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Vendor : IBM
Exam Name : IBM SPSS Modeler - Business(R) Partner Data Mining Associate
Questions and Answers : 25 Q & A
Updated On : April 22, 2019
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IBMSPSSMBPDM IBM SPSS Modeler - Business(R) Partner Data Mining Associate

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IBMSPSSMBPDM exam Dumps Source : IBM SPSS Modeler - Business(R) Partner Data Mining Associate

Test Name : IBM SPSS Modeler - Business(R) Partner Data Mining Associate
Vendor Name : IBM
Q&A : 25 Real Questions

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IBM IBM SPSS Modeler -

Open supply and IBM SPSS Modeler | killexams.com Real Questions and Pass4sure dumps

A a hit statistics-driven company has to provide the appropriate equipment for its records analysts, developers, and enterprise conclusion clients. increasingly, this capacity leveraging open-supply utility. The R or Python programming languages, along with the Apache Spark and Hadoop statistics frameworks, are favored open-supply tools for many modern analysts and builders. but for all their benefits, established open-source information programs additionally include doubtlessly time-drinking drawbacks.

IBM Analyzes Social community Sentiment With SPSS Modeler improve | killexams.com Real Questions and Pass4sure dumps

IBM_logo_2009.jpg IBM (information, web site) has just released an upgrade to the SPSS predictive analytics device with a purpose to enable clients to analyze text gathered from social media sources, including Twitter, facebook, blogs, wikis and RSS feeds.

The upgrade represents a considerable increase in the SPSS Modeler data mining and text analytics answer with advantage that stretch so far as detecting the context of the words used. facts from the social web can also be merged with interior statistics to create much more accurate intelligence about buyers, IBM says.

With its new potential to consider emoticons, slang and other social media terminology, as well as jargon from selected industries, it includes taxonomies from a hundred and eighty verticals ranging from existence Sciences to monetary, assurance and consumer Electronics. IBM SPSS Modeler 14 top class version is designed to analyze any tips that offers businesses insights into how their products are being bought.


here's the first major upgrade to Modeler due to the fact IBM purchased out predictive analytics enterprise SPSS for US$ 1.2 billion closing October.

on the time IBM talked about the acquisition would extra extend its counsel on Demand (IOD) application portfolio and company analytics capabilities. Forrester is interpreting the flow as the first salvo in a fight for supremacy within the business intelligence market.

SPSS Modeler 14

And with this upgrade, IBM has introduced to its social and enterprise analytics capabilities and really upped the ante for rivals. Modeler 14 premium edition, by using inspecting sentiment in social media, can predict what buyers are interested in, the place they will go, and what practices will hold them fascinated.

SPSS_workbench.jpgIBM SPSS Modeler 14 top rate version workbench 

It comes with new semantic networks and greater than four hundred,000 terms throughout most verticals in addition to one hundred,000 synonyms and heaps of manufacturers enabling users to link sentiment and products with no need to construct up their own definitions inside those 180 taxonomies.

SPSS_Add text.jpg

during this appreciate, IBM cites the banking business for instance the place the semantic network protecting it might distinguish between a whole lot of the terminology governing personal loan lending. It is aware of, as an example, that a “floating fee” is a similar as a mortgage mortgage and that variable and adjustable loan rates are simply the same issue.

business intelligent selections

The bottom line right here is more advantageous business resolution making. the use of natural Language Processing it takes key ideas, opinions and expressed sentiment from the web, including survey information and textual content and integrates that data into models designed to foretell customer reaction as well as recommending more suitable company choices.

through placing this along side the structured records that already exists within the business, users can uncover relationships between ideas and client actions with a view to build superior figuring out of consumer organizations and the way they're reacting in a selected channel.

IBM BI enlargement

And as to why IBM went down this route in the first vicinity, at the time of the SPSS acquisition Forrester (information, website) cited three explanations:

  • endured boom of its suggestions On Demand drive -- its smallest business enviornment -- that has considered it purchase out Ascential utility, FileNet and Cognos. IBM’s vision of a better Planet combines instrumentation, interconnectivity and intelligence. with out SPSS, Forrester says, IBM would now not be in a position to convey on this.
  • Strategic stream to counteract the acquisition of solar Microsystems by way of Oracle and put itself ahead of the other large guns at a services degree.
  • Improves market placement by placing itself in a far better position than any other supplier to carry built-in smart options in the future, from actual world attention to resolution help equipment.
  • The latest edition of IBM SPSS Modeler facts mining and textual content analytics workbench is now purchasable international. text analytics workbench is barely available in IBM SPSS Modeler premium edition. 

