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C2020-013 exam Dumps Source : IBM SPSS Modeler Data Mining for Business Partners v2
Test Code : C2020-013
Test Name : IBM SPSS Modeler Data Mining for Business Partners v2
Vendor Name : IBM
Q&A : 25 Real Questions
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IBM IBM SPSS Modeler Data
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See the entire checklist of laptop getting to know options
Watson is an umbrella for all IBM deep studying and synthetic intelligence, in addition to computer getting to know. The company changed into a pioneer in introducing AI technologies to the business world. What this capacity for buyers: Watson Studio is a accurate contender for any organization looking to set up laptop getting to know and deep learning technologies.
The platform provides extensive equipment and technologies for information scientists, builders and field matter experts that want to explore records, build fashions, and instruct and installation machine researching fashions at scale. The solution includes tools to share visualizations and effects with others. Watson Studio supports cloud, computer and native deployment frameworks.
The latter resides behind a company’s firewall or as a SaaS solution running in an IBM private cloud. IBM Watson Studio is ranked as a “chief” within the Forrester Wave. It become a customers’ alternative 2018 recipient at Gartner Peer Insights.
Watson Studio depends on a set of IBM equipment and technologies to construct effective laptop getting to know functions and features. This contains IBM Cloud pretrained laptop getting to know fashions akin to visual focus, Watson natural Language Classifier, and others. The ambiance makes use of Jupyter Notebooks together with other open supply tools and scripting languages to enrich built-in collaborative mission points.
The result is an atmosphere that allows quick and strong computer studying construction and satisfactory tuning of fashions. data scientists and others can choose between a variety of capacities of Anaconda, Spark and GPU environments.
Watson Studio supports stronger visual modeling via a drag-and-drop interface offered via IBM’s SPSS Modeler. moreover, it contains automated deep getting to know the usage of a drag-and-drop, no-code interface in Neural community Modeler.
Overview and contours
records scientists, developers and discipline matter specialists.
Graphical drag-and-drop and command line.
Scripting Languages/codecs Supported
supports Anaconda and Apache Spark. The latter presents Scala, Python and R interfaces.
Most fundamental information and file codecs are supported via open supply Jupyter Notebooks.
IBM Watson Studio connects several IBM items, together with SPSS Modeler and statistics Science adventure (DSX) together with open source equipment, in an effort to bring a sturdy Predictive Analytics and computing device learning (PAML) solution.
The ambiance incorporates open records sets through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud version facets interactivity with laptop servers and R Studio, along with Python, R., and Scala coder for records scientists.
Reporting and Visualization
Visualization through SPSS Modeler. effective logging and reporting features are constructed into the product.
IBM has adopted a pay-as-you-go mannequin. Watson Studio Cloud – common charges $99 per thirty days with 50 ability unit hours per 30 days blanketed. Watson Studio Cloud - enterprise runs $6,000 per thirty days with 5,000 means unit hours. Watson Studio computer costs $199 per month with unlimited modeling. Watson Studio local – for enterprise records science groups N/A.
IBM Watson Studio Overview and lines at a look:
supplier and lines
IBM Watson Studio
extensive statistics science focus with cloud and computing device ML platforms.
Key aspects and capabilities
robust visual focus and herbal classification tools. bendy strategy that accommodates open source equipment. Connects to IBM SPSS Modeler.
highly rated for points and capabilities. Some complaints revolving round the use of notebooks.
Pricing and licensing
Tiered model from $ninety nine monthly per user to $6,000 per month per person or greater at business stage.
IBM tops the list of companies within the predictive analytics application space in response to a quadrant lately released via 360Quadrants, followed closely by means of Oracle and SAP SE. IBM’s SPSS Modeler was rated the most excellent in product fine, reliability, and breadth and depth of providing. IBM SPSS Modeler additionally captured the precise spot in criteria like geographic footprint and viability of company.
360Quadrants defines predictive analytics as a statistical and information mining solution consisting of numerous algorithms and methodologies used for structured as well as unstructured facts to extract company insights.
360Quadrants covers 50+ items within the predictive analytics area and areas the correct 31 of them in a quadrant depending on their exceptional, reliability, and enterprise outcomes. These 31 items are labeled into Visionary Leaders, Dynamic Differentiators, rising agencies, and Innovators.
