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The examine administration study kit includes a complete resolve guide, practice assessments, fifty six dual-sided flashcards, situation of affairs question papers, examination assistance, gratuity material, ninety days plenary working towards uphold in the private enviornment of the enterprise’s IT certification discussion board, and greater. additionally, the package is obtainable in numerous birth strategies – immediate down load, set of CDs, or everything supplied in a bound, plenary coloration booklet (with the gratuity material on a CD).
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Maveric methods, considered one of India's right three unbiased application testing businesses with a sturdy focal point in the banking, fiscal capabilities and coverage (BFSI) vertical, these days announced that it has been permitted through the united kingdom – based mostly, tips methods Examinations Board (ISEB) of The British computing device Society (BCS). identified by means of company properties in Europe as a pre-requisite for utility trying out professionals, the coveted ISEB accreditation affirms that Maveric has matured content material, procedures and start mechanisms for trying out competency construction.
Maveric at this factor of time has a mountainous presence within the UK and is within the manner of providing pilots within the onsite-offshore model. The edge companions consist of prominent system integrators within the enviornment of assurance, telecom and healthcare.
said Mr. Ranga Reddy, CEO, Maveric systems, "UK is an extremely vital market in Maveric's multiply approach with their operations doubling each quarter. As competitors turns into greater fierce in this geography, this acclaimed accreditation gives us a sunder competencies, as we're the best Indian traffic operating in UK with this accreditation".
Mr. Ranga Reddy introduced, "Maveric has utterly met its fiscal projections for 2005-06 and established a sound basis in the middle East and US. Projections for the subsequent 12 months are to the tune of 7 Million dollars with a powerful emphasis on offshore beginning. additional in 2006-07, they might subsist investing significantly in productisation of Maveric's proprietary test frameworks, in the banking vertical".
About Maveric techniques
Maveric methods Ltd. is certainly one of India's exact three independent utility-testing businesses with a robust and dedicated focus in the banking, monetary services and coverage verticals considering the year 2000. over the past five years, Maveric has been pioneering the outsourcing model in application checking out via virtue of its insightful capabilities within the banking and monetary functions vertical, extraordinary core management group and multi-locational checking out amenities. As a leading pure-play, unbiased application trying out company, Maveric tremendously enhances the performance, usability and efficiency of IT solutions, empowering its consumers with a particular impregnate expertise. Maveric's miraculous client profile comprises a wide selection of leading utility product organizations, apparatus integrators and monetary institutions. With worker energy of 300, Maveric has a mountainous presence in Bangalore, Chennai, Dubai, Hyderabad, London and Mumbai.
About assistance methods Examinations Board (ISEB)
ISEB skills add value to skilled careers through offering each the capability and the platform for cognizance and enhanced profession building. via ISEB, BCS supply industry-diagnosed qualifications that measure competence, capacity and performance in many areas of IS, with the end of elevating industry requirements, advertising profession construction and featuring competitive facet for employers. until date, ISEB issued over a hundred and fifty five,000 IT skills across outright disciplines of IT. The ISEB portfolio now offers a huge variety of IT qualifications in numerous disciplines
The basis certificate in software testing provided with the aid of ISEB now has dual accreditation with the ISTQB (international software checking out skills Board) and successful candidates will obtain certification with each ISEB and ISTQB awareness at the foundation degree.
suggestions programs Examination Board (ISEB), a universally diagnosed qualification provided by course of the British computing device Society (BCS), hitherto might simplest subsist received by means of definitely giving the examination in the UK, can now receive sitting right right here in India. This has been made viable due to the alliance solid through Pune-based Quexst acquaintances with UK-primarily based Unicom Seminars Ltd.
talking to CXOtoday, Vandana Dandekar, joint chief executive officer of Quexst, mentioned, ISEB is a lone certificates route touching outright fundamental areas of assistance programs (IS) including management, construction, carrier delivery, and quality.
in keeping with Dandekar, corporates infatuation Satyam own their personnel certified the entire course from UK. despite the fact, with this alliance, Quexst turns into the first enterprise to deliver the ISEB training in India. The BCS in affiliation with Unicom would supply the lessons in India.
Quexst offers exhaustive training that customarily stretches between three to five days. The examination is supervised with the aid of the computing device Society of India, while the papers are corrected with the aid of the BCS. The certification too is awarded from BCS.
when it comes to eligibility, aside from the necessary commencement, the degree of content is such that it necessitates arms-on application construction sustain of two to 3 years.
charge-intelligent she mentioned it is relatively much less costly than what is offered in UK. Disclosed Dandekar, The route payment levels from Rs 15,000 to Rs forty,000, while the examination fees may subsist between Rs 5000-7000.
