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C2090-012 - IBM SPSS Data Analysis for Business Partners v2 - Dump Information

Vendor Name : IBM
Exam Code : C2090-012
Exam Name : IBM SPSS Data Analysis for Business Partners v2
Questions and Answers : 25 Q & A
Updated On : October 16, 2018
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IBM C2090-012 Exam (IBM SPSS Data Analysis for Business Partners v2) Detailed Information

C2090-012 Test Information / Examination Information


Number of questions : 25
Time allowed in minutes: 60
Required passing score : 68%
Languages : English


C2090-012 Objectives


C2090-012 Questions and Answers

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C2090-012 IBM SPSS Data Analysis for Business Partners v2

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C2090-012 exam Dumps Source : IBM SPSS Data Analysis for Business Partners v2

Test Code : C2090-012
Test Name : IBM SPSS Data Analysis for Business Partners v2
Vendor Name : IBM
Q&A : 25 Real Questions

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IBM IBM SPSS Data Analysis

Why Watson and SPSS Are IBM’s huge information Yin and Yang | killexams.com Real Questions and Pass4sure dumps

  • publish
  • after all the speak during the last few weeks about what IBM’s (s ibm) Watson victory on Jeopardy! capability and doesn’t suggest, about what the gadget is and isn’t able to doing, it’s becoming clear that Watson is not HAL of 2001: an area Odyssey notoriety. but when utilized in concert with predictive analytics application, technologies like Watson can develop into part of a complete large-facts architecture that may alternate the facts-analysis online game for every thing from clinical diagnoses to sentiment analysis on Twitter. besides the fact that children, the promise of this holistic information atmosphere doesn’t stop with IBM.

    As I mentioned in a contemporary submit explaining the bounds of Watson as a machine-learning platform, its capacity to system and reply questions based on herbal language is a big deal, but the system as currently comprised is largely relegated to the realm of answering particular questions in keeping with the very selected facts loaded into it. however, because of a $14 billion funding in analytics acquisitions over the last several years, IBM has a robust portfolio products with which to complement Watson’s magnificent capabilities. in line with IBM VP of Predictive Analytics Deepak Advani, SPSS — the predictive piece of IBM’s analytics puzzle, which it purchased for $1.2 billion in 2009 — might simply be Watson’s most advantageous mate.

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    The percentages within the fitness care business — the place IBM already has proven Watson’s expertise could be utilized subsequent — is a great instance of what Advani is speakme about. the usage of SPSS software, he defined, a doctor should be would becould very well be in a position to mine years of affected person data, in addition to different relevant facts sources, to predict the likelihood of a specific patient setting up a major cardiovascular condition, for example. The next step is deciding on the most effective remedy plan, and that’s the place Watson comes in.

    medical doctors have a confined amount of time to examine scientific journals — perhaps simply five hours a month — Advani observed, so Watson’s means to shop and method the assistance contained in, pretty much, all accessible scientific journals as they’re launched becomes very essential. asked a question about the most beneficial-viable treatment plan for that certain affected person, given the affected person’s specific historical past, Watson might scour its database to suggest a plan, or plans, for the medical professional to trust. due to the fact that the myriad variables affecting the success of any given treatment plan, Watson could be able to select a plan that medical professional could not probably have advised.

    The Watson-plus-SPSS combo also may well be very constructive in deriving insights from social media statistics, albeit by the use of a distinct setup. In predictive analytics, Advani defined, the greater facts users put in, the greater correct their results, which makes social media a chief target for application corresponding to SPSS. in spite of everything, he referred to, groups can behavior the entire surveys they need, however consumers are only offering up these insights for free on Twitter and other sites. He described how tv organizations are already using SPSS to do sentiment analysis on quite a few social media websites to make changes to their programming in precise time in response to what they have been in a position to verify clients favored and didn’t like.

    besides the fact that children, even with the ability to realize slang, context and other probably confounding elements, Advani recounted that mining social media records may not be utterly exact off the bat, which is why he recommends a step-via-step approach. just about, he says, the technique is to take heed to the records, take measured moves in line with it, write predictive algorithms in keeping with those outcomes, and then refine the algorithms. If used accurately, predictive software may still finally let clients determine, based on information gleaned from Twitter and in different places, what the public likes and doesn’t like, and the consequences of appearing in any specific method can be.

