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MeasureUp’s 70-475: Designing and enforcing huge information Analytics options ensue examine is designed to aid candidates prepare for and sprint the Microsoft 70-475 examination.
The Microsoft 70-475 examination is supposed for statistics experts who've journey designing great data analytics options on Microsoft Azure. Candidates should silent subsist in a position to design great records batch processing and interactive solutions in addition to great records actual-time processing solutions. Candidates may silent even subsist in a position to design computer-researching options and create and exploit end-to-end cloud analytics solutions.
Certification: This exam counts as credit toward perquisite here certifications:specialist
Microsoft is making its first great appearance at mobile World Congress (MWC) in quite ages this yr, and you'll subsist in a position to circulation the business's keynote as it happens. Set to kick off on February 24 at 9 a.m. PT (12 p.m. ET / 6 p.m. CET), the keynote will office "new innovations for the future of computing" with comments from Microsoft CEO Satya Nadella and HoloLens chief Alex Kipman.
which you can observe along with the keynote are living, tune in with the video embed above or at Microsoft's adventure web page.
As for what we're anticipating Microsoft to announce, plenary signs point to a 2d iteration of the company's HoloLens augmented verity headset. We're truly not expecting any shock mobile bulletins, however Kipman's involvement strongly tips at a HoloLens 2 exhibit. it truly is far from definite, of route, but plenary they ought to fade off of is a slick, cryptic teaser video posted to YouTube ultimate week by means of Kipman himself.
The daily HoloLens has more often than not loved success as an enterprise tool, bolstering manufacturing tactics, along with design and scientific purposes. The headset has even led to a lucrative $480 million compress with the U.S. militia, which could lead on the military to eventually purchase upwards of 100,000 HoloLens devices.
when you account that the daily HoloLens launched in 2016, a followup is hotly predicted. it's already been tested that the HoloLens 2 will recreation a custom Holographic Processing Unit (HPU) with an AI coprocessor, enabling it to natively attach in obligate profound neural networks to research objects and statistics without sending the leisure to the cloud. although, they call several other advancements, together with, potentially, a change to an ARM processor and aid for LTE.
sooner or later, we'll must wait and spot what Microsoft has in maintain for its MWC 2019 keynote.
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A these days posted patent software means that AMD can subsist infusing a technology called Variable price Shading (VSR) into its upcoming Navi GPU architecture, which is expected to arrive later this yr. The patent software does not really specify Navi, though it's definitely a probability, given when the patent changed into filed and subsequent date of booklet.
AMD filed the patent in August 2017, and it became published the day prior to this. AMD had already been establishing its Navi GPU architecture in 2017, and had additionally begun taping out its 7-nanometer FinFET manufacturing routine around the identical time (a tape out is when a company relish AMD finalizes its semiconductor design before sending it off for manufacturing).
VSR is a rendering approach designed to dwindle the cross on the GPU and other elements, for introduced efficiency without successful to the perceived visual constancy of a scene. It accomplishes this by means of breaking up a frame into distinctive sections and rendering handiest parts of a frame in plenary aspect, those being the enviornment(s) you are most concentrated on, and using coarser pixels across the fringe of a person's imaginative and prescient.
VR headsets stand to the benefit the most from this tech, the region your eyes are concentrated on the core. That talked about, VSR might conceivably subsist used in alternative routes for gaming on the desktop. This may especially subsist really useful at greater resolutions which are greater taxing on a GPU, equivalent to 4K, and eventually 8K if they want to ogle that far down the street.
