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Vendor : IBM
Exam Code : 00M-229
Exam Name : IBM SPSS Predictive Analytics Sales Mastery v1
Questions and Answers : 45 Q & A
Updated On : February 19, 2019
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00M-229 IBM SPSS Predictive Analytics Sales Mastery v1

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00M-229 exam Dumps Source : IBM SPSS Predictive Analytics Sales Mastery v1

Test Code : 00M-229
Test Name : IBM SPSS Predictive Analytics Sales Mastery v1
Vendor Name : IBM
Q&A : 45 Real Questions

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IBM IBM SPSS Predictive Analytics

Predictive Analytics utility market's visionary leaders illuminated through new file | killexams.com Real Questions and Pass4sure dumps

IBM tops the listing of vendors within the predictive analytics utility area according to a quadrant recently launched through 360Quadrants, followed closely by Oracle and SAP SE. IBM’s SPSS Modeler become rated the most excellent in product excellent, reliability, and breadth and depth of offering. IBM SPSS Modeler also captured the correct spot in standards like geographic footprint and viability of business.

360Quadrants defines predictive analytics as a statistical and statistics mining solution consisting of a lot of algorithms and methodologies used for structured in addition to unstructured records to extract business insights. 

360Quadrants covers 50+ products in the predictive analytics house and places the top 31 of them in a quadrant reckoning on their first-class, reliability, and business outcome. These 31 products are labeled into Visionary Leaders, Dynamic Differentiators, emerging corporations, and Innovators.

360Quadrants recognizes IBM Corp, SAS Institute, Inc., SAP SE, reasonable Issac organisation (FICO), Tableau software, Inc., RapidMiner, Inc., Oracle Corp, and Angoss application Corp as Visionary Leaders; TIBCO utility, Inc., Microstrategy, Inc., Alteryx, Inc., suggestions Builder, Dataiku, KNIME.COM AG, and NTT statistics organization, as Innovators; GoodData supplier, Microsoft service provider, Teradata service provider, Sisense, Inc., Predixion utility, and Domino data Lab, Inc. as Dynamic Differentiators; and Exago, incorporated, AgilOne, QlikTech foreign, and Kognitio Ltd. as rising avid gamers.

This e-mail handle is being blanketed from spambots. You want JavaScript enabled to view it.


a glance at the IBM SPSS Modeler and IBM SPSS facts analytics equipment | killexams.com Real Questions and Pass4sure dumps

IBM's SPSS predictive analytics equipment consist of IBM SPSS Modeler and IBM SPSS records. SPSS Modeler offers information mining and text evaluation utility, whereas SPSS statistics is an built-in family unit of items. each equipment allow clients to construct predictive models and execute different analytics projects.

The IBM SPSS Modeler objectives clients who've little or no programming advantage. users are provided with a drag-and-drop consumer interface, enabling them to build predictive fashions and function other statistics analytics. Modeler can practice different processes and algorithms to help the user discover information hidden within the statistics. The device can additionally help in integrating and consolidating all types of facts sets from dispersed records sources across the corporation.

The IBM SPSS information suite is an integrated set of products geared towards extra expert information analysts. SPSS data addresses the finished analytical manner, from planning to information collection, evaluation, reporting and deployment.

IBM SPSS Modeler features

edition 18 offers here features:

  • greater than 30 base laptop learning algorithms.
  • Extensions that supply continued advancements for use with open supply products, reminiscent of R and Python.
  • improved assist for a couple of multithreaded analytical algorithms, together with Random timber, Tree-AS, Generalized Linear Engine, Linear-AS, Linear help Vector computer and Two-Step-AS clustering.
  • The capacity to run a variety of Python and Spark computing device studying, in addition to other Python analytics libraries natively in Modeler with out requiring the use of the Analytic Server, as changed into required within the outdated edition.
  • SPSS Modeler bundles are deployed on premises, and SPSS Modeler Gold is attainable as a cloud providing. The client front conclusion of SPSS Modeler runs beneath windows and macOS, while the server part runs on Unix, Linux and home windows.