So, who're their target viewers?
Says Dandekar, In view of the sheer prices of the lessons, they predict corporate sponsorships for personnel who would want to assume in the path. additionally, when developers are ISEB-licensed, IT groups can derive stronger mileage in terms of positioning their items or services in the international markets, above outright in the European market.
beneath the existing agreement with Unicom, Quexst will present the courses in Delhi, Calcutta, Pune and Mumbai. in the meantime in the Southern belt, they draw to present it in two facilities from the three zeroed right down to specifically, Hyderabad, Chennai, and Bangalore. Six months later, they draw to present it in cities corresponding to Nagpur, Indore considering that these are feeder cities to the IT industry.
Quexst buddies presents functions in software checking out, technique consulting and working towards functions.
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SINGAPORE: An oversight in the computer system administered by NCS caused about 7,700 individuals to receive inaccurate healthcare and intermediate- and long-term impregnate subsidies, the Ministry of Health (MOH) said on Saturday (Feb 16).
The affected individuals are among those whose applications or renewals of their Community Health Assist Scheme (CHAS) cards were processed from Sep 18 to Oct 10 ultimate year.Advertisement
The oversight arose in the computer system when it calculated means-test results.
The means-test system calculates the healthcare subsidies which individuals are eligible for, based on their income information. Healthcare subsidies are means-tested so that greater monetary uphold is extended to lower-income households.
"The means-test and subsidy tiers for outright affected individuals own been corrected by Feb 16, 2019,” MOH said.
MOH said no proactive action is required on the Part of the affected people at this point. The ministry added that it is working closely with healthcare service providers and scheme administrators to compass out to those affected.Advertisement Advertisement READ: CHAS IT error: A timeline of how it unfolded
"About 6,400 individuals received higher subsidies than what they are eligible for. The total amount is estimated to subsist about S$2 million. MOH intends to regain from NCS the costs and expenses incurred as a result of this incident, as allowed for under their contract.
"About 1,300 individuals received lower subsidies than what they are eligible for. The total amount is estimated to subsist about S$400,000. This will subsist reimbursed to the affected individuals," MOH said.
MOH said discrepancies in the means-test results of a few CHAS cardholders were noticed by the CHAS card processing team in MOH Holdings when it was reviewing CHAS applications and appeals.
"We received three appeals for the group affected," MOH said.
The first case of discrepancy was detected on Sep 24, 2018 and NCS was alerted immediately, MOH said.
“The issue was initially attributed to intermittent network connection problems. Five more cases were subsequently detected between Oct 9 and Nov 2, and a more thorough investigation was initiated,” added the health ministry.
In late November, NCS traced the root cause of the discrepancies to a software version issue on a server used by the means-test system when it was migrated to another government data centre in September.
“This resulted in the means-test results being computed without the requisite income information. NCS further discovered that their deployment team had in fact fixed the software version issue earlier on Oct 10, 2018 in response to an unrelated unhurried performance issue.
“This stopped further cases of errors but it did not reform the means-test results that had been generated from Sep 18, 2018 to Oct 10, 2018,” MOH said.
CORRECT SUBSIDY TIERS RESTORED BY FEB 16
On Dec 5, MOH received NCS' assessment that the glitch could own affected thousands of people across multiple schemes.
Before making any public announcements, MOH said that it was valuable to rectify the root cause of the glitch in the system to avert more inaccurate means-test results.
It was besides valuable to fully establish the different groups of people and subsidies that were affected, and how they were affected, as well as confine the repercussion of inaccurate means-test results from affecting more schemes and individuals.
MOH added that it was valuable to own a clear rectification draw that can subsist deployed quickly after the announcement, to compass out to the affected individuals to inform and reimburse them, where applicable.
"If they enact not enact so, they could cause confusion and uncertainty, not just among the affected, but the larger group of unaffected. Scheme administrators and service providers will besides puss many queries for which they are unable to respond," MOH said.
MOH said that it worked with NCS from December to establish the extent of the impact, including the reform subsidy tiers for each individual under the different services and schemes. This was to determine who could own received higher or lower subsidies than what they were eligible for.