    MOL to make use of IBM SPSS Modeler for safety evaluation | killexams.com Real Questions and Pass4sure dumps

     Mitsui O.S.okay. lines and its thoroughly-owned consolidated subsidiary MOL tips methods, (MOLIS) to birth multi-dimensional evaluation of the causes for incidents and complications on its operated vessels, the use of IBM's statistical evaluation software, "IBM SPSS Modeler".

    IBM SPSS Modeler is an superior statistics analysis utility that offers potential evaluation from mass volume of information and supports better resolution making to resolve business issues.

    The MOL community has conventionally aggregated incidents and complications facts stated via its operated vessels to "visualize" protected operation. And any longer, the group will increase greater helpful measures to keep away from incidents and determine the results with the aid of examining correlations and causal relationship of statistics from dissimilar sources (as an instance, operation information, crewmember data, vessel inspection statistics, and so forth).

     additionally, it'll construct a brand new evaluation method using the text mining characteristic, for some aspects of unstructured statistics, similar to close misses gathered from crewmembers.

    previous to this evaluation, the community held a 3-month trial starting in July 2017 and constructed evaluation fashions that investigate causal relationship of suggestions on crewmembers, such as downtime complications and years of onboard event.

    The MOL community continually makes use of and applies ICT know-how in a proactive method, with the intention making certain safe, stable cargo transport and fitting the area chief in protected operation.

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    In today's hyper-fast cloud computing era, machine learning solutions drive exponential progress in improving systems. Machine learning's ability to leverage Big Data analytics and identify patterns offers critical competitive advantage to today's businesses.

    Often used in combination with artificial intelligence and deep learning, machine learning uses sophisticated statistical modeling. These complex systems may reside in private cloud or public cloud. In any case, the passage of time boosts machine learning: as more data is added to a task and analyzed over time, ML produces more accurate the results.

    The global machine learning market totaled $1.4 billion in 2017, according to BCC Research. It is estimated to top $8.8 billion by 2022, a jaw-dropping compound annual growth rate (CAGR) of 43.6 percent.

    When considering which machine learning vendor to select, weigh these factors:

  • Major platform or stand alone vendor: some of the ML vendors below offer a full cloud computing platform, others are stand alone firms dedicated to ML. There's no hard and fast rule, but the stand alone firms may be more incentivized to bid competitively for your business.
  • Type of machine learning: Do you want to implement ML for retail? For healthcare? Ask your potential vendor what sectors they have invested in the most heavily.
  • Vendor's future direction: Where directions, in terms of R&D and investment, will this vendor focus on in the years ahead? That answer to that question might help you find the best match for your long term goals.
  • https://o1.qnsr.com/log/p.gif?;n=203;c=204660771;s=9478;x=7936;f=201812281332180;u=j;z=TIMESTAMP;a=20403946;e=i

    The machine learning landscape is changing rapidly. Machine learning start-ups are constantly jumping into the space. Established vendors are introducing a variety of offerings that use machine learning in one form or another. Sorting through the choices and options can prove confusing. We’ve identified eight top vendors in the machine learning space, based on the features they offer, analyst reviews, customer testimonials and independent research.

    Jump to:


    Alteryx offers a unified platform that addresses data, analytics and machine learning. The vendor strives to minimize the need for data scientists by creating new and improved partnerships among IT, analytics experts and lines of business through a collaborative approach.

    The product is available in both a code-free and code-friendly format. It uses a drag-and-drop workflow interface and ties into third party demographic, firmographic and spatial data. The company is rated as a “Leader” on the Gartner Magic Quadrant (MQ). Alteryx is moving toward powerful automation and rule-based recommendations, Gartner noted.

    Alteryx offers integration with numerous major partners, including Tableau, AWS, Teradata, Microsoft, DataRobot, Salesforce, Oracle, Cloudera and Qlik. Machine learning functions feature parallel model analysis with predictive analytics, along with the ability to automate workflows and various processes.

    Read our in-depth review of AlteryxSee user reviews of Alteryx

    AWS SageMaker

    The fully managed machine learning service allows data scientists and developers to build and train machine learning models and insert them into applications. This makes it possible to run parallel models, conduct flexible distributed training, and tackle A-B testing along with other tasks.

    SageMaker is a fully managed AWS service that includes an integrated Jupyter authoring notebook instance. This aids in exploration and analysis by offering deep visibility into data sources. SageMaker offers a set of algorithms optimized for the environment and it supports proprietary algorithms and custom training scripts.