    IBM SPSS Modeler provides the following editions:

  • SPSS Modeler very own: A single-user desktop product.
  • SPSS Modeler expert: A computer product that works with IBM SPSS Analytic Server, offering stronger scalability and efficiency and enabling applications for use throughout a company.
  • SPSS Modeler top class: This edition includes advanced algorithms and capabilities, akin to textual content analytics, entity analytics and social community analysis, that raise model accuracy with unstructured data.
  • SPSS Modeler Gold: This version gives analytical choice administration, collaboration and deployment capabilities. SPSS Modeler Gold is also accessible as a cloud offering.
  • IBM SPSS information points

    SPSS facts version 24 includes the following new points:

  • The capacity to access greater than one hundred extensions, enabling users to take knowledge of free libraries written in R, Python and SPSS syntax.
  • The IBM SPSS Extension Hub to browse, download, update, remove and generally manage extensions.
  • a tremendous improve to the customized Dialog Builder, enabling users to more without difficulty construct and set up their personal extensions. Enhancements include new controls and new homes for present controls and a number of other advancements to the person interface.
  • improvements that allow users to extra without difficulty and promptly import and export data into SPPS records.
  • advancements to the customized Tables module, including new statistical functionality and customer-requested facets.
  • IBM SPSS statistics presents the following three variations (each and every with additional modules):

  • SPSS facts regular equipment deliver superior statistical strategies that help linear and nonlinear statistical fashions, as well as predictive simulation modeling, which debts for unclear inputs, geospatial analytics and customised tables.
  • SPSS statistics professional tools help facts coaching, lacking values and facts validity, decision bushes, and forecasting.
  • SPSS records top rate provides advanced analytical suggestions, together with structural equation modeling, in-depth sampling assessment and testing. This bundle also contains methods that goal direct marketing and excessive-end charts and graphs.
  • Pricing for the SPSS Modeler and SPSS facts predictive analytics equipment differ counting on the bundle alternate options, the variety of users and the license duration. SPSS data is now attainable as a subscription choice or a perpetual license. IBM presents free trials of both IBM SPSS Modeler and IBM SPSS data.

    next Steps

    Why the time period unstructured statistics is a misnomer

    How huge statistics is changing statistics modeling recommendations

    big information techniques pose new challenges to information governance

    linked materials View more

    IBM is speeding up SPSS with Apache Spark | killexams.com Real Questions and Pass4sure dumps

    IBM these days introduced that it's bettering a couple of of its existing application products with the Apache Spark open-source statistics processing engine, including the SPSS predictive analytics software.

    IBM purchased SPSS for $1.2 billion in 2009. SPSS itself all started at Stanford tuition in 1968 and is customary in records classes at universities.

    IBM is particularly incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman informed VentureBeat in an e mail.

    Spark is also being utilized to IBM’s BigInsights, Streams, and DataWorks software, in line with a press release.

    The Sparkification of IBM comes after big Blue announced a tremendous dedication to the open-supply utility in June. on the time, IBM announced Spark as a provider on its Bluemix cloud; that provider is now frequently purchasable.

    IBM has made more than 60 contributions to Spark seeing that the June announcement, according to the commentary.


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    There is a war a-brewin’, but this war will be fought with wits and not brute strength. Ever since Russian President Vladimir Putin’s declaration that “the nation that leads in AI (Artificial Intelligence) will be the ruler of the world,” the press and analysts have created hysteria regarding the ramifications of artificial intelligence on everything from public education to unemployment to healthcare to Skynet.

    Note: artificial intelligence (AI) endows applications with the ability to automatically learn and adapt from experience via interacting with the surroundings / environment. See the blog “Artificial Intelligence is not Fake Intelligence” for a more detailed explanation on artificial intelligence and machine learning.

    The Fast Company article “How to Stop Worrying and Love the Great AI War of 2018,” projected that the AI battle would ultimately boil down between the “AI Big 6”:  Alphabet/Google, Amazon, Apple, Facebook, IBM, and Microsoft. However, there are other contenders worthy of consideration including GE, Tesla, Netflix, Baidu, Tencent, and Albaba.

    But what are the characteristics of organizations that will be the ultimate winners in this Great AI War? What are the behaviors and actions that will distinguish those organizations that capitalize on this AI gold rush while others “fumble the future”?

    I believe that the AI winners will have the following characteristics:

  • Users, not purveyors, of AI technology
  • Embrace open source for technology agility (independence)
  • Mastery of Big Data (and no, Big Data is not dead)
  • Let me state my case.