    within the social media realm, Watson might come into the photograph previous, when attempting to system purchaser sentiment toward any specific subject. In a contemporary Forbes article on the way forward for the Watson technology, IBM VP of emerging applied sciences Rod Smith mentioned the use case of an unnamed client “combing via months value of Twitter chatter to extract an image of what buyers believe concerning the customer’s items.” Of direction, one needn’t use the accurate Watson know-how stack for data mining at such an epic scale — Hadoop functions, including IBM’s InfoSphere BigInsights utility may do the job, too — but Watson’s natural-language capabilities may make the manner somewhat a little bit easier and, maybe, extra accurate.

    in the equal article, Smith made an extra observation that really drives home the element of how rather accessible the Watson, or Watson-like expertise, might turn into once IBM starts formally turning it into a product. Whereas Jeopardy! required Watson to reply a question in a count of seconds, such actual-time performance would be useless in all but the most-disturbing enterprise environments, which cuts down on the infrastructure required. As Smith mentioned, if a customer could wait two minutes for an answer, it wouldn’t want an entire room filled with servers dedicated to just to that cause.

    I may still element out, notwithstanding, that IBM is rarely the simplest seller or company targeting these kinds workloads; it simply has the deep pockets to required to have them all below its banner, and the advertising capabilities to make Watson a family identify. SAS, for instance, is a tremendous player in predictive analytics, and the Apache Mahout venture is dedicated to the styles of machine-learning algorithms that make Watson so sensible. And, as alluded to by way of Smith, it doesn’t require an IBM Power7-based supercomputer to churn out answers in lower than actual time; commodity machines might work just high-quality. The true story is concerning the introduction and democratization of advanced records-processing suggestions mixed with superior predictive analytics to achieve probably the most correct and actionable insights possible. It’s a narrative worth telling — and selling.

    to hear more about what every person is doing in the huge records area, make sure to attend out structure huge records convention March 23 in big apple city. IBM’s Jeff Jonas will be speaking on “geospatial records as analytic tremendous-meals,” and different audio system span the spectrum from EMC (s emc) to Revolution Analytics to InfoChimps.

    connected content from GigaOM pro (sub req’d):

  • Will fb (or Apple) Be the next tremendous Hadoop Champion?
  • huge records Marketplaces Put a value on finding Patterns
  • What IBM Does With huge statistics

  • SPSS brass set to cash in on IBM deal | killexams.com Real Questions and Pass4sure dumps

    (Crain's) -- IBM Corp.'s $1.2-billion buyout of enterprise-application maker SPSS Inc. will create a big windfall for company executives.

    CEO Jack Noonan, Chief financial Officer Raymond Panza and other appropriate brass at Chicago-based SPSS stand to assemble tens of thousands and thousands of bucks within the surprise deal, introduced Tuesday morning. IBM's offer of $50 money for each and every share of SPSS amounts to a 43% top class over Monday's closing expense of SPSS stock on the Nasdaq inventory market.

    Mr. Noonan, CEO when you consider that 1992, holds inventory and options value $22.5 million within the deal, according to his holdings on Dec. 31 as disclosed within the business's most fresh proxy commentary. Mr. Panza's stock and options are value $11.7 million at the buyout expense.

    If both are terminated after the sale, their employment contracts supply for additional cash funds. Mr. Noonan would obtain $6 million and Mr. Panza $2.7 million. it's no longer clear whether both will live on, and the enterprise did not return phone calls in quest of remark.

    usual, SPSS personnel cling $90 million of inventory alternatives and restrained shares that automatically develop into vested, based on filings. SPSS, headquartered in the Loop, has about 1,200 employees, half of them remote places.

    IBM didn't expose its plans for SPSS, together with no matter if it plans to eliminate any jobs on the enterprise after the acquisition, which requires approval through SPSS shareholders and is expected through the events to shut this year. SPSS' $303 million in annual income represents simplest a tiny fraction of IBM's $104 billion.

    The lack of SPSS as a stand-on my own public company hurts a native application business that has few primary home-grown gamers. however the acquisition may give a source of sorely crucial funding and talent for others.

    "every time an organization is acquired, you come to be with much less of a presence within the metropolis," says Matt McCall, managing director at Northfield-primarily based mission capital firm Draper Fisher Jurvetson Portage. "i would now not be stunned to see a handful of recent opportunities spin out of SPSS or see startups with SPSS executives on them."