    Sagemaker also features integration with Docker containers and Apache Spark. Together with AWS, it’s possible to use the integrated SageMaker console to launch and scale projects quickly. The platform supports HIPAA and other compliance standards.

    Read our in-depth review of SageMakerSee user reviews of SageMaker

    Google Machine Learning Engine

    Google Cloud Machine Learning (ML) Engine is a fully managed platform designed for developers and data scientists. The product allows users to construct high quality models using multiple machine learning frameworks, including scikit-learn, XGBoost, Keras, and TensorFlow.

    The latter is a sophisticated deep learning framework that powers many Google products, from Google Photos to Google Cloud Speech. It allows users to deploy machine learning into production without the need for Docker containers. The highly scalable environment—which accommodates training on managed clusters, delivers a framework for automatically designing and evaluating model architectures. It is designed to accommodate existing workflows, and it scales to handle batch prediction and online prediction requirements.

    Other key features include: automatic resource provisioning, the use of portable models, server-side processing and integrated cloud data storage.

    Read our in-depth review of Google Machine Learning EngineSee user reviews of Google Machine Learning Engine

    IBM Watson Studio

    IBM was a pioneer in the artificial intelligence (AI) and machine learning spaces. Today, Forrester ranks the company as a “Leader” among multimodal predictive analytics and machine learning solutions providers.

    IBM Watson Studio is designed to accommodate a variety of independent platforms and various types of power users. This includes data engineers, app developers and data scientists. The result is strong collaboration capabilities. Among its top features: a robust architecture, strong algorithms and a powerful ability to execute machine learning.

    The Watson Studio is part of an extensive collection of technologies and solutions that are available under the IBM Watson umbrella. It is designed to accommodate IBM’s SPSS modeling workflows but also plug in open source machine learning libraries and notebook-based interfaces. The solution is available in the cloud on or on the desktop. It incorporates visual drag-and-drop tools.

    Read our in-depth review of IBM Watson StudioSee user reviews of IBM Watson Studio

    Microsoft Azure Machine Learning Studio

    Azure Machine Learning Studio has emerged as a leading solution in the managed cloud space. It delivers a visual tool that aids developers, data scientists and non-data scientists in designing machine learning pipelines and solutions that address a wide variety of tasks. The platform offers a browser-based, visual drag-and-drop authoring environment that requires no coding. Gartner ranks Microsoft a “Visionary” in its MQ. The solution offers a high level of flexibly, extensibility and openness.

    Azure ML Studio also delivers comprehensive capabilities across the full range of descriptive, diagnostic, predictive and prescriptive analytic types. Microsoft is continuing to expand the features and capabilities within Azure Machine Learning. This includes promoting deep learning through the Microsoft Cognitive Toolkit (formerly CNTK) as well as the joint ONNX open standard for neural networks. Microsoft also plans to automate a growing number of functions within the Azure Machine Learning platform.

    Read our in-depth review of Microsoft Azure Machine Learning StudioSee user reviews of Microsoft Azure Machine Learning Studio


    RapidMiner offers data prep, machine learning and model deployment tools that deliver deep insight into data. It delivers a platform that is designed to tackle a variety of big data and analytics requirements.

    In the machine learning space, the company has established itself as a heavyweight. It delivers a simple-to-use visual environment that incorporates automated model creation along with suggestions, guides and information about what approach might work best for a given situation. RapidMiner’s features and high level of flexibility have established it as a popular choice among machine learning solutions.

    Data scientists and non-data scientists alike can easily inspect and customize projects and build on results. RapidMiner is ranked as a “Leader” in both the Gartner MQ and the Forrester Wave. It is among the highest rated data and machine learning solutions at Gartner Peer Insights.

    Read our in-depth review of RapidMinerSee user reviews of RapidMiner

    SAP Leonardo

    Leonardo serves as an umbrella for a variety of SAP data products and services. It incorporates AI and machine learning through a cloud platform. This includes a machine learning foundation that allows developers, data scientists and others to create, run and deploy algorithms and machine learning apps.

    The technology extracts data from text, images, natural language and other media to generate computer-based predictions. The highly scalable platform includes predefined and pretrained machine learning services, but it also incorporates TensorFlow models. This allows users to adopt a flexible framework to training and tuning machine learning. Forrester ranks SAP as a “strong performer” in the overall predictive analytics and machine learning category and cites the breadth and depth of service as a strong point.

    However, Gartner rates the company a “Niche Player” in its MQ. It noted that SAP has made considerable strides in the data and machine learning spaces but still lags behind others in terms of features and capabilities.