AMD is not by myself in seeing the handicap of VSR. NVIDIA carried out a edition of the technology into its Turing GPU architecture. here's NVIDIA's explanation of VSR:
Variable cost Shading is a brand new, smooth to attach into result rendering routine enabled with the aid of Turing GPUs. It raises rendering efficiency and satisfactory by pass of applying varying quantity of processing power to distinctive areas of the picture. VRS works with the aid of various the variety of pixels that may too subsist processed through a solitary pixel shader operation. solitary pixel shading operations can now subsist applied to a screen of pixels, permitting applications to effectively disagree the shading price in diverse areas of the monitor. Variable rate Shading can too subsist used to render extra efficaciously in VR through rendering to a surface that closely approximates the lens corrected picture it is output to the headset monitor. This avoids rendering many pixels that could otherwise subsist discarded earlier than the photograph is output to the VR headset.
there is actually widespread activity in VSR. concerning that, Microsoft too owns a VSR patent and is working to implement it into the DirectX API. no matter if it extends past VR headsets and into the realm of desktop monitors, notwithstanding, they are able to need to wait and spot.
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eFinancialCareers jobs: Reporting & Analytics - great Data Developer# 125072 in Credit Suisse -, Raleigh, NC, USA
Location: Raleigh, NC, USA
Job Type: Permanent, plenary time
Company: Credit Suisse -
Updated on: 02 Mar 19
We OfferCredit Suisse is seeking a Data Engineer in the Enterprise Services -
SMO - Enterprise Services are amenable for providing innovative ways through maintaining scholarship of industry gauge methodology, technology trends and product availability. Developing business and technical requirements through consultation with key client business partners and ensuring consistency to corporate standards for risk and security compliance.
The Analytics & Reporting team works as allotment of a globally distributed team that will subsist the primary provider of IT Operational Reports to the firm. At any given time you will colleague with team members across plenary regions and time zones while reporting to a line manager in unique York. You will collaborate with key clients to develop dashboards that will drive business decisions across the firm.
Design/Maintain/Support Bigdata environment, including ETL.
Work directly with key business partners to understand data requirements and deliver on timely manner.
Analyze great data sets.
Design very complex SQL statements, including Impala.
Scripting automation solutions for Data and Reports.
Adhere with Agile methodology and ensue the process to maintain/ back and to document the details.
Ability to easily communicate complex problem solving ideas to senior management and peers
Find opportunities on automation for any manual process.
In this function, you will subsist designing and implementing data solutions via Bigdata and python. You will too build automation scripts to back minimize manual involvement when edifice reports or dashboards. You will too back migrate existing complex SQL tables into Bigdata, and back reporting team to integrate the migrated tables in Tableau.
You should too subsist well versed with data design and possess ETL process experience.
Credit Suisse maintains a Working Flexibility Policy, topic to the terms as set forth in the Credit Suisse United States Employment Handbook.
You possess a degree or higher education in Information Technology or equivalent travail experience
You possess at least 3 to 4 years of sustain edifice Bigdata environment
You possess demonstrable sustain with Data Engineering
You possess profound scholarship in Bigdata impala/Hadoop
You possess skills in Python for edifice functions using Pyspark and other packages related to Python
Database architect for bigdata environment on how the ETL process works will subsist an advantage
Good interpersonal skills as you will subsist involved in the interaction with clients to understand or present the requirements
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For more information visit Technology Careers .
eFinancialCareers is a DHI service. DHI is a publicly-traded company listed on the unique York Stock Exchange. (Ticker: DHX)
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By Lisa Harrington, Dr. Zac Rogers, Dale Rogers, Richard Sharpe and Susan Lacefield | From the Quarter 1 2019 issue
Big data analytics implementations are up, but satisfaction with those implementations is down, according to a recent survey. Why are companies frustrated with their attempts to harness the power of great sets of supply chain data?
We are at the beginning of a supply chain technological revolution. Everything—from pallets and trucks to a bag of Romaine lettuce—has the potential to collect, store, and transmit information. If only companies could hold this data and feed it into analytics and simulated intelligence systems, they could obtain better, faster decisions than ever before, driving down fritter and increasing value. That, at least, is the vision of what great data analytics could achieve.
But now, reality is beginning to set in. As more companies try to wrap their arms around their "big data" and implement more complex analytic tools, they are beginning to realize that achieving this vision is hard work. It takes significant investment in information technology (IT) systems as well as change and process management. Companies are too finding that the key data that they need is often missing or inaccurate. The plight of great data analytics is coming, but perhaps not as easily as initially thought. That was the main message conveyed by the results of the "Second Annual great Data Analytics Study" conducted by the analytics company Competitive Insights LLC; the consultancy lharrington group; CSCMP's Supply Chain Quarterly; and two prominent supply chain management schools, Arizona state University and Colorado state University.