The final assessment was completed on Jan 14 and MOH worked with vouchsafe scheme administrators and healthcare institutions to finalise the remedial action plans, including how affected individuals will subsist informed and reimbursed.
The reform subsidy tiers of outright affected individuals were restored by Feb 16.
Service providers and scheme administrators will now progressively inform the affected individuals and sort for reimbursements where applicable.
“We await outright the affected individuals to subsist informed by mid-March 2019,” MOH said.
MOH said that NCS has acknowledged the oversight and has taken further remedial action by tightening the system deployment processes.
“Additional safeguards own been Put in residence to avert any recurrence of such incidents. NCS has reiterated its commitment to being held to the highest standards as a service provider.
MOH takes a serious view of the incident, and has worked with NCS on commandeer remedial measures. MOH will travail with NCS on measures to avert such errors in the future,” the ministry stated.
NCS is an information and communications technology (ICT) and communications engineering services provider. It is a subsidiary of the Singtel Group.
According to its website, NCS offers services that include consulting, technology solutions, as well as systems evolution and integration.
Pradipto Chakrabarty tells you why one should trap certified
In a world where recruiters assume mere seconds to scan through most resumes, a pertinent certification inserted at the right place, can build your resume stand out from the rest.
Certifications declare your prospective employer that you are prepared for a job in an all-round manner. They besides betoken to your peers that you assume your job role seriously. They act as a signal of your competency in roles where basic and practical skills are required for the evolving technology industry. IT certifications signify that you are well-versed with practically demonstrable skills and will assume incorporate emerging technological scenarios in your accounting.
After a basic computer IT course certification, experts recommend that you travail in an IT organisation for at least nine months, implementing, installing, maintaining, troubleshooting and configuring simple to mid-level intricate networks and computer systems before you chase into more specialised fields such as cyber-security.
Enterprise technology certifications are incomplete without knowledge of how intricate servers and routers manage information, communication, transmission and encryption. Certifications are cheaper to trap than full-fledged degrees, proffer greater flexibility in terms of both learning content and outcomes and spy splendid on your CV too!
The writer is Regional Director, CompTIA India
One of the Great ironies of the “big data” revolution is the course in which so much of the insight they draw from these massive datasets actually comes from small samples not much larger than the datasets they own always used. A social media analysis might originate with a trillion tweets, exhaust a keyword search to reduce that number to a hundred million tweets and then exhaust a random sample of just 1,000 tweets to generate the final result presented to the user. As their datasets trap ever larger, the algorithms and computing environments they exhaust to resolve them own not grown accordingly, leaving their results to subsist less and less representative even as they own more and more data at their fingertips. What does this spell for the future of “big data?”
Stepping back from outright of the hype and hyperbole, there is considerable truth to the statement that they live in an era in which data is valued sufficiently that they believe it worth the time and expense to collect, store and resolve it at scales that significantly exceed those of the past.
It is just as exact that many of their beliefs regarding the size of the datasets they exhaust are are absolutely wrong. In particular, many of the vanguards of the mountainous data era that they hold up as benchmarks of just what it means to travail with “big data” infatuation Facebook and Twitter, are actually vastly smaller than they own been led to believe.
In many ways, much of the size of the “big data” revolution exists only in their imaginations, aided by the reality distortion field of the mountainous web companies that tout their mammoth scale without actually releasing the difficult numbers that might lead us to muster those claims into question.
Most troublesome of all, however, is the course in which they resolve the data they have.
Computing power continues to multiply and in today’s world of GPUs, TPUs, FPGA’s and outright other manner of accelerators, they own no shortage of hardware on which to speed their analyses. The problem is that despite nearly unfathomable amounts of hardware humming away in the data centers of the mountainous cloud companies, they are silent just as hardware constrained as they own always been.
We may own vast amounts of hardware, but the datasets they wish to resolve are even larger.
It is a truth of the modern computing era that it is far cheaper to collect and store data than it is to resolve it. In many ways this is an ironic reversal from even just a decade ago in which many great scientific computing codes would recompute intermediate results from scratch rather than store them to disk due to it being faster to compute the result again than load it from a great direct attached HPC storage fabric.
Where CPU once outpaced disk, today it is the opposite. Storage is now so cheap that keeping multiple copies of a petabyte across multiple geographically distributed datacenters for maximum redundancy costs less than $10,000 a month in the cloud. Storing a lone copy in an on-premises JBOD costs less than $25,000 worth of NAS-grade drives and prices continue to fall.
