lands massive $250M round | real questions and Pass4sure dumps

Big-data company Databricks Inc. is topping up its war chest with a hefty $250 million late-stage round of funding.

The succession E round was led by Andreessen Horowitz. Coatue Management, Microsoft Corp. and novel Enterprise Associates furthermore participated in the round, which brings Databricks’ total capital raised to date to $498.5 million.

Databricks sells a Unified Analytics Platform based on the open-source Apache Spark big-data framework that’s used by enterprises to dissect data, build data pipelines across siloed storage systems and prepare labeled datasets for model building. The notion is that organizations can then utilize the Unified Analytics Platform to train machine learning and other simulated intelligence models using their existing data.

The platform furthermore provides collaborative features intended to foster closer communication between data scientists and engineers who need to travail together in order to build better AI models.

Led by the founders of Apache Spark, Databricks has helped to drive massive adoption of that open-source platform in the final few years. In its press release, the privately owned company said it managed to top $100 million in annual recurring revenue during 2018 while tripling year-over-year growth in its subscription revenue.

Ali Ghodsi (pictured), Databricks’ cofounder and chief executive officer, said those numbers fabricate Databricks one of the fastest-growing enterprise software companies around.

“What’s driving this incredible growth is the market’s massive appetite for Unified Analytics,” Ghodsi said in a statement. “Organizations need to achieve success with their AI initiatives and this requires a Unified Analytics Platform that bridges the divide between ample data and machine learning.”

Databricks’ growth has been helped by some useful updates over the past year that aimed to expand the utility of its Unified Analytics Platform. Back in March for example, the company launched a cloud-based offering called Microsoft Azure Databricks that can easily breathe deployed on Microsoft’s Azure cloud and integrated with services such as the Azure SQL Data Warehouse.

Other novel features added over the final year comprise Databricks Runtime For ML, which provides preconfigured modeling and distributed training environments. Tightly integrated with such current AI frameworks and libraries as TensorFlow, Scikit-Learn, Keras and XGBoost, they serve to accelerate environment provisioning and configuration management.

The company furthermore introduced Databricks MLflow, which provides an open source toolkit for simplifying ML modeling, training and operationalization across multiclouds. The offering integrates closely with Apache Spark, SciKit-Learn, TensorFlow and other open-source frameworks.

Such progress is positive, but in an article final summer, analyst James Kobielus of Wikibon, owned by the identical company as SiliconANGLE, pointed out that Databricks faces significant challenges in trying to sustain its growth in a market where Apache Spark’s adoption is likely to plateau in a similar vogue to how Hadoop’s has. Kobielus notes that Apache Spark is starting to display its age, and feels increasingly devotion a legacy technology in many AI shops. Moreover, he said, its recent novel offerings don’t signal a transparent differentiation for Databricks in a market that has become saturated with AI DevOps, modeling and training tools offered by the likes of Amazon Web Services Inc. and Google LLC, among others.

Still, Spark remains the industry’s most paramount runtime engine and library for in-memory, parallel machine learning, as well as a useful implement in many AI platforms for efficient data preparation and training for many modeling initiatives, Kobielus said. As such, the analyst said, he believes Databricks will breathe able to utilize today’s funding to innovate further and overcome the the challenges he highlighted.

“Databricks is able to attract so much funding because the larger data-science workbench market, aka ‘AI DevOps,’ is booming,” Kobielus said. He renowned that Databricks now offers a “full-featured data science workbench” that’s not only fairly develope in comparison to others, but furthermore provides a smooth upgrade path for data scientists working on sophisticated AI projects.

Those sentiments were shared by analyst Doug Henschen of Constellation Research Inc., who told SiliconANGLE that he believes Spark will note yet more adoption in the enterprise both for its data-processing and data-transformation capabilities, and for the advanced analytical travail it performs.

“Databricks faces competition both from third-party Spark services and emerging analytics platforms and studios such as Amazon SageMaker and Google AI Hub,” Henschen said. “The higher value is in the model-development work, and that’s where lots of alternatives to Databricks are emerging.”

Kobielus furthermore renowned the might of Databrick’s rivals’ offerings but said the company remains a pacesetter in the data science market.

“It’s an exceptionally solid brand sprint by dynamic visionaries who are doing excellent travail and hold loyal customers,” Kobielus said. “My sense is that they’re more likely to prevail going forward in this changed market than the combined Cloudera/Hortonworks is, based on the strengths of their respective machine learning, abysmal learning and simulated intelligence evolution platforms.”

Databricks certainly has a way to execute just that. In a statement to SiliconANGLE, the company said it’s aiming to utilize its novel funds to double down on its growth in Asia, Europe and the Middle East, and the Americas. This includes expanding its sales teams in those regions, more investment in engineering, and opening novel offices in several countries across the globe.

Photo: SiliconANGLE Since you’re here …

… We’d devotion to uncover you about their mission and how you can cheer us fulfill it. SiliconANGLE Media Inc.’s industry model is based on the intrinsic value of the content, not advertising. Unlike many online publications, they don’t hold a paywall or sprint banner advertising, because they want to retain their journalism open, without influence or the need to chase traffic.The journalism, reporting and commentary on SiliconANGLE — along with live, unscripted video from their Silicon Valley studio and globe-trotting video teams at theCUBE — win a lot of difficult work, time and money. Keeping the quality lofty requires the support of sponsors who are aligned with their vision of ad-free journalism content.

