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IBMSPSSMPRO | IBM SPSS Modeler Professional(R) Certification

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IBMSPSSMPRO - IBM SPSS Modeler Professional(R) Certification - braindump

Vendor IBM
Exam Number IBMSPSSMPRO
Exam Name IBM SPSS Modeler Professional(R) Certification
Questions 73 Q & A
Recent Update February 14, 2019
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IBMSPSSMPRO exam Dumps Source : IBM SPSS Modeler Professional(R) Certification

Test Code : IBMSPSSMPRO
Test Name : IBM SPSS Modeler Professional(R) Certification
Vendor Name : IBM
Q&A : 73 Real Questions

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IBM IBM SPSS Modeler Professional(R)

A Pure Play On Self-service big statistics Prep And Analytics: look ahead to Smarter Valuation Entry point | killexams.com Real Questions and Pass4sure dumps

No outcomes found, try new key phrase!the general analytics professional reviews basic use of five equipment – the leaders being, open source statistical programming language R (which is supported by means of Alteryx), IBM SPSS data, the SAS p...

IBM Watson Studio Spark executions the use of RStudio IDE | killexams.com Real Questions and Pass4sure dumps

in this publish i'll reveal you a way to use the IBM Watson Studio Spark executions the usage of RStudio IDE, which launched from Watson Studio initiatives.

RStudio IDE

RStudio is the premier built-in construction atmosphere (IDE) for R programmers. Watson Studio gives a convenient method of loading and executing R scripts.

Spark service

Watson Studio offers creating Spark execution environments internal projects to execute Spark courses within the cloud.

Launch RStudio IDE

Refer RStudio launch tutorial. This documentation aims RStudio with undertaking and it isn't yet purchasable in all information facilities.

operating Spark programs from RStudio

RStudio uses the brand new sparklyr package (http://spark.rstudio.com/index.html) to join with the Spark kernel gateway on the cloud the usage of Spark-as-a-carrier interactive APIs. The sparklyr equipment comprises a dplyr interface to Spark records frames in addition to an R interface to Spark’s disbursed computing device learning pipelines.

that you would be able to use your present Spark instances from RStudio. to use this function, run right here steps:

1 Load and monitor purchasable Spark circumstances

2 hook up with a Spark instance

three Run dplyr APIs and Spark’s dispensed laptop learning libraries

4 monitor tables for Spark loaded records sets

5 View logs for Spark kernel interaction

6 View Spark join repute and fix or disconnect

listing accessible Spark cases

in the event you birth RStudio two info are created in the working listing (don’t delete them!):  1) config.yml file — Lists your entire accessible Spark situations.  2) .Rprofile file — Configures your Spark environment.

These data are created under your home directory, /home/rstudio. If the working directory is different from the domestic directory, which you could copy the config.yml and .RProfile files to your latest working directory.

that you would be able to load and screen Spark circumstances through the use of the load_spark_kernels() and display_spark_kernels() R features . example:

This feature lists handiest your presently accessible Spark instances. in order for you an extra Spark illustration, create it in Watson Studio Environments.

join with the chosen Spark illustration

To hook up with Spark, run the spark_connect R function. for instance:

sc <- spark_connect(config = kernels[1])

After this Spark context is created, all subsequent operations may be achieved the usage of this Spark example:

as soon as linked to Spark we will see connection status like below

Run dplyr APIs and Spark’s disbursed computer gaining knowledge of libraries

To run dplyr functions, load the dplyr package after which run the copy_to feature the use of the Spark context. for example:

library(dplyr) localDF <- data.body(name=c("John", "Smith", "Sarah", "Mike", "Bob"), age=c(19, 23, 18, 25, 30)) sampletbl <- copy_to(sc, localDF, "sampleTbl")

This creates a Spark statistics body on the far flung kernel in response to a local R information frame, and displays the local references in the Spark view:

View the table for Spark loaded facts units

Spark View indicates all the remote Spark statistics frames. that you would be able to click on on the table icon to display sample views of these tables.

View the log for Spark kernel interplay

that you could opt for the Logs icon to view the entire calls to the Spark illustration.

View Spark connect fame and fix or disconnect a service

that you would be able to view the connection reputation on the Spark View, and you'll connect to or disconnect from a Spark carrier.

connect Disconnect read venture info in Spark

RStudio offers utility feature get_project_asset_path() to simplify entry to undertaking asset data from spark jobs. here is the example to load assignment file in spark and create spark records frame

# R interface for Apache Sparklibrary(sparklyr)

library(dplyr)

# load kernelskernels <- load_spark_kernels()

# screen kernelsdisplay_spark_kernels()

# connect with our spark kernelsc <- spark_connect(config = kernels[1])

# create a route to Tim's test bucketpath <- get_project_asset_path("airline15krows.csv")

# study the usage of sparklyr package airline15krows_tbl <- spark_read_csv(sc,identify = "airline15krows", course = course, delimiter="|", infer_schema = FALSE)

# list all tablessrc_tbls(sc)head(airline15krows_tbl,4)

Examples

which you can find the illustration R script files in the /ibm-sparkaas-demos folder under your domestic listing. These examples demonstrate scenarios that you would be able to run with Spark in RStudio.

spark-kernel-primary.R

Creates standard R records frames and generates remote Spark statistics frames based on the local R records frames. also runs some basic filters and DBI queries.

sparkaas_mtcars.R

masses the widely wide-spread mtcars R statistics body and then generates a Spark facts frame for the mtcars statistics body. It then does transformations to create a training facts set and runs a linear mannequin on the practising statistics set.

sparkaas_flights.R

hundreds some greater facts sets, creates ggplot for prolong and runs windows capabilities. See sparklyr — R interface for Apache Spark for extra assistance.