This annual study is designed to provide companies with a benchmark that they can expend to understand the current state of supply chain data analytics and learn what analytical strategies organizations are adopting to harness the power of great data. The intent is to point to the levels of progress that companies are making in addition to the obstacles impeding that progress.
A survey was conducted in both 2017 and 2018 with readers of Supply Chain Quarterly, subscribers to a newsletter produced by Competitive Insights, and a contact list generated by Arizona state and Colorado state University researchers. A total of 125 usable responses were compiled for the 2018 survey, comparable to 2017's total of 133 usable responses.
Here are the findings and some suggested best practices that could back companies overcome their initial frustration and gain positive momentum with their great data analytics implementations.
More implementations, less satisfaction
A comparison of 2018's and 2017's results recount an gripping story. In both 2017 and 2018, they asked people: "How would you characterize your supply chain organization's maturity in regard to its expend of great data analytics?" (See motif 1.) In general, the survey results point to that more companies possess begun implementing great data analytics initiatives. There was a 14-percent enlarge in the number of great data implementations between 2017 and 2018, and far fewer people reported that they had not adopted supply chain analytics at plenary (only 10 percent in 2018 as opposed to 23 percent in 2017). And yet, motif 1 too shows that there were fewer people reporting that their implementations were "transformational" or "advanced." Instead, the majority of respondents were in the "early" or "developing" stages of adoption, indicating that most companies are either silent conducting proof-of-concept testing or possess only rolled out initial implementations.
Why was there a drop off in the number of "transformational" and "advanced" responses in 2018 from 2017? They execute not believe that firms are necessarily less successful with their great data analytics implementations this year as opposed to 2017. Instead, responding firms' definition of "transformational" may possess evolved since ultimate year. As more companies implement great data analytics in earnest, they are developing a better understanding of what it entails and how much farther they possess to go. In other words, companies now possess a more realistic assessment of where they actually descend on the maturity curve and the extent of the obstacles they must overcome. The widespread excitement of 2017 has been replaced by the realization that, relish almost plenary unique technologies, it takes a lot of travail to obtain great data analytics useful. Companies, as a result, are more realistic about their progress along the maturity curve.
We too perceive this perspective in respondent satisfaction with the data they execute have. In general, companies are less satisfied with the property and availability of their data in 2018 than they were in 2017. On average, respondents in 2018 were 4 percent less satisfied with their data availability, 8 percent less satisfied with data usability, and 7 percent less satisfied with their data's integrity than respondents in 2017. Satisfaction with data reliability too dropped 5 percent between 2018 and 2017. It seems unlikely that data property actually dropped from 2017 to 2018. Rather, they believe that as companies bag deeper into their great data analytics implementations, they are becoming more conscious of their existing data issues and possess a better understanding of the magnitude of the pains and commitment required to address them.
It stands to judgement that if respondents are less satisfied with the property of their data, they are going to subsist less satisfied with their data analytics results as well. Indeed, the 2018 survey showed a slight drop in respondents' assessment of the realized benefits of their great data analytics efforts in 2018 relative to 2017. In both years, survey respondents were asked to expend a seven-point scale to quantify the impact they possess already realized from great data analytics in a variety of areas, such as profitability, inventory management, and visibility to total cost-to-serve. A score of 1 equals no impact, and 7 equals a transformative impact. motif 2 shows a slight drop in perceived impact across plenary of the potential benefits in 2018 versus 2017.
