SPS-202 exam Dumps Source : IBMSPSSMBPDM- IBM SPSS Modeler Business(R) partner Data Mining Associate
Test Code : SPS-202
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IBM tops the list of companies in the predictive analytics application district in line with a quadrant these days launched via 360Quadrants, followed closely by using Oracle and SAP SE. IBM’s SPSS Modeler turned into rated the most confiscate in product high-quality, reliability, and breadth and depth of offering. IBM SPSS Modeler moreover captured the suitable spot in criteria enjoy geographic footprint and viability of business.
360Quadrants defines predictive analytics as a statistical and statistics mining acknowledge including a great number of algorithms and methodologies used for structured in addition to unstructured information to extract company insights.
360Quadrants covers 50+ products in the predictive analytics space and locations the excellent 31 of them in a quadrant depending on their excellent, reliability, and business influence. These 31 products are classified into Visionary Leaders, Dynamic Differentiators, emerging groups, and Innovators.
360Quadrants recognizes IBM Corp, SAS Institute, Inc., SAP SE, unprejudiced Issac organisation (FICO), Tableau application, Inc., RapidMiner, Inc., Oracle Corp, and Angoss utility Corp as Visionary Leaders; TIBCO application, Inc., Microstrategy, Inc., Alteryx, Inc., assistance Builder, Dataiku, KNIME.COM AG, and NTT facts enterprise, as Innovators; GoodData business enterprise, Microsoft corporation, Teradata corporation, Sisense, Inc., Predixion utility, and Domino records Lab, Inc. as Dynamic Differentiators; and Exago, integrated, AgilOne, QlikTech overseas, and Kognitio Ltd. as rising gamers.
IBM these days announced that it's bettering a pair of of its present utility products with the Apache Spark open-supply data processing engine, including the SPSS predictive analytics software.
IBM purchased SPSS for $1.2 billion in 2009. SPSS itself began at Stanford institution in 1968 and is regular in statistics courses at universities.
IBM is particularly incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman informed VentureBeat in an electronic mail.
Spark is moreover being applied to IBM’s BigInsights, Streams, and DataWorks application, in response to a press release.
The Sparkification of IBM comes after huge Blue introduced a major dedication to the open-supply utility in June. on the time, IBM introduced Spark as a provider on its Bluemix cloud; that service is now often accessible.
IBM has made greater than 60 contributions to Spark considering the June announcement, in line with the observation.
Unstructured facts, textual content analytics, and computing device gaining lore of without coding will now live built-in into eQHealth population fitness Analytics
BATON ROUGE, La. --(company WIRE)
eQHealth options, a frontrunner in population health administration and scientific administration options, currently grew to become an IBM enterprise companion and utilizer of the IBM Embedded Analytics answer. The goal of the settlement is to support eQHealth’s analytics capabilities in digesting and extracting import and cost from structured and unstructured textual content healthcare information from custody coordinator’s fitness possibility assessments, clinician notes, and different kinds of unstructured statistics. The IBM facts Science Platform lets eQHealth leverage SPSS Modeler for predictive modeling, laptop studying, statistical evaluation, text analytics and resolution optimization. this could permit eQHealth to swiftly progress from statistics exploration to insight and creation. The IBM SPSS Modeler additionally presents effortless deployment for desktop discovering. further, SPSS Modeler will increase present predictive modeling and random stratification capabilities.
eQHealth has employed facts analytics and data to deliver insights and determination assist for medical assistance and clinical administration technique improvements for his or her customers and developed the first edition of the enterprise’s proprietary Peer Utilization assessment application (PURs) greater than 25 years in the past. including IBM SPSS Modeler is anticipated to provide an extra layer of performance to latest competences.
“IBM SPSS Modeler is varied because of its skill to support many statistics types and sources, deliver a number of algorithmic strategies and machine getting to know thoughts, every bit of with out the exigency for advanced coding easing the project of integration and pace to acknowledge deployment. They appear to live ahead to an extended and productive relationship with IBM and the SPSS Modeler group,” stated Mayur Yermaneni, Chief approach and boom Officer, eQHealth options.