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Maximizing ample Data: Booz Allen Shares 5 Tips for Federal Agencies | real questions and Pass4sure dumps

Feb 08, 2019 (3BL Media via COMTEX) -- SOURCE:Booz Allen Hamilton

From government spending figures to public health statistics to geospatial imagery, the federal government is gathering, storing, and sharing more data than ever. Agencies hold started consolidating this information into ample data platforms for analysis and decision-making. But often such a "data lake" threatens to become a "data swamp" that slows agencies' growth and progress.

What's holding agencies back? First of all, it's difficult to combine, travail with, and dissect data that comes in various sources, formats, and types--especially given the strict security and privacy regulations federal agencies need to follow. People trained in data science are often in short supply and expensive to hire. Moreover, 60 percent of ample data initiatives overall fail--often because organizations failed to first define the problem they were trying to solve.

In a recent issue of InsideBIGDATA magazine, Booz Allen Senior Associate Chris Brown and Principal David Cunningham provided five tips for pitiable from "data lakes" to actionable insight.

  • First identify the problem. What does your organization point to execute with its data lake? To what degree will it need to scale? The answers to these questions will guide data collection, security, cloud infrastructure, and more.
  • Define and secure the data. To procure diverse forms of data ready for analysis, you'll need metatags for aggregating and processing and attributing authorization schemes for security.
  • Make information easier to find. Agencies can catalogue and organize information into various zones: raw and trusted data (depending on whether it's been quality-checked and tagged), data to breathe used for analysis, and data held in a "sandbox" for testing, prototyping, and exploration.
  • "Democratize" data analytics. Data zones, self-service tools, and automation can cheer spread the analytics workload out to team members beyond highly skilled and specialized data scientists.
  • Use Agile to accelerate progress. This methodology and culture can cheer organizations manage uncertainty, help teamwork, and pivot quickly in ample data projects.
  • Read the full InsideBIGDATA article and learn more about how Booz Allen is using data science and analytics to cheer clients decipher problems.

    Tweet me:How can federal agencies maximize the #BigData they collect? @BoozAllen shares tips with @insideBigData

    KEYWORDS: Booz Allen Hamilton, Innovation and Technology, NYSE: BAH, ample Data, data science, analytics, InsideBIGDATA, data structures


    Data mining brings novel clarity to plant breeding, according to novel Iowa state University study | real questions and Pass4sure dumps

    a corn field

    Data mining methods that draw on computer science and statistics can call traits in corn with better accuracy than previous prediction methods. Photo by Qi Mu. Larger image.

    AMES, Iowa – The immense number of practicable hybrids that can breathe created from inbred corn plants can leave plant breeders wondering where to start when attempting to produce novel crop varieties with desirable traits. But novel research from an Iowa state University agronomist shows how advanced data mining techniques can enhance the efficiency of the process.

    The study, published recently in the peer-reviewed academic journal Molecular Plant, outlines several data management approaches that can cheer plant breeders call the traits of potential hybrids faster and cheaper than growing and testing the plants. Jianming Yu, professor of agronomy and Pioneer Distinguished Chair in Maize Breeding, said the study shows how genomic selection, or making accurate predictions of how a plant will execute based on its genotype, can inform plant breeding decisions.

    Recent advances in biotechnology hold allowed scientists to compile massive amounts of genomic data, mapping much of the genetic material present in various organisms, Yu said. But matching the genotype of an organism with its real-world, observable traits, or phenotype, presents a different set of challenges.

    The ISU researchers worked with a multi-institution team, including scientists from the University of Missouri, the University of North Carolina and the University of Delaware. The team started the project by generating phenotypic metrics for 276 corn hybrids. The team explored a scope of data mining approaches to a dataset containing genomic information as well as phenotypic metrics of these hybrids. Using a suite of data mining methods, they predicted flowering time, ear height and bow for each hybrid, then compared their predictions to the real result and create their way was more accurate than previous prediction models.

    “With these novel methods, training samples for structure prediction models were more informative so that prediction accuracy was higher,” said Tingting Guo, the first author of the paper and a postdoctoral research associate in agronomy.

    The team applied the methods to two other crops, wheat and rice, for which experimental records were available for a great number of hybrids from earlier studies. The approach furthermore worked well for these other crops.

    The research demonstrated that efficient genomic prediction models can breathe established with a training set of 2-13 percent of the size of the gross set, enabling an efficient exploration of many genetic combinations.

    The data mining methods tested in the study drew on several disciplines, including computer science and statistics. The techniques rawboned on clustering and graphic networking, but Yu described the approach as a passage of recognizing patterns in vast amounts of data.

    “Data mining is another passage of proverb pattern finding,” Yu said. “We looked at a great dataset and create patterns linking genotype to phenotype. They then answer the question, ‘Which passage of selecting the samples would allow breeders to exploit the patterns with minimal initial efforts?’”

    Yu said this line of research could bridge the gap between predicting traits and designing better crops. Optimizing the process of how plant breeders select genetic lines to test will allow for more strategic utilize of time and effort, leading to more rapid progress in developing crop varieties with desirable traits.

    Funding for the research came from the National Science Foundation and the ISU Plant Sciences Institute. Other ISU scientists contributing to the research comprise Xiaoqing Yu, a former ISU postdoctoral research associate; Xianran Li, an adjunct associate professor of agronomy; and Haozhe Zhang, a graduate coadjutant in statistics; and Chengsong Zhu, a former ISU postdoctoral research associate. 

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