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SPS-201 IBM SPSS MBPDA - IBM SPSS Modeler Business

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SPS-201 exam Dumps Source : IBM SPSS MBPDA - IBM SPSS Modeler Business

Test Code : SPS-201
Test Name : IBM SPSS MBPDA - IBM SPSS Modeler Business
Vendor Name : IBM
: 25 Real Questions

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IBM IBM SPSS MBPDA -

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

IBM tops the record of providers in the predictive analytics software house in accordance with a quadrant recently launched by 360Quadrants, followed intently by way of Oracle and SAP SE. IBM’s SPSS Modeler became rated the most suitable in product best, reliability, and breadth and depth of providing. IBM SPSS Modeler also captured the proper spot in standards like geographic footprint and viability of company.

360Quadrants defines predictive analytics as a statistical and facts mining answer which includes a large number of algorithms and methodologies used for structured as well as unstructured facts to extract company insights. 

360Quadrants covers 50+ products within the predictive analytics space and areas the correct 31 of them in a quadrant counting on their first-class, reliability, and company influence. These 31 items are classified into Visionary Leaders, Dynamic Differentiators, rising companies, and Innovators.

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

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

IBM nowadays introduced that it's enhancing a number of of its latest software items with the Apache Spark open-source information processing engine, together with the SPSS predictive analytics utility.

IBM purchased SPSS for $1.2 billion in 2009. SPSS itself began at Stanford institution in 1968 and is conventional in information courses at universities.

IBM is mainly incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman informed VentureBeat in an email.

Spark is also being utilized to IBM’s BigInsights, Streams, and DataWorks software, based on an announcement.

The Sparkification of IBM comes after huge Blue introduced an important dedication to the open-supply utility in June. on the time, IBM introduced Spark as a service on its Bluemix cloud; that carrier is now frequently available.

IBM has made more than 60 contributions to Spark given that the June announcement, based on the statement.


Monster Merger: IBM Buys SPSS For Approx. $1.2 Billion In cash Deal | killexams.com Real Questions and Pass4sure dumps

IBM is buying analytics application and options provider SPSS in an all money transaction at a cost of $50/share – a forty two % top rate to Monday’s closing fee of $35.09 on Nasdaq – resulting in a complete money consideration in the merger of approximately $1.2 billion. The acquisition is subject to SPSS shareholder approval, regulatory clearances and different closing situations, and is anticipated to shut later in the 2d half of 2009.

big Blue noted the acquisition of the publicly-held Chicago business turned into expected to beef up its advice-agenda initiative, which helps businesses take guidance and turn it into a strategic asset. IBM shares fell 67 cent to $116.ninety six in pre-market trading, while SPSS shares jumped 41 percent to $forty nine.fifty nine.

A message from SPSS Chairman, CEO & President Jack Noonan:

We and IBM view this as a tremendously-complementary circulate from both a know-how and a market place point of view.

We’re excited about this contract and the tremendous possibilities that SPSS and IBM can obtain collectively in continuing to steer an trade that they helped form. The depth and breadth of IBM’s components, its consumer and market reach can only boost their capacity to tackle the growing market for Predictive Analytics.

In joining with IBM, they are able to strengthen Predictive Analytics as a competitive potential for agencies and companies international. They see this as a transformative event with the intention to speed up the adoption of Predictive Analytics.

Between now and the acquisition shut, they can proceed to support their shoppers and companions in commonplace trend. existing contracts will remain in effect, account relationships and aid infrastructure will remain unchanged.

Predictive analytics software captures and analyzes information about individuals’s attributes, attitudes and behaviors to profit a full figuring out of expected future behaviors, so organizations can make smarter decisions for more desirable company effects.

IBM says it's going to continue to support and boost SPSS applied sciences whereas allowing shoppers to take abilities of its personal product portfolio. SPSS will turn into a part of the guidance administration division inside the utility community business unit, led through Ambuj Goyal, popular supervisor, IBM assistance management.

replace – In different information: IBM additionally received Ounce Labs, a company of commercial enterprise supply code protection checking out and intends to combine it into its Rational utility company. economic terms had been no longer disclosed.


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IBM SPSS MBPDA - IBM SPSS Modeler Business

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Comparing the leading big data analytics software options | killexams.com real questions and Pass4sure dumps

There are many vendors selling products classified as big data analytics software. However, it's challenging to...

differentiate these products based on functionality alone, as many of the tools share similar features and capabilities. Additionally, some of the tools exhibit extremely subtle differences.