About eQHealth solutions
founded in 1986, eQHealth solutions is a inhabitants fitness management and know-how solutions enterprise that touches thousands and thousands of lives annually privilege through the nation. Their high-tech and excessive-touch fashions include innovative know-how options, fine custody coordination functions, and focus on much consequences and optimization of company and payer networks. eQHealth serves a lot of entities together with federal, situation and industrial consumers. www.eqhs.org
eQHealth SolutionsKatie Varnado, 225-248-7069Channel advertising Managereqhealth@eqhs.org
Copyright business Wire 2019
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You’re reading this article because you’re actively interested in text analytics technology and market trends. Whether you’re a solutions provider or consumer, you want to live sure you’ve invested your time and fiscal resources wisely.
The picture is complex, however, as the text analytics domain is not dominated by any one algorithm or approach, vendor or business sector. So far, the diversity of options has benefitted potential users; awareness (and realization) of the technology’s capabilities is structure rapidly in areas such as media monitoring, publishing, customer-experience management and semantic search. Meanwhile, growth remains sturdy in domains that possess long applied text analytics. These areas include life sciences, intelligence and law enforcement, and fiscal services. While the text analytics domain isn’t unaffected by current business conditions, economic pressures could actually spur uptake motivated by a quest for efficiency through automation.Sources and Perspectives
To abide on top of trends, I try to talk to current and prospective users as often as I can, and I moreover try to sustain up with researchers and fellow analysts. (You’ll find links for some of the folks I supervene at my BeyeNETWORK channel.) I moreover entangle up with vendors periodically.
Because it’s helpful to understand current industry perspectives, I recently invited CEOs, CTOs and thought leaders to respond to the following query:
What outcome you perceive as the 3 (or fewer) most primary text analytics technology, solution or market challenges in 2009?
Before relaying answers I received, I’ll mention two meta-replies. First, Marti Hearst, associate professor in the UC Berkeley School of Information, declined to respond, in piece because “…for me, 2009 is too immediate a time horizon.” This thought applies not only to academic researchers but moreover to anyone who’s thinking strategically, beyond this year’s crop of product releases and business conditions. Next, Lexalytics CEO Jeff Catlin candidly characterized his response as “somewhat self-serving thoughts.” No doubt many of the vendors quoted perceive the greatest challenges as residing in areas their tools address, so capture their views with a grain of salt.
I’ll relay every bit of views with minimal editing, complete with vendor self-promotion because, after all, commercial products are “where the rubber hits the road” for most enterprise users.Text Analytics in 2009
Let’s start with Maria Milosavljevic, CTO at Capital Markets Cooperative Research Centre, an Australian group that is conducting leading-edge R&D in real-time security-market surveillance. The top 3 for Maria are:
Breck Baldwin of Alias-i, author of LingPipe natural language processing software, is very to-the-point in his reply, with a sort of twist on Maria’s expectations response.
#1 thing the realm needs is a profound and real success myth of text analytics.
The reply from Sid Banerjee, CEO of Clarabridge, looks at factors related to scaling text analytics to the enterprise, “the expansion of text analytics across a few dimensions.” His reply:
Aaron B. Brown of IBM similarly looks at architectural issues and moreover at business concerns. Aaron is Program Director, ECM Discovery, for IBM Information Management Software and has much market and technical insights. I interviewed him eventual year on “Text Analytics for Legal Compliance.” That exchange is still a well-behaved read. Here are his thoughts on challenges for 2009:
Other replies moreover looked at business conditions and at experiences in the adoption and expend of text technologies. Lexalytics CEO Jeff Catlin foresees:
Craig Norris, CEO at Attensity, similarly famous three varieties of challenge, in his case relating to technology, implementation and seeing occasion in adverse business conditions:
Vendor prognosticators with a solutions focus include Keith Collins, CTO at SAS, and Manya Mayes, SAS Chief Text Mining Strategist, perceive as challenges for 2009:
Yves Schabes, President of Teragram, a SAS company, focuses his response on one particular challenge – increasing workers’ efficiency:
Given the current economic situation, great organizations are forced to sustain up a speedily pace of business with fewer resources, and it is therefore faultfinding that these organizations possess every bit of of their content organized correctly so that employees can spend more time fulfilling their jobs and less time searching for information on their enterprise's system.