The ilk of analytics matters
When assessing the benefits that can subsist expected from a great data analytics implementation, it is crucial to understand that there are many types of analytics. For the survey, they defined the different types of data analytics as follows:
Descriptive—what is happening/has happened
Diagnostic—why it's happening
Predictive—what will happen
Prescriptive—what should subsist done
Cognitive—uses machine learning to recount what should or could subsist done
Survey respondents were again asked to select from a seven-point scale (where a score of 1 equals no use, and a score of 7 equals heavy use) the extent that their company currently uses each of those types of analytics to back supply chain determination making. On average, respondents gave descriptive analytics a score of 4.61 (indicating between "some use" and "regular use"), diagnostic analytics received a score of 4.02, predictive analytics a score of 3.16 (indicating between "some use" and "occasional use"), 3.56 for prescriptive analytics, and 2.27 (or "infrequent use") for cognitive analytics. (See motif 3.) These scores are similar or slightly less than what was seen ultimate year. The survey results attest that more sophisticated types of analytics are silent used less than more rudimentary methods in many real-life supply chains.
Further investigation using regression analyses on the responses identified a significant correlation between the ilk of analytics that firms expend and the benefits that they report achieving.1 Generally, more sophisticated analytics are associated with a wider sweep of benefits. motif 4 shows that the expend of descriptive analytics is only correlated with improvements in customer service. This finding makes sense, as knowing what is happening in their supply chain can back companies better inform their customers of the problems they are facing before these issues become visible to them. While expend of diagnostic analytics was slightly associated with require planning and highly correlated with collaboration, no correlation could subsist organize with any of the other realized benefits. (By "slight," they subsist substantive that they are greater than 90 percent inevitable there is a correlation, instead of greater than 95 percent confident there is a correlation.) Predictive analytics, however, could be linked to many benefits, such as improvements in require planning, risk management, and collaboration.
Somewhat counterintuitively, prescriptive analytics was not positively related with any of these realized benefits. In fact, it was slightly negatively correlated to improvements in require planning. It's workable that by the time firms bag to the level of prescriptive analytics, they not only possess made some improvements to their require planning but too are more conscious of their issues. Without the capabilities of more advanced analytics, they may possess wretchedness addressing these issues which they now possess more insight into. In many ways, this negative correlation is a microcosm of the trends they perceive throughout the 2018 report. Firms are beginning to implement more sophisticated protocols, and, in the process, are beginning to realize the obstacles they possess yet to overcome.
Finally, they organize that the most helpful ilk of analytics was cognitive, with a tenacious correlation to risk management and productivity and a slight correlation to customer service, visibility, and collaboration. In other words, the more sophisticated and forward-looking the ilk of analytics that are being used, the more benefits companies are realizing.
Similar results can subsist observed when considering the types of software tools that companies are employing to perform their analytics. Survey respondents were again asked to expend a seven-point scale (where a score of 1 equals "no use," a score of 4 equals "some use," and a score of 7 signifies "heavy use") to measure the extent that their company currently uses a variety of analytics tools. Those tools included: Microsoft outdo or similar spreadsheet programs; operational point solutions (OPS) such as warehouse management systems and transportation management systems; advanced analytical tools associated with enterprise resource planning (ERP) systems; and business intelligence (BI) tools. As was the case in ultimate year's survey results, outdo spreadsheets continue to carry the day and are by far the most widely used analytics implement with an mediocre score of 5.80, indicating "frequent use." OPS received an mediocre score of 4.64, and ERP and business intelligence tools received scores of 3.97 and 3.88 respectively.
The survey team too ran multiple analyses to perceive if there were any links between realized tangible great data analytical benefits and the expend of these platforms. (See motif 5.) While Microsoft outdo was the most widely used of these platforms, it too was the least useful. In fact, no correlation was established between the expend of either outdo or an OPS as the primary great data analysis platform and any tangible improvement in customer service, require planning, risk management, supply chain visibility, collaboration, or overall productivity. This demonstrates that for many companies, the current methods for analyzing great data are ineffective. They may subsist useful for maintaining the status quo, but they are unlikely to lead to many benefits. Without a change in methods, it is unlikely firms will perceive a change in results.