That being said, your key differentiating factors will likely focus on balancing ease of use, algorithmic sophistication and price in relation to your organization's capability and level of maturity in analytics.

In this article, they examine products from nine big data analytics software vendors: Alteryx Inc., IBM, KNIME AG, Microsoft, Oracle, RapidMiner Inc., SAP, SAS Institute Inc. and Teradata Corp. Some of these vendors provide more than one tool. See the "Leading vendors of big data analytics software" sidebar below for more details about their specific product offerings.

These vendors represent different facets of the big data analytics market. Let's compare and contrast the ways that these products meet the business needs of user organizations.

Analyst expertise and skills 

Some data analytics tools are targeted to novice users, some are targeted to expert data analysts and some are engineered to appeal to both types of users.

Products such as IBM SPSS Modeler, RapidMiner's tools, Oracle Advanced Analytics and the Automated Analytics version of SAP BusinessObjects Predictive Analytics are generally designed to enable users with a limited background in statistics or data analysis to analyze data, develop analytical models and design analytics workflows with little or no coding.

While each vendor wraps its core analytics components with an intuitive user interface to guide the analyst's progress in data preparation, analysis, and then model design and validation, the approach taken may differ, especially when comparing a stand-alone product, such as RapidMiner, with one that's a component of a larger suite, such as the Oracle product.

Tools such as IBM SPSS Statistics, KNIME Analytics Platform, the Expert Analytics module of SAP BusinessObjects Predictive Analytics, Microsoft R and the Teradata Aster Analytics platform provide the more sophisticated functionality that expert users expect. Oracle R Advanced Analytics for Hadoop (ORAAH), one of the components in the Oracle Big Data Software Connectors suite, provides an R interface for manipulating Hadoop Distributed File System data and writing mapper and reducer functions in R. This flexibility may be appealing to more advanced data scientists.

Alteryx and SAS Enterprise Miner offer functionality adapted to the user's level of expertise, and essentially fall into both categories. Alteryx has added improvements to data profiling to help data scientists better understand their data sources. Overall, SAS Enterprise Miner and IBM's SPSS tools stand out when it comes to supporting more advanced analytical techniques and model scoring, as well as a broader array of analysis functions, including neural networks, association analysis and visualization capabilities.

Analytical diversity

Depending on the use case and application, your organization's users will be required to support different types of analytics capabilities that will use specific types of modeling, such as regression, clustering, segmentation, behavior modeling and decision trees.

While this has resulted in broad support for the various forms of analytical modeling at a high level, some vendors have invested decades of work into tweaking different versions of their algorithms and adding more sophisticated functionality. It's important to understand which models are most relevant to your business problems and to evaluate the products in terms of how they best serve your users' business needs.

It's important to understand which models are most relevant to your business problems and to evaluate the products in terms of how they best serve your users' business needs.

The more mature and higher-end -- and, accordingly, higher-priced -- tools will exhibit the greatest analytical breadth. Oracle Data Miner includes an array of well-known machine learning approaches to support clustering, predictive mining and text mining. Both editions of IBM's SPSS product provide a diverse set of analytical techniques and models. And SAS Enterprise Miner supports many algorithms and techniques, including decision trees, time series, neural networks, linear and logistic regression, sequence and web path analysis, market basket analysis, and link analysis.

The newer generation -- and, in some cases, lower-priced -- products support different models, but perhaps with a narrower range of algorithmic sophistication.

The model inventory in Alteryx Analytics Gallery includes such capabilities as regression analysis, decision trees, association rule analysis, classification and time series analysis. KNIME includes methods for text mining, image mining and time series analysis, and also integrates machine learning algorithms from other open source projects, such as Weka and JFreeChart.

Another aspect of analytical diversity is integration with programming languages and statistical tools, such as R, for incorporating existing libraries, as well as user-defined functionality. In fact, integration with R could be considered an increasingly critical differentiator.

Alteryx Designer, Microsoft R, SAS Enterprise Miner, Teradata Aster Analytics, Oracle's ORAAH and KNIME's Analytics Platform all interface and support integration with R. Several of the vendors, including IBM, Oracle, Microsoft, RapidMiner and SAP, provide a growing library of extensions to R and Python, enabling users to take advantage of free libraries.

Scope of the data to be analyzed

There are multiple facets of the scope of the data to be analyzed, including the issue of structured vs. unstructured information, as well as access to conventional on-premises databases and data warehouses, cloud-based data sources, and data managed in big data platforms, such as Hadoop.