Neil Hartley, CEO of Leximancer, sees “an industry that has been around for a long time and yet has seen diminutive in the pass of being operationalized within business processes.” Neil continues:
Yes, there are proofs of concept, dash by experienced vendor staff but these [prototypes], in my experience, rarely accumulate adopted by the business a) because of the setup and maintenance required, and b) because the source data vocabulary changes making the initial setup redundant. Autonomy is the exception, but their deployments are largely search/retrieval-based and less qualitative.
What the business needs is a tall degree of automation (without the exigency for inordinate setup and maintenance) together with clarity in the analysis and the talent to apply control to the process where needed. This is exactly what Leximancer provides.
The other major trend I perceive is the exigency to manufacture gregarious media actionable, and this is something we’ve focused on heavily on their blog. I mediate customer attitudes on gregarious media or microblogging sites are a leading indicator for the business. The business that waits for these trends to live reflected in their formal feedback programs may find it is too late to capture efficacious action.Technology and Applications
I had expected that a higher balance of 2009 “challenges” responses would headquarters on the technology and its applications. Responses I’ve already quoted touch on those areas with perhaps greater attention to business and market concerns. Eric Martin, Product Marketing Manager at SPSS, focused solely on solutions, which to me reflects both Eric’s confidence in his company’s market position and his own background: Eric earned a Ph.D. in immunology and cellular biology and, enjoy many others, started using text mining on biomedical articles before joining text analytics pioneer LexiQuest, which was later acquired by SPSS. Challenge areas Eric sees for 2009 are:
Matthew Hurst of Microsoft Live Labs is likewise a techie at heart, and he’s moreover very interested in user-generated and other online content. Matthew says, “As reported elsewhere in research and industry literature, the majority of textual data being published online today is from the many genres of gregarious media. To fully leverage this data, the key challenges are:
Lastly, Ren Mohan, Co-Chairman and CTO of IxReveal, replied not only with the three challenges his company hears most often from their clients, but moreover with a pair of “megatrends” he is nascence to see. Challenges are:
Ren’s “megatrends” are:
Ren’s conclusion: “These are truly exciting times!” Indeed.A Live Challenge?
Here are thoughts on one more potential challenge for 2009, a live challenge, of a different variety.
As an aside while forwarding his capture on 2009 text-analytics challenges, Lexalytics’ Jeff Catlin suggested a bake-off to live held at the 2009 Text Analytics Summit, which is slated for June 1-2 (preceded by tutorials) in Boston. Jeff says in his blog, “Our market is still dominated by too much flashy marketing and not enough down in the dirt numbers for ‘apples to apples’ comparisons.” Jeff is right, although I’ll add that I believe that most of the flashy marketing actually fronts very capable products. Perhaps they can accumulate something going for this year – a commercially oriented version of the TREC (Text REtrieval Conference) challenge. abide tuned, and outcome deem attending this year’s summit.
And if you’re a current or prospective text analytics user and would enjoy to expose me about challenges you visage or await to face, gladden outcome accumulate in touch by email at firstname.lastname@example.org or by phone at (301) 270-0795).
Seth is a business intelligence and conclusion systems expert. He is founding chair of the Text Analytics summit and principal consultant at Washington, D.C., based Alta Plana Corporation. Seth consults, writes, and speaks on information-systems strategy, data management and analysis systems, IT industry trends, and emerging analytical technologies. Seth chairs the Sentiment Analysis Symposium and the Text Analytics Summit.
Editor’s Note: More articles and resources are available in Seth's BeyeNETWORK Expert Channel. live sure to visit today!
