|Exam Name||:||IBMSPSSMBPDM- IBM SPSS Modeler Business(R) Partner Data Mining Associate|
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|Updated On||:||February 21, 2019|
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SPS-202 exam Dumps Source : IBMSPSSMBPDM- IBM SPSS Modeler Business(R) Partner Data Mining Associate
Test Code : SPS-202
Test Name : IBMSPSSMBPDM- IBM SPSS Modeler Business(R) Partner Data Mining Associate
Vendor Name : IBM
Q&A : 25 Real Questions
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IBM tops the list of companies in the predictive analytics application area 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 appropriate in product high-quality, reliability, and breadth and depth of offering. IBM SPSS Modeler also captured the suitable spot in criteria like geographic footprint and viability of business.
360Quadrants defines predictive analytics as a statistical and statistics mining answer including a large 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, fair 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 couple 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 also 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 knowledge of without coding will now be 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 meaning and cost from structured and unstructured textual content healthcare information from care 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 chance 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, all with out the need for advanced coding easing the project of integration and pace to answer deployment. We seem to be 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 right through the nation. Our high-tech and excessive-touch fashions include innovative know-how options, fine care coordination functions, and focus on great consequences and optimization of company and payer networks. eQHealth serves a lot of entities together with federal, state 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 be sure you’ve invested your time and financial 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 building rapidly in areas such as media monitoring, publishing, customer-experience management and semantic search. Meanwhile, growth remains strong in domains that have long applied text analytics. These areas include life sciences, intelligence and law enforcement, and financial 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 stay on top of trends, I try to talk to current and prospective users as often as I can, and I also try to keep up with researchers and fellow analysts. (You’ll find links for some of the folks I follow at my BeyeNETWORK channel.) I also catch 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 do you see as the 3 (or fewer) most important 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 part because “…for me, 2009 is too close a time horizon.” This thought applies not only to academic researchers but also 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 see the greatest challenges as residing in areas their tools address, so take their views with a grain of salt.
I’ll relay all 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 field needs is a profound and real success story 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 also at business concerns. Aaron is Program Director, ECM Discovery, for IBM Information Management Software and has great market and technical insights. I interviewed him last year on “Text Analytics for Legal Compliance.” That exchange is still a good read. Here are his thoughts on challenges for 2009:
Other replies also looked at business conditions and at experiences in the adoption and use of text technologies. Lexalytics CEO Jeff Catlin foresees:
Craig Norris, CEO at Attensity, similarly noted three varieties of challenge, in his case relating to technology, implementation and seeing opportunity in adverse business conditions:
Vendor prognosticators with a solutions focus include Keith Collins, CTO at SAS, and Manya Mayes, SAS Chief Text Mining Strategist, see 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, large organizations are forced to keep up a fast pace of business with fewer resources, and it is therefore critical that these organizations have all 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 little in the way of being operationalized within business processes.” Neil continues:
Yes, there are proofs of concept, run by experienced vendor staff but these [prototypes], in my experience, rarely get 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 high degree of automation (without the need for excessive setup and maintenance) together with clarity in the analysis and the ability to apply control to the process where needed. This is exactly what Leximancer provides.
The other major trend I see is the need to make social media actionable, and this is something we’ve focused on heavily on our blog. I think customer attitudes on social media or microblogging sites are a leading indicator for the business. The business that waits for these trends to be reflected in their formal feedback programs may find it is too late to take effective action.Technology and Applications
I had expected that a higher proportion of 2009 “challenges” responses would center 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, like 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 also 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 social 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 our clients, but also with a couple of “megatrends” he is beginning 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 take on 2009 text-analytics challenges, Lexalytics’ Jeff Catlin suggested a bake-off to be 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 we can get something going for this year – a commercially oriented version of the TREC (Text REtrieval Conference) challenge. Stay tuned, and do consider attending this year’s summit.
And if you’re a current or prospective text analytics user and would like to tell me about challenges you face or expect to face, please do get in touch by email at email@example.com or by phone at (301) 270-0795).
Seth is a business intelligence and decision 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. Be sure to visit today!
Recent articles by Seth Grimes
Data scientists rely on tools/products/solutions to help them get 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 go together. In the latter posting, he looked at pairs of tools and found that some tools tend to go together (usage of tools are correlated with each other). He offered the anonymized raw data set for free to encourage 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 all tools simultaneously. In general, principal components analysis examines the statistical relationships (e.g., covariances) among a large set of variables and tries to explain 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 strength of relationship between the variables and each of the underlying components. The results of the principal components analysis tell us two things:
This use 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 describe the data, the selection of the number of components is based on the results. The goal of the current analysis was to explain the relationship among the 95 tools with as few components as was necessary. Toward that end, there are a couple 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 unity (1.0). Another way 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 break around the 13th and 14th eigenvalue. Therefore, I chose a 13-factor solution to explain 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 somewhat easy 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 tend to go together (if you use one of IBM’s tools, you tend to use 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 suggest that 13 tool grouping describe the data. I’ve listed the groupings below and include the tools that describe each grouping (if number of votes are greater than 20). Tools that fall within a specific group tend to be used together.
The use of specific Big Data, data mining and data science tools tend to occur together. Based on the current analysis of tool usage, the 90+ tools can be grouped into a smaller subsets of tools. The results of the current analysis are somewhat consistent with the prior results. For example, Piatetsky found that Pig usage was closely associate with Hadoop, Spark, scikit-learn, Unix shell/awk/gawk and Python. In the current analysis, we found that Pig was also associated with Hadoop and Spark. However, we found that the use of Pig was associated with HBase, Hive, Mahout, MLlib, Scala and SQL on Hadoop. It’s important to note that the former analysis used the top 20 tools while the latter analysis used all 90+ tools; this difference could explain the differences between the two analyses.
To improve your chances of success in your Big Data projects, it’s important that you select the right data tools. No single tool will do it all, and data professionals tend to use more than one data-related tool (in this study, we found that, on average, data professionals use 5 data tools). One way to help in your selection process is to identify the tools sets that other data professionals are using. The results suggest that you consider looking at tools within components as potential candidates for tools you might consider.
Some of the tool 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 work together better than with other vendors’ products. Still, more research is definitely needed to understand why certain tools tend to be 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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