However, there are varying degrees of support for data managed within less-conventional data lakes -- either managed within Hadoop or in another NoSQL data management system intended to provide horizontal scaling. The factors for distinguishing among the products must be based on your organization's specific requirements for accessing and processing data volumes and data variety.

In recognition of the growing diversity of input sources and the variety of underlying systems used to house those data sets, another set of emerging features that is being adopted by these vendors involves data accessibility. IBM, RapidMiner, Alteryx, Oracle and Microsoft have all improved their tools' data import, export and connectivity capabilities. These enhancements should enable users to access a more comprehensive list of data sources while simplifying and speeding up the process of loading data into the products.

Support for scalability and high performance

The need for scalable performance is driven by your organization's data volumes and appetite for analysis. Smaller organizations with less data may be able to tolerate products that don't have performance characteristics that scale with the available resources, such as the entry-level versions of the lower-end tools, including RapidMiner, KNIME, Microsoft R Open and Alteryx Designer, which can run on desktop systems and don't require additional server components.

Larger organizations are more likely to have a greater inventory of data sets to analyze, as well as broader communities of users. This introduces two additional requirements -- high performance and facilitation of collaboration. The adaptability of a product to high-performance architectures is a good indication of scalability, and most of the products can be adapted to the parallelism of Hadoop or employ some other means of achieving faster computation.

All of the products do have some support for Hadoop, including IBM SPSS Modeler and SPSS Statistics; RapidMiner's commercial component Radoop, which connects the Studio front end and Server analysis engine to data stored in Hadoop; Oracle's Big Data Discovery and ORAAH tools; and KNIME's Big Data Extensions and Cluster Execution add-ins.

IBM SPSS now also provides enhanced support for a number of multithreaded analytical algorithms that may speed performance. Teradata Aster Analytics addresses high-performance requirements through its Massively Parallel Processing architecture. SAP's Expert Analytics version of SAP BusinessObjects Predictive Analytics can execute in-memory data mining for handling large-volume data analysis efficiently. Microsoft R Server leverages its ScaleR module, a comprehensive library of big data analytics algorithms that support parallelization. Scoring algorithms implemented using SAS Enterprise Miner can be deployed and executed within a Hadoop environment.

In addition, integration with Apache Spark appears to be of growing importance. SPSS, KNIME, Oracle, RapidMiner and SAP all provide access to Apache Spark libraries to support analytics applications that need to scale with exploding data volumes. This enables developed applications to take advantage of a high-performance cluster platform to distribute the workflow across the cluster.

Collaboration

As noted, the larger the organization, the more likely there will be a need to share analyses, models and applications across different groups and among many analysts. Organizations that have many analysts distributed across the enterprise may look for increased means to share models and collaborate regarding the interpretation of results.

IBM's SPSS Modeler Gold edition provides collaboration capabilities, and RapidMiner's Server product provides support for sharing and collaboration. Alteryx Analytics Gallery provides a mechanism for sharing sophisticated analytics applications in the cloud with members of an extended organization. KNIME offers commercial extensions to support team collaboration, as well as extensions supporting operational collaboration, such as remote-scheduled execution, report generation, shared data space and a workflow repository. SAS Enterprise Miner's client-server architecture enables business users and data analysts to work collaboratively by sharing models and other work products.

Alteryx, KNIME and Teradata Aster have added capabilities to help manage analytical workflows. Also, some of the vendors have started to look at ways to enable their tools to integrate with others that may have complementary functional sweet spots. For example, Teradata Aster now has an extension to integrate with KNIME that enables users to leverage the KNIME workflow editor and incorporate Aster Analytics functions into those workflows.

Vendor size and product integration

Vendors can be compared in terms of their size. One might compare and contrast what could be referred to as the mega-vendors, whose big data analytics tools are just one product among a massive portfolio of tools. If you work for a larger organization that typically negotiates site-wide, enterprise licenses for the full suite of a vendor's tools from a mega-vendor such as IBM, SAS, SAP or Oracle may be a reasonable choice.

The large vendors sell big data analytics tools that are a part of a much larger tool ecosystem. Presumably, the products from a mega-vendor will be at least somewhat integrated and intended to work together. In addition, some people feel more comfortable with bigger vendors, with an expectation of stability and consistent customer service. On the other hand, you may only be able to acquire these big data analytics tools as part of a much larger software licensing arrangement.