    Read our in-depth review of SAP LeonardoSee user reviews of SAP Leonardo

    SAS Visual Data Mining and Machine Learning

    A pioneer in the data mining and analytics spaces, SAS has established itself as a top choice among machine learning vendors. It offers Visual Data Mining and Machine Learning (VDMML), which ranked at the standout “Leader” in the Forrester Wave and as a top “Leader” in the Gartner MQ, though the research firm notes that the overall platform has lost some ground in terms of vision and ability to execute.

    VDMML connects to numerous other products and data sources. Forrester noted that SAS has emerged as the first truly multimodal PAML solution. It uses a wizard to automatically train a model, convert it to code and plug in integrated features for automated machine learning. The unified environment also includes robust visual tools for creating models as well as deep neural networks.

    In addition, data scientists can embed snippets of code directly into SAS programs to further enhance capabilities, including machine learning and deep learning output.

    Read our in-depth review of SAS Visual Data Mining and Machine LearningSee user reviews of SAS Visual Data Mining and Machine Learning

    Honorable Mentions

    In addition to our top picks, there are other promising tools out there.


    Splunk Enterprise provides a broad based platform that can be used for searching, monitoring, and analyzing data. The software can import data from a variety of sources, from logs to Big Data sources.

    Data Science and Machine Learning Vendors at a Glance:

    Vendor and features


    AWS SageMaker

    Google Machine Learning Engine

    IBM Watson Studio

    Microsoft Azure Machine Learning


    SAP Leonardo

    SAS Visual Data Mining and Machine Learning

    ML Focus

    Analytics and ML platform that allows users to build models in a single workflow

    Hosted platform that supports supervised and unsupervised ML and deep learning.

    Hosted platform that runs ML training jobs and predictions at scale.

    Broad data science focus with cloud and desktop ML platforms.

    Automated ML platform running on hosted Azure Cloud.

    Highly automated ML platform suited for firms aiming to use machine learning broadly.

    Broad and deep data science and ML platforms.

    Multimodal PAML solution with powerful wizards to support data mining and ML. Strong automation.

    Key Features and capabilities

    Offers more than 250 code free pre-built tools and supports open source tools and scripting.

    Zero-setup integrated Jupyter authoring notebook instance. Access to rich array of open source frameworks and tools.

    Automatic resource provisioning and monitoring for CPUs, GPUs and TPUs. Strong open source support for tools and scripting languages.

    Strong visual recognition and natural classification tools. Flexible approach that incorporates open source tools. Connects to IBM SPSS Modeler.

    Strong descriptive, diagnostic, predictive and prescriptive analytics tools and capabilities. Strong support for open source ML tools and scripting.

    Offers more than 1,500 machine learning and data prep functions, and it supports more than 40 files types. Connects to Amazon S3 and Dropbox.

    Fully supports a BYOM approach. Strong open source integration. Runs in SAP’s HANA public cloud.

    Includes best practice templates and in-memory processing that supports supervised and unsupervised learning.

    User comments

    High ratings for simplicity and usability. Complaints about the lack of robust reporting and visualization features.

    High marks for the interface, configurability and usability. However, some say that the familiarity with AWS is crucial.

    High ratings for functionality and integration.

    Highly rated for features and capabilities. Some complaints revolving around using notebooks.

    High ratings at Gartner Peer Review. Some complaints about support.

    Among the highest rated data science and ML solutions. Users describe it as powerful and “revolutionary” though there are complaints about the lack of GPU support. 

    Customers give SAP high marks for usability and performance. Some complain about the lack of visualization tools for external data.

    High ratings. Users like the powerful features and capabilities built into the platform, though some remark that there is a substantial learning curve associated with the product.

    Pricing and licensing

    Tiered system based on products used. Ranges from $5,195 to $33,800 per seat annually.

    Complex tiered pricing model based on ML notebook instances and cloud resources consumed.

    Based on resources and training hours used. Ranges from 1 cent per hour to $31 per hour for various services.

    Tiered model from $99 per month per user to $6,000 per month per user or more at enterprise level.

    Tiered pricing based on users and usage. Ranges from near zero to tens of thousands per user annually.

    Tiered pricing ranging from $2,500 per user per year to upwards of $10,000 per user per year.

    Tiered model based on node hours consumed in the cloud. Billing blocks are charge in increments of €7.25.