The ratio between the size of their data and the computing power they own to process that data isn’t remarkably changed.
My IBM PS/2 Model 55SX desktop in 1990 had a 16Mhz CPU, 2MB of RAM and a 30MB difficult drive. That’s roughly one Hz of processor power for every 1.87 bytes of difficult drive space.
Fast forward almost 30 years to 2019. A typical commercial cloud VM “core” is a hyperthread of a 2.5Ghz to 3.5Ghz (turbo) physical processor core with 2GB-14GB RAM per core and effectively eternal storage via cloud storage, though local disk might subsist limited to 64TB or less. Using the same CPU-to-disk ratio as 1990, that 3.5Ghz turbo hyperthread would subsist paired to 6.6GB of disk (actually, 3.3GB of disk, taking into account the fact that a hyperthread is really only half of a physical core).
Such a comparison isn’t quite impartial given the capabilities of newer CPUs and the vastly improved speeds of modern disk systems, but it is worth pointing out just how far the ratio between CPU power and disk storage has slipped over the past three decades.
Even when it comes to data transfer rates, itsy-bitsy has changed in those 30 years. The disk transfer rate on the 1989 IBM PS/2 Model 55SX was rated at 7.5MBPS (around 0.94MB/s). Dividing the CPU’s 16Mhz clock speed by 0.94MB/s works out to around 17 bytes/s per Hz. mercurial forward today and a typical cloud VM might max out at around 180MB/s maximum sustained read from standard persistent disk. With a turbo clock speed of 3.5Ghz hyperthread / 180MB/s that works out to around 19 bytes/s per Hz, roughly the same disk transfer rate per Hz almost 30 years later.
Of course, there are many other factors that influence the speed with which data from disk can actually subsist utilized by a CPU in practice, but no matter how you slice it, in terms of the ratio between the performance of their storage systems and the performance of their CPU systems, they haven’t really undergone a transformative shift in the decades since I sat at my first desktop. Moreover, the virtualization of the modern cloud enacts a sheer cost that slows it even further.
Modern clouds address this differential through data parallelism, sharding data across multiple virtual cores. Yet, to maintain the same ratio of CPU to disk to process a petabyte with 3.5Ghz virtual cores in the modern cloud would require a cluster of 152,381 VM cores, assuming faultless linear scalability. A 10PB dataset would require 1.5 million VM cores. In reality, communications overhead and the limits of hardware scaling spell the number of cores required to achieve academic scaling results would subsist considerably higher.
Amazingly, the CPU cost for outright those cores would only subsist a few hundred dollars to a few thousand dollars per hour of analysis and a few thousand dollars to store the petabyte for a week (not counting the time it takes to upload or ship).
Moreover, platforms infatuation Google’s cloud build it petty to scale across great datasets, with BigQuery able to table scan a plenary petabyte in under 3.7 minutes with one line of SQL, no programming or manual data sharding required.
We obviously aren’t lacking for the hardware to process petabytes or even tens or hundreds of petabytes.
In fact, BigQuery even offers fixed rate pricing starting at $40,000 per 2,000 slots that allows companies to fulfill unlimited queries over unlimited volumes of data for the same fixed monthly cost. A company’s data science division could query multi-petabyte datasets non-stop 24 hours a day for the same fixed cost, removing cost as a limiting factor in performing absolute population-scale analyses.
The problem is that when it comes to mountainous data analyses there seems to subsist a tremendous gulf between the companies performing population-scale analyses using tools infatuation BigQuery that resolve the totality of their datasets and recur absolute results and the repose of the “big data” world in which estimations and random samples seem to dominate.
Estimations are particularly prevalent in spaces infatuation social media and behavioral analysis.
In a world in which outright it takes is a lone line of SQL to resolve tens of petabytes with absolute accuracy, why is estimation so popular?
Partially the reply is their preoccupation with speed over accuracy. Why wait minutes for petascale analyses when you can wait seconds for a random sample that may or may not suffer even the slightest resemblance to reality?
In turn, their talent to tolerate immense oversight in their “big data” results often comes from the fact that the consequences for execrable results are minimal in many “big data” domains. An analysis of the most influential Twitter users by month for a given topic over the ultimate few years doesn’t really own a “right” reply against which an estimation can subsist compared. Moreover, even if the results are entirely wrong, the consequences are minimal.