Smaller vendors, such as KNIME, Alteryx and RapidMiner, have revenues that are generally based on licensing and support for a small number of big data analytics products. A smaller vendor may provide closer contact with their product management and innovation teams, and you may be able to influence the direction of the product roadmap or enhanced functionality.

A smaller vendor might also be more flexible in terms of price and the features included in the licensing arrangement. You must realize, however, that working with a smaller vendor does present some risk in terms of stability, the resources available for support and the possibility that the company may be acquired, which can impact the customer relationship.

The larger vendors are clearly responsive to user needs for integration with other systems, although that often centers on other products within each vendor's inventory. For example, SAP Predictive Analytics has improved integration with SAP HANA and BusinessObjects Cloud. SAS Enterprise Miner has added nodes to execute code in a SAS open, cloud-ready, in-memory Viya environment. Microsoft offers SQL Server R Services, an R installation that runs alongside SQL Server and enables users to integrate Microsoft R Server data with SQL Server and Microsoft's other business intelligence tools.

Budget for licensing and maintenance

Almost all of the vendors sell different versions or editions of their products, with a range of costs for acquisition and total cost of operation. IBM, Oracle, RapidMiner, Teradata and Microsoft sell editions at different tiers, with the license cost proportional to the features, capabilities and freedom from limitations in terms of the volumes of data to be analyzed or the number of processing nodes the product can use.

KNIME and RapidMiner provide free and open source versions of their products, either charging for support services or for editions supporting enterprise-class applications. KNIME, RapidMiner and Alteryx have relatively low licensing costs for a smaller number of users. If you're considering SAS or SAP, you must contact them for pricing alternatives.

The marketplace for big data analytics software can be a confusing place, but hopefully this article has helped you understand the benefits big data analytics software can provide your organization, and assisted you in differentiating between the specific tools examined here.


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Unfriendly Skies: Predicting Flight Cancellations Using Weather Data (Part 3) | killexams.com real questions and Pass4sure dumps

In Part 1 of this series, they wrote about their goal to explore a use case and use various machine learning platforms to see how they might build classification models with those platforms to predict flight cancellations. Specifically, they hoped to predict the probability of the cancellation of flights between the ten U.S. airports most affected by weather. They used historical flight data and historical weather data to make predictions for upcoming flights.

In Part 2, they started their exploration with IBM SPSS Modeler and APIs from The Weather Company. With this post, they look at IBM's Data Science Experience (DSX).

Tools Used in This Use Case Solution

DSX is a collaborative platform for data scientists built on open-source components and IBM-added value, which is available in the cloud or on-premise. In the simplest terms, DSX is a managed Apache Spark cluster with a Notebook front-end. By default, it includes integration with data tools like a data catalog and data refinery, Watson Machine Learning services, collaboration capability, model management, and the ability to automatically review a model's performance and refresh/retrain the model with new data. And IBM is quickly adding more capabilities. Read here to see what IBM is doing lately for data science.

A Python Notebook Solution

In this case, they followed roughly the same steps they used in the SPSS model from Part 2 — only this time, they wrote Python code in a Jupyter notebook to get similar results. They encourage readers to come up with their own solutions. Let us know. We'd love to feature your approaches in future blog posts.

The first step of the iterative process is gathering and understanding the data needed to train and test their model. Since they did this work for Part 2, they made use of the analysis here.

  • Flights data: They gathered data for 2016 flights from the U.S. Bureau of Transportation Statistics website. The website allowed us to export one month at a time, so they ended up with twelve CSV (comma separated value) files. Importing those as DataFrames and merging into a single DataFrame was straightforward.
  • Image title

    Figure 1: Gathering and preparing flight data in IBM DSX

  • Weather data: With the latitude and longitude of the 10 Most Weather-Delayed U.S. Major Airports, they used one of the Weather Company's APIs to get the historical hourly weather data for all of 2016 for each of the ten airport locations and created a CSV file that became their data set in the notebook.
  • Combined flights and weather data: To each flight in the first dataset, they added two new columns, ORIGIN and DEST, containing the respective airport codes. Next, they merged flight data and the weather data so that the resulting DataFrame contained the flight data along with the weather for the corresponding origin and destination airports.
  • Data Preparation, Modeling, and Evaluation

    To start preparing the data, they used the combined flights and weather data from the previous step and performed some cleanup. They deleted columns of features that they didn't need and replaced null values in rows where flight cancellations were not related to weather conditions.