    #1 Users, Not Purveyors, of AI TechnologyThe Market Capitalization Leaderboard shown in Figure 1 offers important clues as to which organizations will likely be the AI winners. What will set these organizations apart will be not the selling of technology, but their ability leverage AI for “value capture.”

    Figure 1: Marketing Capitalization Leaders as of May 26, 2017.

    By the way, I think Kleiner Perkins was lazy in classifying “Industry Segment.” The market leaders are less purveyors of AI technology than they are users of AI technology.

  • Less than 10% of Amazon’s revenue comes from technology (cloud); $12B in cloud revenue out of a total revenue of $136B in 2016. So what Industry Segment are they in?
  • Google had quarterly revenues (Q1, 2016) of $26B of which digital media/advertising (search) represented $23B. Their “other” businesses (including Google Cloud) were only $3B. So what Industry Segment are they in?
  • Apple’s most recent quarterly (Q3, 2016) revenues were $42B out of which the iPhone (personal communications, information and entertainment) and the associated iPhone ecosystem (iTunes, Apple Music, App Store) comprised an aggregated $37.5B.
  • Finally, I’m not aware of any AI or data technologies that Facebook sells to the general market. Facebook generated $9.3B in revenue in Q2, 2017 of which $9.16B came from Ad revenue. So what Industry Segment are they in?
  • Mastering Value Capture. Just having the technology is not sufficient; it’s how you use the technology to derive and then drive new sources of customer, business, operational, and financial value that matters. Ultimately, the AI war is about “value capture.”

    The companies listed in Figure 1 are trying to dominate markets, not technology. For example:

  • Apple (#1) seeks to dominate personal communications
  • Google/Alphabet (#2) seeks to dominate digital media, advertising and personal communications
  • Amazon (#4) seeks to dominate online commerce
  • Facebook (#5) seeks to dominate social media, and advertising
  • Each of these AI leaders seeks to extend their value capture capabilities into new markets, including transportation (autonomous vehicles), healthcare, finance, media, and entertainment.

    Other market leaders are also moving aggressively to exploit the power of AI to capture more customer, products and operational value. JPM Morgan (#11) is focused on building an AI platform (see “JPMorgan Takes AI Use to the Next Level”) that will allow JPMC to dominate financial trading. And GE (#16) has made a strategic bet with their Predix platform (see “GE’S Big Bet on Data and Analytics”) as the platform for dominating the Industrial Internet of Things.

    Microsoft (#3) is the one exception as Microsoft is a purveyor of technology. But even Microsoft is branching beyond just selling technology into trying to dominate markets such as digital media, entertainment, and social media where their AI “chops” can give them competitive advantages (see “The Jewel of Microsoft’s Earnings”).

    #2 Embrace Open Source for Technology Agility (Independence)AI leaders will exploit open-source business models to establish platform dominance/standardization, and create technology agility and independence. They will develop an enabling technology, and then give it away via open source. This enables them to encourage the growing community of developers, especially those up-and-coming developers in universities and research labs, to build out and create de facto standards around their enabling technologies.

    Open Source Leaders. The Global AI winners are significant contributors to the artificial intelligence and machine learning open source communities. This includes developments such as Amazon Machine Learning, Google TensorFlow, Facebook Caffe2, Microsoft Azure ML Studio, Microsoft Distributed Machine Learning Toolkit, Facebook GraphQL, and Facebook Torch.

    The leadership role that the “Great AI War” combatants are playing can be seen in many open source projects. For example, Torch is an open source machine learning library and scientific computing framework. The “official maintainers” of Torch are:

  • Research Scientist @ Facebook
  • Senior Software Engineer @ Twitter
  • Research Scientist @ Google DeepMind
  • Research Engineer @ Facebook
  • Training and Education. Another strategy from the Global AI leaders the creation of community or industry training and education opportunities around their open source technologies. For example, Google is committing $1 billion to train American workers to build new businesses with Google’s AI tools (see “Google Commits $1 Billion in Grants to Train U.S. Workers for High-Tech Jobs”).