Partially the reply is that their more intricate algorithms own not kept pace with the size of the datasets they travail with today. Many of the analytic algorithms of greatest interest to data scientists were born in the era of small data and own yet to subsist modernized for the size of data they wish to apply them to today. Few mapping platforms can fulfill spatial clustering on billions of points, while graph layout algorithms struggle to scale beyond millions of edges.
In short, the volume of data available to us has outpaced the talent of their algorithms to build sense of it. In the past they had low volumes of very wealthy data. Today they own towering volumes of very indigent data, import they own to process much more data to achieve the same results.
When their algorithms aren’t scalable, they are left processing the same amount of data as before, but that sample is less and less representative of the whole.
A classical SNA graph analysis of the past might involve a network with a few thousand or tens of thousands of edges, which could subsist visualized in its entirety even by the software of the day. Today they can easily obtain graphs in the billions of edges, but their most common graph visualization tools struggle to defer useable results in reasonable time with anything more than a few hundred thousand edges.
That means that while a visualization of the past might own reflected the entire graph, today that graph is more likely to represent just a miniscule fraction of the plenary dataset. A 10,000-edge graph visualized in Gephi shows the totality of its structure. A 10 billion-edge graph sampled down to 10,000 edges represents just 0.0001% of the total graph.
It isn’t just a question of speed. Many of their most heavily used algorithms were never designed to travail with great datasets even when speed is not of concern. Few graph layout algorithms can enact much to extract the structure of a dense trillion-edge graph even if given limitless computing power and as much time as they requisite to complete.
In short, they are confronted with the paradox that the more data they have, the less representative their findings are due to the requisite for sampling.
Platforms infatuation BigQuery are slowly changing the algorithmic side of that equation. As they chase beyond their reporting roots towards providing towering flush analyses infatuation geospatial analytics, they are birth to externalize the kindly of massive algorithmic scalability that Google uses internally to bring more and more algorithms into the scalable cloud era. As these trends progress, algorithmic scalability will steadily become less of a limiting factor for common exhaust cases.
That leaves the mindset factor.
We requisite to chase beyond the credence that it is acceptable to fulfill the “sampling in secret” used by some social media analytics platforms in which certain displays quietly depend on sampling even while they declare their users they fulfill absolute counts.
Sampling can subsist useful in certain circumstances. However, they requisite to understand precisely when and where it is used, the size of the sample and how that sample was selected. Understanding that a visualization was based on a random sample of just 1,000 out of 100 million matching tweets might give respite to how representative its results truly are. Moreover, if that “random sample” is actually based on randomly selecting 100 dates and taking the first 10 tweets from each date in chronological order, that may result in very different findings than sampling at the tweet level. Having visibility into these methodological decisions is absolutely critical.
Perhaps the biggest issue is that they requisite to discontinue treating “big data” as a marketing gimmick.
As companies own begun to market themselves in terms of the size of the datasets they hold and analyze, we’ve let ebb of the credence of actually understanding anything about the data and algorithms we’re using.
Even the most rigorous data scientists freely accept the credence of reporting trends from Twitter without having any credence just what the plenary trillion-tweet archive from which they are working actually looks like. In fact, few data scientists that travail with Twitter even know there own been just over one trillion tweets sent.
Understanding your denominator used to subsist something that was considered sacrosanct to data science. Somehow, we’ve reached a point where they just accept reporting findings from analyses where they no longer own a denominator.
Once they start treating “big data” analysis as a methodologically, algorithmically and statistically rigorous process based on well understood data, they recognize the requisite for population-scale analyses with guaranteed correctness. They turn to platforms infatuation BigQuery and its ilk to speed their analyses and focus on accuracy and completeness. They modernize the algorithms they need. They relentlessly test their results.
Most importantly, they restore the denominator to their workflows.
Putting this outright together, it is ironic that in a world drowning in data, they own increasingly turned to sampling to render their “big data” small again. As their datasets own grown in size, they own sampled them ever more aggressively to preserve the actual amount of data fed to their analyses and algorithms roughly the same. Whereas in years past their analyses might own been based on the totality of a dataset, today that same analysis might consider just one ten thousandth of one percent of that dataset. As they own ever more data, their results are becoming ever less representative.
In short, their vaunted “big data” revolution has actually resulted in less understanding of their world through less representative data than ever before.
In the end, they requisite to discontinue treating “big data” as a marketing slogan and bring ourselves back to the era where they actually cared about the quality of their data analyses before they undermine the public's faith in the power of data.
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