    Next, they took the features they discovered when they created a model using SPSS (such as flight date, hour, the day of the week, origin and destination airport codes, and weather conditions) and they used them as inputs to their Python model. They also chose the target feature for the model to predict: the cancellation status. They deleted the remaining features.

    Next, they ran OneHotEncoder on the four categorical features. One-hot encoding is a process by which categorical features get converted into a format that works better with certain algorithms, like classification and regression. Figure 2 shows the number of feature columns, expanded significantly with one hot encoding.

    Image title

    Figure 2: One-hot encoding expands four feature columns into many more

    Interestingly, the flight data is heavily imbalanced. Specifically, as seen in Figure 3, of all the flights in the data set only a small percentage are actually canceled.

    Image title

    Figure 3: Historical data; distribution of canceled (1) and non-canceled (0) flights

    To address that skewness in the original data, they tried oversampling the minority class, under-sampling the majority class, and a combination of both — but none of these approaches worked well. They then tried something called SMOTE (Synthetic Minority Over-Sampling Technique), an algorithm that provides an advanced over-sampling algorithm to deal with imbalanced datasets. Since it generates synthetic examples rather than just using replication, it helped their selected model work more effectively by mitigating the problem of overfitting that random oversampling can cause. SMOTE isn't considered effective for high dimensional data, but that isn't the case here.

    In Figure 4, they notice a balanced distribution between canceled and non-canceled flights after running the data through SMOTE.

    Image title

    Figure 4: Distribution of canceled and non-canceled flights after using SMOTE

    It's important to mention is that they applied SMOTE only to the training dataset, not the test data set. A detailed blog by Nick Becker guided their choices in the notebook.

    At this point, they used the Random Forest Classifier for their model. It did the best when they used SPSS so they used again in their notebook. They have several ideas for the second iteration of their model in order to tune it, one of which is to try multiple algorithms to see how they compare.

    Since this use case deals with classification analysis, they used some of the common ways to evaluate the performance of the model: the confusion matrix, F1 score, and ROC curve, among some others. Figures 5 and 6 show the results.

    Image title

    Figure 5: Test/validation results

    Image title

    Figure 6: ROC curve for training dataset

    Figure 6 is the ROC curve from the training dataset. Figure 5 shows us that the results of the training and test datasets are pretty close, which is a good indication of consistency, though they realize that with some tuning it could get better. Nevertheless, they decided that the results were still good for the purposes of their discussion in this blog, and they stopped their iterations here. They encourage readers to refine the model further or even to use other models to solve this use case.

    Conclusion

    This was a project to compare creating a model in IBM's SPSS with IBM's Data Science Experience. SPSS offers a no-code experience while DSX offers the best of open-source coding capability with many IBM value-adds. SPSS is an amazing product and gets better with every release, adding many new capabilities.

    IBM's Data Science Experience is a great platform for both the beginning and experienced data scientist. Anyone can log in and have immediate access to a managed Spark cluster with a choice of a Jupyter notebook front-end using Scala, Python or R, SPSS and visual data modeler (no coding). It offers easy collaboration with other users, including adding other data scientists who could then look over their shoulders and make suggestions. The community is active and has already contributed dozens of tutorials, datasets, and notebooks. If they had added Watson Machine Learning, they could very easily have deployed and managed their model with an instant REST endpoint to call from any application. If their data was changing, they could have WML review their model periodically and retrain it with any new data if their metric (ROC Curve) value fell below a given threshold. That, along with new data cataloging and data refinery tooling added recently, make this a platform worth checking out for any data science project.

    SPSS has a lot, but not everything. Writing the python code in a notebook was a bit more time-consuming than what they did in SPSS, but it also gave quite a bit more flexibility and freedom. They had access to everything in the python libraries, and of course, one of the benefits of python as an open-source language is the trove of helpful examples.

    I would say both platforms have their place, and neither can claim to be better for everything. Those doing data science for the first time will probably find SPSS an easier place to start given its drag-and-drop user interface. Those who have come out of school as programming wizards will want to write code, and DSX will give them a great way to do that without worrying about installing, configuring, and correctly integrating various product versions.

    Resources

    The IBM notebook and data that form the basis for this blog are available on GitHub.

    Topics:

    ai ,machine learning ,classification ,predictive analytics ,python ,data science ,jupyter notebook ,regression ,algorithms ,tutorial



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