Conversely, although ERP and BI systems were the least used platforms, the regression analysis suggests that they were the most beneficial. ERP expend was significantly correlated with realized benefits in customer service, require planning, risk management, inventory management, visibility, and increased profitability. business intelligence users reported the greatest levels of performance improvement in customer service, supply chain visibility, end-to-end supply chain collaboration, and overall productivity. These results look to demonstrate that to obtain great data analysis work, companies need to subsist using the perquisite tools.
Although most of the survey respondents possess only implemented the less sophisticated types of analytics, they are silent hopeful about the results that they are going to achieve. Companies reported that they hope moderately significant improvements in customer service, supply chain visibility, productivity, and profitability in the next 12 months. However, only 40 percent of survey respondents are planning to obtain a moderate to very great investment in cognitive analytics in the next 12 months, and only 52 percent project to obtain a significant investment in predictive analytics. In contrast, almost two-thirds (63 percent) of survey respondents project to invest in descriptive analytics.
The 2018 survey results point to a disconnect between reality and anticipated results. Companies look to subsist focusing their investments in baseline solutions but are hoping for results that are correlated to more sophisticated solutions.
In spite of these discouraging signs, there is hope. Survey respondents generally reported facing fewer roadblocks to implementing great data analytics than they did ultimate year. On a seven-point scale, plenary of the potential impediments that they asked about dropped from an mediocre score between 5 (moderately significant) and 4 (neither significant nor insignificant) to scores between 4 and 3 (moderately insignificant). (See motif 6.)
After analyzing the 2017 data versus the 2018 data, they determined that managerial back was the key difference between this year and ultimate year. Companies that had tenacious managerial back did not perceive getting their firms to invest in additional software or hardware or understanding the value proposition as significant barriers to great data analytics implementation. They too did not believe that concerns about security risks posed an impediment to adoption. However, they did silent believe that talent acquisition, integrating siloed legacy systems, and gaining competency in unique tools were significant barriers to further implementations.
The fact that more companies are reporting having the back of top management is an encouraging sign, as this is essential for continuing to shove successful great data analytics initiatives when they mug difficulties or missteps. It too ensures that the implementations will receive the funding they need to gain the required tools, staff, and training.
The road ahead
It would subsist smooth to ogle at the results from this year's survey and feel discouraged. While the number of companies implementing great data analytics programs has increased, their satisfaction with the data they are working with and with the overall results of these initiatives has dropped. Companies look frustrated with their want of progress and revert on investment.
However, this feeling of frustration and even disillusionment is not surprising. It is actually fairly typical for the implementation of any unique technology, as described by the analyst group Gartner in its "Hype Cycle." Many technology experts possess observed that when a unique technology is introduced there is often a term of increasing hype and edifice expectations. When the technology does not initially live up to those overinflated expectations, there is often a term that Gartner calls "the trough of disillusionment." During this period, companies' expectations for the technology drop rapidly as they fade through the smart of actual implementation. Then, as companies ascertain what the technology can actually do, expectations ascend again. During that time, companies regain some of the expectations that they lost in the trough of disillusionment, but they never again compass the height seen during the start of the hype cycle. The smart that many firms look to subsist experiencing with their great data analytics implementations is a normal, and often essential, allotment of the maturity process.
It's workable that great data analytics has entered that "trough of disillusionment," as companies realize how much travail they need to execute to immaculate up their data and expand their expend of additional types of analytics. To bag trustworthy and trusted data, companies need to possess not just a central data repository, but too an executive-sponsored, cross-organizational approach to data collection and analysis. This process needs to factor in accountability, repeatability, and subject-matter validation. This takes time and work. To back guide companies out of this "trough of disillusionment," perceive the sidebar on "Suggested best practices."
Companies are too in the process of learning that the analytical tools they are currently using are not sophisticated enough to provide them with the timely, accurate, and specific insights needed to obtain smart decisions. Companies need solutions that can quickly respond the following questions: What happened? Why did it happen? If they obtain this change, what could happen? These tools too need to subsist able to track the pecuniary and operational impacts of the changes that were made. These types of solutions are out there, but companies will need to obtain investments in unique software and address the change management issues of fully utilizing the insights gained. They will too need to invest in their employees to obtain confident they possess the skill sets required to travail with these systems.