Recent articles by Seth Grimes
Data scientists reckon on tools/products/solutions to lighten them accumulate insights from data. Gregory Piatetsky of KDNuggets conducts an annual survey of data data professionals to better understand the different types of tools they use. Here are the results of the 2015 survey. He followed his initial posting with additional analyses to better understand which tools depart together. In the latter posting, he looked at pairs of tools and institute that some tools attend to depart together (usage of tools are correlated with each other). He offered the anonymized raw data set for free to animate other people to analyze the data, which I did.Dimension Reduction through Principal Components Analysis
His approach looked at pairs of tools to understand their relationship with the other. I took a slightly different approach. I applied principal components analysis. The current approach groups the tools by looking at the relationship among every bit of tools simultaneously. In general, principal components analysis examines the statistical relationships (e.g., covariances) among a great set of variables and tries to clarify these correlations using a smaller number of variables (components).
The results of the principal components analysis are presented in tabular format called the principal component matrix. The factor matrix is an NxM table (N = number of original variables and M = number of underlying components). The elements of a principal component matrix represent the relationship between each of the variables and the components. These elements represent the power of relationship between the variables and each of the underlying components. The results of the principal components analysis expose us two things:
This expend of principal components analysis is exploratory in nature. That is, I didn’t impose a pre-defined structure on the data itself. The pattern of relationships among the 95 tools drove the pattern of results. While human judgment comes into play in the determination of the number of components that best characterize the data, the selection of the number of components is based on the results. The goal of the current analysis was to clarify the relationship among the 95 tools with as few components as was necessary. Toward that end, there are a pair of rules of thumb that I used to determine the number of components using the eigenvalues (output of principal components analysis). The first rule of thumb is to set the number of components based on the number of eigenvalues greater than solidarity (1.0). Another pass is to plot (called a scree plot) the 95 eigenvalues to identify a clear breaking point along the eigenvalues.
The plot of the eigenvalues appeared to wreck around the 13th and 14th eigenvalue. Therefore, I chose a 13-factor solution to clarify the relationships among the 95 data science tools.Table 1. Principal Component Matrix of 95 Data Science Tools – data from KDNuggets 2015 annual survey of data professionals. Click image to enlarge.
Based on a 13-factor solution, the principal component matrix (see Table 1) was quite smooth to interpret. Some of the cell values in the matrix in Table 1 are bold to represent values greater than .33. The components’ headings are based on the tools that loaded highest on that component. For example, four IBM products loaded highly on component 6, showing that usage of these tools by a given respondent attend to depart together (if you expend one of IBM’s tools, you attend to expend the other(s)); as a result, I labeled that component as IBM. Similarly, based on the tools that were highly related to the other 12 factors, the other 12 factors were labeled accordingly.Tool Groupings
The results imply that 13 utensil grouping characterize the data. I’ve listed the groupings below and include the tools that characterize each grouping (if number of votes are greater than 20). Tools that drop within a specific group attend to live used together.
The expend of specific gigantic Data, data mining and data science tools attend to occur together. Based on the current analysis of utensil usage, the 90+ tools can live grouped into a smaller subsets of tools. The results of the current analysis are quite consistent with the prior results. For example, Piatetsky institute that Pig usage was closely associate with Hadoop, Spark, scikit-learn, Unix shell/awk/gawk and Python. In the current analysis, they institute that Pig was moreover associated with Hadoop and Spark. However, they institute that the expend of Pig was associated with HBase, Hive, Mahout, MLlib, Scala and SQL on Hadoop. It’s primary to note that the former analysis used the top 20 tools while the latter analysis used every bit of 90+ tools; this incompatibility could clarify the differences between the two analyses.
To help your chances of success in your gigantic Data projects, it’s primary that you select the privilege data tools. No lone utensil will outcome it all, and data professionals attend to expend more than one data-related utensil (in this study, they institute that, on average, data professionals expend 5 data tools). One pass to lighten in your selection process is to identify the tools sets that other data professionals are using. The results imply that you deem looking at tools within components as potential candidates for tools you might consider.
Some of the utensil groupings are simply based on the parent company of the tools, including IBM, Microsoft and SAS. Perhaps cross-selling data science tools is a lot easier within a major brand than it is across different brands. This makes sense to the extent that a company’s products likely drudgery together better than with other vendors’ products. Still, more research is definitely needed to understand why inevitable tools attend to live used together.
I’m interested in seeing what others find with these data. Here is the link to the anonymized data set (CSV format).
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