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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 designation : IBM SPSS Predictive Analytics Sales Mastery v1
Vendor designation : IBM
: 45 true Questions

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IBM IBM SPSS Predictive Analytics

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

IBM tops the listing of vendors within the predictive analytics utility belt 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 likewise captured the correct spot in standards relish 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 traffic 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 traffic 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.

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a glance at the IBM SPSS Modeler and IBM SPSS facts analytics equipment | killexams.com true 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 limited 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 exercise different processes and algorithms to attend the user learn information hidden within the statistics. The device can additionally attend in integrating and consolidating complete 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 basis laptop learning algorithms.
  • Extensions that supply continued advancements for employ with open supply products, reminiscent of R and Python.
  • improved assist for a pair of multithreaded analytical algorithms, together with Random timber, Tree-AS, Generalized Linear Engine, Linear-AS, Linear attend Vector computer and Two-Step-AS clustering.
  • The capacity to dash a variety of Python and Spark computing device studying, in addition to other Python analytics libraries natively in Modeler with out requiring the employ 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 employ throughout a company.
  • SPSS Modeler top class: This edition includes advanced algorithms and capabilities, akin to textual content analytics, entity analytics and gregarious community analysis, that raise model accuracy with unstructured data.
  • SPSS Modeler Gold: This version gives analytical selection administration, collaboration and deployment capabilities. SPSS Modeler Gold is likewise accessible as a cloud offering.
  • IBM SPSS information points

    SPSS facts version 24 includes the following unique points:

  • The capacity to access greater than one hundred extensions, enabling users to hold 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 rigor construct and set up their personal extensions. Enhancements include unique controls and unique homes for present controls and a number of other advancements to the person interface.
  • improvements that allow users to extra without rigor and promptly import and export data into SPPS records.
  • advancements to the customized Tables module, including unique 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 attend 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 attend facts coaching, lacking values and facts validity, determination bushes, and forecasting.
  • SPSS records top rate provides advanced analytical suggestions, together with structural equation modeling, in-depth sampling assessment and testing. This bundle likewise contains methods that goal direct marketing and excessive-end charts and graphs.
  • Pricing for the SPSS Modeler and SPSS facts predictive analytics equipment disagree counting on the bundle alternate options, the variety of users and the license duration. SPSS data is now attainable as a subscription selection 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 unique challenges to information governance

    linked materials View more

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

    IBM these days introduced that it's bettering a pair 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 complete 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 likewise being utilized to IBM’s BigInsights, Streams, and DataWorks software, in line with a press release.

    The Sparkification of IBM comes after gigantic 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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    IBM SPSS Predictive Analytics Sales Mastery v1

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    Winning the #ArtificialIntelligence War | @ExpoDX #IoT #DigitalTransformation | killexams.com true questions and Pass4sure dumps

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

    Note: synthetic intelligence (AI) endows applications with the competence to automatically learn and proper from sustain via interacting with the surroundings / environment. behold the blog “Artificial Intelligence is not Fake Intelligence” for a more particular explanation on synthetic intelligence and machine learning.

    The rapidly Company article “How to desist Worrying and relish the magnificient AI War of 2018,” projected that the AI battle would ultimately simmer down between the “AI gigantic 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 breathe the ultimate winners in this magnificient 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 maintain the following characteristics:

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

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

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

    By the way, I referee Kleiner Perkins was sluggish 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 vigilant of any AI or data technologies that Facebook sells to the universal 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 employ the technology to derive and then drive unique sources of customer, business, operational, and financial value that matters. Ultimately, the AI war is about “value capture.”

    The companies listed in pattern 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 gregarious media, and advertising
  • Each of these AI leaders seeks to extend their value capture capabilities into unique markets, including transportation (autonomous vehicles), healthcare, finance, media, and entertainment.

    Other market leaders are likewise pitiable aggressively to exploit the power of AI to capture more customer, products and operational value. JPM Morgan (#11) is focused on structure an AI platform (see “JPMorgan Takes AI employ to the Next Level”) that will allow JPMC to dominate financial trading. And GE (#16) has made a strategic ante with their Predix platform (see “GE’S gigantic ante 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 gregarious 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 traffic 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 synthetic 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 breathe 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 unique 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 captious is that these AI leaders are seeking to avoid technology and architecture lock-in. They maintain watched archaic 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 expense 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 magnificient AI War.

    #3 Mastery of gigantic DataEveryone knows about the astounding growth of gigantic data over the terminal decade as organizations focused on capturing particular customer, product, operational and market data. Initially fueled by commerce, web and gregarious media data, gigantic data has accelerated with the growth of video, wearables, and the Internet of Things. (See pattern 2).

    However, organizations maintain struggled to monetize this wealth of data. Enter synthetic intelligence.

    Figure 2: Fueling the Insatiable Appetite for Data

    More Data = Better AI. synthetic intelligence can exploit massive data sets to identify patterns on a scale that flummox traditional traffic 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 abysmal Learning application by virtue of owning the world’s largest repository of photos.

    To illustrate the symbiotic relationship between gigantic data and AI, let’s glare at autonomous vehicles (AV). AV require enormous quantities of data to feed the AV machine learning algorithms. It would hold 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 attend generate enough data in order to train Autonomous Vehicles (see “GTA is Teaching Self-Driving Cars How to Navigate Better in the true 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 structure the machine learning and synthetic intelligence models (see “Data Lake traffic Model Maturity Index” for more details on data lake traffic 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 breathe used simultaneously at near-zero marginal cost across an infinite traffic and operational employ cases. Understanding the proper economic value of the organization’s data can attend to prioritize technology and traffic 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 traffic Strategies with Data Science,” AI winners will ultimately breathe those organizations that are the most effectual at leveraging data and analytics to power their traffic models (see pattern 3).

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

    Ultimately, AI winners will master three key characteristics:

  • Focus on Value Capture by identifying, validating and prioritizing the organization’s key traffic and operational employ 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 gigantic Data and the Data Lake to exploit the unique economic value of the data and analytic digital assets (see “Data Lake traffic Model Maturity Index”).
  • So in conclusion, let’s maintain some fun with this blog and referee outside of the box about some hypothetical scenarios in which companies exploit this AI gold rush:

  • What would breathe the traffic model ramifications to GE if they were to open source Predix and tender Predix training to universities and third party developers?
  • What would breathe the traffic model ramifications to JPMC if they were to open source their trading platform to universities and third party developers?
  • What would breathe the traffic model ramifications if IBM moved out of the technology purveyor traffic 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 magnificient AI war, now is not the time for organizations to breathe diffident or to cling to old, outdated traffic 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 magnificient synthetic Intelligence (AI) War! appeared first on InFocus Blog | Dell EMC Services.

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