    SPSS, headquartered in 1968 through three Stanford university graduate students who migrated to the college of Chicago's national Opinion analysis middle, makes what is referred to as predictive analytics application used with the aid of companies and governments to research survey records, identify patterns and make predictions about customer habits, hiring needs and different selections. or not it's a key enviornment for IBM, as it tries to find technology that businesses will spend funds on all over a downturn.

    company intelligence is a $25-billion market, dominated by using a handful of corporations which have been snatched up by way of giants IBM, Oracle and SAP. SPSS dominates the desktop-computer market for predictive analytics and already had a cross-selling relationship with IBM.

    "SPSS wasn't out browsing itself," says Steve Ashley, an analyst at Robert W. Baird & Co. in Milwaukee. "IBM's big push at this time is to offer equipment to consumers to make experience of their statistics, and SPSS become the remaining most important independent business-intelligence software enterprise."

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    C2090-012 IBM SPSS Data Analysis for Business Partners v2

    Study Guide Prepared by Killexams.com IBM Dumps Experts


    Killexams.com C2090-012 Dumps and Real Questions

    100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



    C2090-012 exam Dumps Source : IBM SPSS Data Analysis for Business Partners v2

    Test Code : C2090-012
    Test Name : IBM SPSS Data Analysis for Business Partners v2
    Vendor Name : IBM
    Q&A : 25 Real Questions

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    What you need to know about predictive analytics | killexams.com real questions and Pass4sure dumps

    Predictive analytics

    In an era when data is seen as an increasingly valuable commodity, the tools and techniques used to analyze that data gain extra importance.

    Historically analysis, even using computers, has been something of a labor intensive task because the raw data needed to be verified and complex models built to process it. But due to the growth of machine learning and data mining techniques we’re now seeing an increase in predictive analysis where machines can take historical and current information and apply it to a model to predict future trends.

    Whilst in business terms predictive analytics is primarily about looking to the future -- spotting sales trends for example -- it has other uses too. It can be used to spot trends that might indicate fraudulent credit card use for example. As with any other type of data processing the quality of the results depend heavily on the integrity of the data and the quality of the underlying model.

    Analytical Models

    Predictive analysis relies on the use of models to "score" data. These models come in three main types. The most common is the predictive model which uses sample data with known attributes to train the model so that it can then look at new data and determine how it’s likely to behave. This information can be used to predict customer behavior or spot patterns of activity.

    Descriptive models are used to classify data into groups. Where predictive models focus on the behavior of an individual customer, to determine credit ratings for example, descriptive models sort customers by characteristics like their age or their past buying behavior. This information can then be used in marketing campaigns aimed at specific groupings.

    Decision models take a wider view, taking account of the known data as well as results from predictive models. This allows them to forecast the results of decisions that involve a large number of different variables.

    Within these models various statistical techniques are at the heart of processing the data. The regression model is the most common, used to define the relationship between variables. In addition time series modeling is used to forecast the way variables will behave in future.

    Technology

    Predictive analysis is closely tied to other technologies, in particular machine learning. Data mining too is an important part of unlocking the value contained in business systems. As more powerful systems become readily available technologies like neural networks will be applied to build more complex analysis models.

    Commercial analytics tools are available from many of the big IT suppliers. These include SAP HANA, Oracle Advanced Analytics and IBM SPSS Modeler. Open source tools are also on offer from developers including GNU and Apache.

    Business Uses

    Predictive analytics has been used in many areas of business in recent years. It’s frequently found in customer relationship management (CRM) systems where it can help in building sales campaigns, or providing customer services. Analytics can spot buying patterns and identify problems that could lead to lost business.

    The internet has made it much easier for customers to compare services and products from different suppliers, so increasingly companies are looking at how they can retain existing business. Predictive systems can help here too, by looking at buying records and service use patterns it’s possible to spot when someone is perhaps thinking about switching suppliers. They can then be targeted with marketing initiatives to persuade them to stay.

    In some businesses predictive analysis is aimed more at preventing problems than at gaining new business. A 2014 survey by SAP revealed that just over 40 percent of financial services businesses used predictive analytics to minimize risk. We’ve already touched on the use of predictive systems to spot fraudulent activity, the US Internal Revenue Service is known to use predictive analysis on tax returns to spot suspicious activity, but they have other uses in this sector too. Predictive systems can identify those customers who are likely to make late payments for example and help focus collection efforts. For insurance providers the technology can be used in the identification of risk and thus help to calculate premiums more accurately.