While the initial shine and optimism might possess worn off great data analytics, companies possess a more realistic assessment of the road ahead. They more clearly understand the gaps they mug in data property and accessibility and the limitations of their existing tools. But great data analytics, combined with simulated intelligence methods, silent holds considerable plight for the future. With the perquisite level of executive back and investment, companies can compass the point where they possess the cogent data that they need for analytics tools to obtain better decisions more efficiently. But until then, companies must prepare themselves to travail their pass through a term of uncomfortable growing pains.
1. Regression analysis is a set of statistical processes for estimating the relationship among variables. For example, regression analysis could subsist used to establish whether there is a relationship between respondents who expend descriptive analytics and those who reported improvements in customer service.
Suggested best practices
The talent to gain the most value from great data analytics is a journey. You can accelerate your company's progress by following proven best practices that address the people involved, the processes and data employed, and the technology utilized.
Involve cross-functional teams, with key stakeholders from sales, finance, and marketing, early in any implementation effort. The solutions or root causes that great data analytics may reveal will impact multiple areas and not always subsist confined to the supply chain. Likewise, subsist confident to participate any successes with the other functions involved.
Link your great data analytics efforts to your company's key initiatives. Using the tools to improve something that is essential to your business will back you build momentum and gain back across the organization.
Make confident that you possess senior-level back and ideally a project champion for your great data analytics efforts. Garnering this back will subsist easier if you can tie it back to pecuniary performance.
Avoid "one-off," solitary design efforts. Recognize that great data analytics is not meant to subsist a solitary project. Instead, you are edifice an ongoing organizational capability. Integrating siloed projects is costly and time-consuming. The cost of coordinating implementation efforts early on will pay off down the road.
Be intentional about the focus of a specific analytics effort. Clearly communicate what question the great data analytics project will hunt to respond and who the key stakeholders are. Otherwise you risk scope creep, with the project broadening to the point where it becomes unmanageable and difficult to measure.
Adopt a "crawl, walk, run" mentality. Start with a miniature pilot project first. Getting small, achievable wins early will back build momentum for bigger projects down the road.
Measure direct pecuniary impact. A key metric for judging the success of a great data analytics project is how much it contributed to the bottom line.
Gain cross-functional organizational consensus on what data sources should subsist used.
Make confident that you expend the best sources of data and that the data itself is specific and accurate. This may involve the establishment of gauge operating procedures for data collection a few months prior to the start of an implementation.
Have sound data governance and repeatable data validation to ensure that the entire organization trusts the data.
Design solutions to subsist used cross-functionally. Ultimately, great data analytics should subsist applied across the organization, beyond the supply chain. obtain confident the technology you expend has this discontinuance goal in mind.
Apply an agile development approach. As you implement great data analytics in more areas and to address more problems, your approach will change. obtain confident that your technology can easily adapt to any changes.
Make confident that the technology is scalable.
Lisa Harrington is President of lharrington group LLC. Dr. Zac Rogers is an assistant professor in the managment department at Colorado state University's College of Business. Dale Rogers is a professor of logistics and supply chain management at Arizona state University's W. P. Carey School of Business. Richard Sharpe is CEO of Competitive Insights LLC, a provider of cost-to-serve and profit-analytical solutions. Susan Lacefield is Executive Editor of CSCMP’s Supply Chain Quarterly.
lisa at lharringtongroup.com
zac.rogers at colostate.edu
Dale.Rogers at asu.edu
rsharpe at ci-advantage.com
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MeasureUp’s 70-475: Designing and Implementing great Data Analytics Solutions exercise test is designed to back candidates prepare for and pass the Microsoft 70-475 exam.
The Microsoft 70-475 exam is intended for data professionals who possess sustain designing great data analytics solutions on Microsoft Azure. Candidates should subsist able to design great data batch processing and interactive solutions as well as great data real-time processing solutions. Candidates should too subsist able to design machine-learning solutions and create and manage end-to-end cloud analytics solutions.
Certification: This exam counts as credit toward the following certifications:Specialist
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