360Quadrants recognizes IBM Corp, SAS Institute, Inc., SAP SE, reasonable Issac organization (FICO), Tableau software, Inc., RapidMiner, Inc., Oracle Corp, and Angoss software Corp as Visionary Leaders; TIBCO software, Inc., Microstrategy, Inc., Alteryx, Inc., advice Builder, Dataiku, KNIME.COM AG, and NTT information organisation, as Innovators; GoodData corporation, Microsoft supplier, Teradata business enterprise, Sisense, Inc., Predixion utility, and Domino statistics Lab, Inc. as Dynamic Differentiators; and Exago, integrated, AgilOne, QlikTech overseas, and Kognitio Ltd. as emerging avid gamers.
IBM's SPSS predictive analytics equipment include IBM SPSS Modeler and IBM SPSS information. SPSS Modeler gives records mining and text analysis utility, while SPSS information is an integrated household of items. each equipment permit clients to build predictive models and execute different analytics projects.
The IBM SPSS Modeler goals clients who've little or no programming advantage. clients are supplied with a drag-and-drop person interface, enabling them to build predictive fashions and operate different records analytics. Modeler can follow distinctive tactics and algorithms to support the person find suggestions hidden in the statistics. The device can additionally aid in integrating and consolidating every kind of records units from dispersed facts sources across the company.
The IBM SPSS statistics suite is an built-in set of items geared towards greater knowledgeable facts analysts. SPSS information addresses the finished analytical technique, from planning to information assortment, evaluation, reporting and deployment.
IBM SPSS Modeler points
version 18 offers here points:
greater than 30 base laptop gaining knowledge of algorithms.
Extensions that give continued improvements for use with open source products, such as R and Python.
better support for a number of multithreaded analytical algorithms, including Random trees, Tree-AS, Generalized Linear Engine, Linear-AS, Linear support Vector computer and Two-Step-AS clustering.
The means to run a lot of Python and Spark desktop gaining knowledge of, as well as other Python analytics libraries natively in Modeler without requiring the use of the Analytic Server, as changed into required in the outdated edition.
SPSS Modeler bundles are deployed on premises, and SPSS Modeler Gold is obtainable as a cloud offering. The customer front end of SPSS Modeler runs under windows and macOS, whereas the server element runs on Unix, Linux and home windows.
IBM SPSS Modeler offers here variations:
SPSS Modeler own: A single-user computing device product.
SPSS Modeler knowledgeable: A computer product that works with IBM SPSS Analytic Server, presenting enhanced scalability and performance and enabling purposes for use throughout a company.
SPSS Modeler top class: This version contains advanced algorithms and capabilities, similar to text analytics, entity analytics and social community analysis, that increase mannequin accuracy with unstructured facts.
SPSS Modeler Gold: This edition gives analytical determination administration, collaboration and deployment capabilities. SPSS Modeler Gold is also available as a cloud providing.
IBM SPSS facts elements
SPSS records edition 24 contains right here new elements:
The potential to entry greater than one hundred extensions, enabling users to take expertise of free libraries written in R, Python and SPSS syntax.
The IBM SPSS Extension Hub to browse, download, update, get rid of and generally manipulate extensions.
a tremendous upgrade to the customized Dialog Builder, enabling users to greater conveniently construct and installation their personal extensions. Enhancements consist of new controls and new homes for present controls and several other advancements to the person interface.
improvements that allow users to more without problems and right away import and export statistics into SPPS statistics.
advancements to the customized Tables module, including new statistical functionality and consumer-requested points.
IBM SPSS data presents here three variations (each with extra modules):
SPSS statistics ordinary tools give advanced statistical techniques that support linear and nonlinear statistical models, in addition to predictive simulation modeling, which bills for doubtful inputs, geospatial analytics and customized tables.
SPSS records skilled tools aid statistics coaching, missing values and statistics validity, decision bushes, and forecasting.
SPSS facts top rate provides superior analytical strategies, together with structural equation modeling, in-depth sampling evaluation and testing. This bundle additionally comprises approaches that goal direct advertising and excessive-end charts and graphs.
Pricing for the SPSS Modeler and SPSS statistics predictive analytics tools range depending on the bundle options, the number of clients and the license duration. SPSS data is now available as a subscription option or a perpetual license. IBM presents free trials of both IBM SPSS Modeler and IBM SPSS facts.
Why the time period unstructured information is a misnomer
How big statistics is changing facts modeling techniques
large facts techniques pose new challenges to facts governance
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Download the authoritative guide: Cloud Computing 2019: Using the Cloud for Competitive Advantage
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.
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.
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
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
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
Data Science and Machine Learning Vendors at a Glance:
Vendor and features
Google Machine Learning Engine
IBM Watson Studio
Microsoft Azure Machine Learning
SAS Visual Data Mining and Machine Learning
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.
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.
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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.
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.
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.
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.
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.
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