    The medical sector is also starting to use predictive analysis techniques. On a macro scale they can be used to predict what proportion of the population is likely to develop certain conditions such as heart disease or diabetes. But they can also support clinical decision making, using patient data to plan long-term treatment.

    Another field where predictive analytics has the scope to make a big difference is project management. Here it can be used to predict the risks of project failures, overspends and so on, data which can be used by managers to keep things on track.

    Security too is an area where this type of analysis is going to gain in importance. The latest behavioral systems are able to analyze the way a person uses a computer or phone and accurately predict when an account sign on is being used by someone else. This has implications not just for securing systems but for policing things like pay walled websites.

    The Future

    There is some skepticism surrounding the value of predictive analytics, much of this surrounds the importance of using data in context. Lots of factors influence individual behavior and many of these -- such as weather conditions or someone having had an argument with their partner -- are impossible to factor into even the most sophisticated model.

    But despite these doubts business leader are keen to take advantage of the opportunities predictive technology can offer. "The impact of integrating real-time analytics with business operations is immediately apparent to business people because it changes the way they do their jobs, says Jim Sinur, research vice president at Gartner. "The most dramatic change is the increased visibility in how the company is running and what is happening in its external environment. Individual contributors and managers have more situational awareness, so they are able to make better decisions faster".

    Our everyday world is more and more driven by data and although there may be limitations in what predictive systems can achieve, that data can still offer valuable insights. Businesses need to remain competitive and by helping them to identify opportunities, improve customer service and minimize risks predictive analytics is almost certain to play a key role.

    Published under license from ITProPortal.com, a Future plc Publication. All rights reserved.

    Photo Credit: one photo/Shutterstock


    Upcoming m-ize Webinar Focuses On Detecting and Preventing Warranty Fraud Using Predictive Analytics | killexams.com real questions and Pass4sure dumps

    TAMPA, Fla.--(BUSINESS WIRE)--m-ize, the leading provider of Smarter Customer Engagement solutions, is proud to be hosting an upcoming webinar Detecting and Preventing Warranty Fraud Using Predictive Analytics.

    During the session an expert panel will discuss how predictive analytics can drive cost savings through elimination of warranty fraud. The webinar will be presented live Wednesday, May 7, 2014 at 1:00 EDT.

    Warranty claims fraud is currently costing manufacturers billions of dollars annually. Recent studies indicate that up to 10% of warranty costs are related to claims fraud.

    Companies suspect warranty fraud, but are not sure of the extent, or ways to eliminate it. Existing tools and methods to detect warranty fraud are complex and expensive, causing manufacturers to unwillingly bear excessive costs.

    Terry Hawkins, President of the Global Warranty and Service Contract Association, and advisor to m-ize, reports, “Excess warranty payments have been a real and thorny issue for the industry and companies can quickly add millions to their bottom line with effective warranty fraud analysis solution in place.”

    Predictive Analytics is the next step in Business Intelligence, helping manufacturers reduce fraud and improve processes. Applied to business, predictive models are used to analyze current data and historical facts in order to better understand customers, products and partners as well as identifying potential risks and opportunities for an organization.

    Featured in a recent Warranty Week article, m-ize CEO Ashok Kartham said, “m-ize is bringing cloud based warranty fraud detection capabilities to reduce the cost, time and expertise needed to realize immediate benefits by leveraging the leading predictive analytics platform powered by IBM SPSS.”

    In addition to the moderator, Warranty Week Editor, Eric Arnum, the panel for this webinar includes:

  • Venky Rao, North American Predictive Analytics Segment Leader, IBM
  • Terry Hawkins, President, Global Warranty & Service Contract Association
  • Ashok Kartham, Founder & Chief Executive Officer, m-ize
  • Together these four individuals represent decades of expertise in the warranty space. Their combined level of understanding related to warranty chain management and appropriate solutions is unmatched in the industry.

    Live Q & A with the audience will follow the presentation. For more information and to register for the event, please visit:  http://info.m-ize.com/detecting-and-preventing-warranty-fraud-using-predictive-analytics



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