    A Pure Play On Self-Service Big Data Prep And Analytics: Wait For Smarter Valuation Entry Point | killexams.com real questions and Pass4sure dumps

    No result found, try new keyword!AYX data by YCharts Business value derived from ... open source statistical programming language R (which is supported by Alteryx), IBM SPSS Statistics, the SAS programming language, Microsoft Excel ...

    They’ve Mastered the Science of Business Analytics | killexams.com real questions and Pass4sure dumps

    Dell wanted to analyze 2 million records from its supply chain to maximize computer sales efficiency. Enterprise Bank wanted to know which mobile banking services its customers use and why. MFS Investments wanted a better understanding of its project management database to predict timelines and completion dates.

    Those were among the eight industry-partner capstone projects that the first cohort of Master of Science in Business Analytics (MSBA) students completed before graduating from the Manning School of Business this spring. Eleven students earned the school’s first MSBA degrees in May, while seven more are expected to complete the program this summer.

    “Being in the first cohort was a great experience,” says Neha Ajgaonkar, who enrolled in the program after earning her bachelor’s in management studies and master’s in commerce in her home country of India. She says the MSBA program gave her valuable hands-on experience with scripting languages such as R, Python and SQL and software like IBM SPSS Modeler, Weka and RapidMiner.

    MSBA grad student Tri Mai presents his capstone project with industry partner MFS Investments. “Coming from a management background, it feels good to know that I can now use these tools to solve analytical problems,” says Ajgaonkar, who received the Outstanding MSBA Student award at the Manning School’s graduate Hooding Ceremony. She says her MSBA degree will broaden her career options in a rapidly growing field: “Analytics is in such demand in every industry.” To meet this growing demand, the Manning School launched the MSBA program in 2016. The 30-credit program, under the direction of coordinator Tom Sloan, has six core courses covering data mining, statistics, database management and decision analytics. Students choose three electives from one of four tracks: Big Data analytics, managerial decision making, marketing analytics and finance analytics. Members of the first MSBA cohort pose with Dean Sandy Richtermeyer and Program Coordinator Tom Sloan during the graduate Hooding Ceremony.

    The program’s second cohort drew 20 students in 2017, and Motiwalla expects that number to climb this fall.

    Students worked in teams of two or three on the capstone projects, which also included industry partners Digital Federal Credit Union (DCU), Semcasting, Home Health Foundation and an area software company that wished to remain anonymous. Another team of students worked with the UMass Lowell hockey team to predict player performance.

    “This is an outstanding example of experiential learning for our students and relationship-building with the business community,” says Manning School Dean Sandy Richtermeyer.

    Students Jonathan Wolfe and Cristian Joia built a household income prediction model for Semcasting, a North Andover company that provides marketers with data and audience targeting solutions. Using a data set of 4.7 million records, the students’ model looks at regional differences in income levels to predict the incomes in households targeted by Semcasting marketing campaigns.

    “It was nice to use software in actual situations — this is a company that is dealing with a problem right now — versus working with a dataset that nobody is going to look at down the road,” says Joia, who is completing his MSBA through the Plus 1 Program after earning his bachelor’s in business (with a concentration in finance) in 2017. MSBA students Cristian Joia, left, and Jonathan Wolfe discuss their Semcasting capstone project with an industry guest.

    “We were able to take everything we learned the past few semesters, like using R, writing code and using regression models, and apply them in one project,” adds Wolfe, who earned his bachelor’s in business (with a concentration in marketing) from the Manning School in 2016. 

    Brian Collins, executive vice president and director of operations at Enterprise Bank, was impressed by the work of students Christine Vaudo and Alex Twogood, who used descriptive and prescriptive analytics to look at customers’ mobile banking behaviors.

    “We gave them all the data and wanted to see what they’re seeing,” Collins says. “We expected them to come up with some things we haven’t seen, and they did a good job.”

    David Araujo, senior vice president of technology and innovation at DCU, worked with students Hsinyi Shen, Ijne Huang and Yufei Guo on a project to help protect against credit card fraud. He was “blown away” by the students’ confidence and poise as they presented their capstone.

    “It shows that they’re prepared for the real world,” says Araujo, who interviewed several Manning School students interested in data analytics for summer internships before the capstone presentations. 

    Buoyed by the industry partnerships, Motiwalla’s next goal is to create a center for business analytics at the Manning School that will provide students with the computing power needed to analyze ever-increasing amounts of Big Data. The center could also provide industry partners with access to students looking to take on more real-world projects.

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    IBM IBMSPSSMBPDM Exam (IBM SPSS Modeler - Business(R) Partner Data Mining Associate) Detailed Information


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