    Avoiding Technology Lock-in.  But equally important is that these AI leaders are seeking to avoid technology and architecture lock-in. They have watched old school organizations struggle with proprietary software packages that took months if not years for upgrades and bug fixes, while paying a burdensome annual maintenance fees (33% of list price means you’re buying the entire software package again every 3 years). In a world where the enabling data and analytic technologies are changing nearly daily, technological and architecture agility (at scale) and independence is mandatory for organizations looking to win the Great AI War.

    #3 Mastery of Big DataEveryone knows about the astounding growth of big data over the last decade as organizations focused on capturing detailed customer, product, operational and market data. Initially fueled by commerce, web and social media data, big data has accelerated with the growth of video, wearables, and the Internet of Things. (See Figure 2).

    However, organizations have struggled to monetize this wealth of data. Enter artificial intelligence.

    Figure 2: Fueling the Insatiable Appetite for Data

    More Data = Better AI. Artificial intelligence can exploit massive data sets to identify patterns on a scale that flummox traditional Business Intelligence “slice and dice” and query technologies. Data is the food that feeds AI. The more data the AI models consume, the smarter AI gets. For example, Facebook is mastering facial recognition via its DeepFace Deep Learning application by virtue of owning the world’s largest repository of photos.

    To illustrate the symbiotic relationship between big data and AI, let’s look at autonomous vehicles (AV). AV require enormous quantities of data to feed the AV machine learning algorithms. It would take tens of thousands of hours of real-world driving data across a variety of driving scenarios to teach cars how to navigate on their own. To address this data volume problem, AV companies are using the video game “Grand Theft Auto” to help generate enough data in order to train Autonomous Vehicles (see “GTA is Teaching Self-Driving Cars How to Navigate Better in the Real World”).

    Data Lake. Leading AI organizations are exploiting the data lake concept to not only store the growing wealth of structured and unstructured (internal and publicly-available) data, but to provide an elastic, scalable, self-provisioning data science platform for “collaborative value creation” in building the machine learning and artificial intelligence models (see “Data Lake Business Model Maturity Index” for more details on data lake business model maturation).

    Exploiting the Economic Value of Data. Leading AI organizations realize that data and analytics are unlike any traditional corporate assets. Data and analytics are digital assets that never wear out, never deplete, and can be used simultaneously at near-zero marginal cost across an infinite business and operational use cases. Understanding the true economic value of the organization’s data can help to prioritize technology and business investments that accelerate value capture from these data sources (see University of San Francisco research paper “Determining the Economic Value of Data” for more details).

    Conclusion: How to Become an AI WinnerAs has been discussed many times in my blog series, and explored in detail in my book, “Big Data MBA: Driving Business Strategies with Data Science,” AI winners will ultimately be those organizations that are the most effective at leveraging data and analytics to power their business models (see Figure 3).

    Figure 3: How Effective Is Your Organization at Leveraging Data and Analytics to Power Your Business Models?

    Ultimately, AI winners will master three key characteristics:

  • Focus on Value Capture by identifying, validating and prioritizing the organization’s key business and operational use cases (see “Use Case Identification, Validation and Prioritization”).
  • Avoid technology and architecture lock-in and create technology independence via an open source technology strategy
  • Mastery of Big Data and the Data Lake to exploit the unique economic value of the data and analytic digital assets (see “Data Lake Business Model Maturity Index”).
  • So in conclusion, let’s have some fun with this blog and think outside of the box about some hypothetical scenarios in which companies exploit this AI gold rush:

  • What would be the business model ramifications to GE if they were to open source Predix and offer Predix training to universities and third party developers?
  • What would be the business model ramifications to JPMC if they were to open source their trading platform to universities and third party developers?
  • What would be the business model ramifications if IBM moved out of the technology purveyor business and instead acquired companies in financial services and healthcare where their Watson AI platform could create market dominance?
  • As the world prepares for the impending great AI war, now is not the time for organizations to be shy or to cling to old, outdated business models.

    Fortune Favors the Brave.

    Sources

    Figure 1: ScoopNest “2017 global market capitalization leader board: tech is 40% of top 20 companies and 100% of top 5” and Consultancy UK “Market capitalisation of world’s 100 biggest companies hits $17.4 trillion”

    The post 3 Keys to Winning the Great Artificial Intelligence (AI) War! appeared first on InFocus Blog | Dell EMC Services.

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    IBM 00M-229 Exam (IBM SPSS Predictive Analytics Sales Mastery v1) Detailed Information



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