See additionally sparklyr Examples for extra examples.

Mahesh Kurapati is an Advisory software Engineer with the IBM Analytics group. Mahesh’s simple center of attention is on the building of a lot of micro-functions for IBM information Science journey. Mahesh is concerned in the building of quite a few glowing.information aspects and SparkaaS integration with RStudio. With greater than two decades of journey in application building, Mahesh has contributed key functionalities to IBM items including SPSS statistics, SPSS Modeler, and SPSS Analytics Server.

at first published at datascience.ibm.com on September 28, 2016.


comparing the leading huge information analytics utility options | killexams.com Real Questions and Pass4sure dumps

there are many companies selling products categorised as huge information analytics application. however, it be difficult to...

differentiate these items in line with functionality alone, as most of the tools share an identical aspects and capabilities. moreover, one of the equipment show extraordinarily subtle modifications.

That being referred to, your key differentiating components will likely center of attention on balancing ease of use, algorithmic sophistication and value in the case of your company's ability and degree of maturity in analytics.

in this article, we assess items from nine massive information analytics application 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 device. See the "main vendors of big records analytics utility" sidebar under for extra particulars about their specific product choices.

These vendors symbolize distinctive facets of the huge statistics analytics market. Let's compare and contrast the ways that these products meet the company needs of person businesses.

Analyst competencies and expertise 

Some records analytics equipment are targeted to newbie clients, some are centered to expert information analysts and a few are engineered to attraction to both kinds of users.

products equivalent to IBM SPSS Modeler, RapidMiner's tools, Oracle advanced Analytics and the automated Analytics edition of SAP BusinessObjects Predictive Analytics are commonly designed to enable clients with a limited history in data or records evaluation to analyze statistics, develop analytical models and design analytics workflows with little or no coding.

while each and every seller wraps its core analytics add-ons with an intuitive consumer interface to e-book the analyst's progress in records training, analysis, after which model design and validation, the approach taken might also fluctuate, mainly when evaluating a stand-alone product, reminiscent of RapidMiner, with one it's a element of a larger suite, such because the Oracle product.

tools similar to IBM SPSS information, KNIME Analytics Platform, the professional Analytics module of SAP BusinessObjects Predictive Analytics, Microsoft R and the Teradata Aster Analytics platform give the more refined performance that skilled users expect. Oracle R advanced Analytics for Hadoop (ORAAH), probably the most add-ons in the Oracle huge statistics utility Connectors suite, gives an R interface for manipulating Hadoop disbursed File system data and writing mapper and reducer capabilities in R. this adaptability may be appealing to greater superior facts scientists.

Alteryx and SAS enterprise Miner present performance adapted to the person's stage of abilities, and essentially fall into each classes. Alteryx has introduced improvements to information profiling to assist information scientists more suitable consider their records sources. standard, SAS commercial enterprise Miner and IBM's SPSS equipment stand out when it involves helping greater advanced analytical techniques and model scoring, as well as a broader array of evaluation features, together with neural networks, association analysis and visualization capabilities.

Analytical variety

counting on the use case and software, your firm's clients can be required to support several types of analytics capabilities so as to use specific forms of modeling, equivalent to regression, clustering, segmentation, conduct modeling and decision timber.

whereas this has resulted in large support for the a variety of styles of analytical modeling at a high degree, some providers have invested decades of work into tweaking distinctive versions of their algorithms and including more subtle performance. or not it's important to remember which models are most principal to your company issues and to consider the products in terms of how they most desirable serve your users' enterprise wants.

it's vital to have in mind which fashions are most relevant to your enterprise complications and to evaluate the items when it comes to how they most advantageous serve your clients' business wants.

The more mature and better-end -- and, accordingly, bigger-priced -- equipment will reveal the most effective analytical breadth. Oracle statistics Miner contains an array of widespread desktop studying approaches to support clustering, predictive mining and text mining. both variations of IBM's SPSS product give a various set of analytical strategies and models. And SAS business Miner supports many algorithms and strategies, together with determination timber, time collection, neural networks, linear and logistic regression, sequence and internet route analysis, market basket analysis, and hyperlink evaluation.

The more recent era -- and, in some circumstances, reduce-priced -- products support different fashions, but in all probability with a narrower latitude of algorithmic sophistication.

The model inventory in Alteryx Analytics Gallery includes such capabilities as regression evaluation, determination trees, association rule analysis, classification and time sequence analysis. KNIME comprises strategies for text mining, photograph mining and time collection analysis, and also integrates laptop getting to know algorithms from other open supply tasks, corresponding to Weka and JFreeChart.

another factor of analytical diversity is integration with programming languages and statistical equipment, comparable to R, for incorporating current libraries, in addition to user-described performance. in reality, integration with R may well be considered an increasingly crucial differentiator.

Alteryx designer, Microsoft R, SAS enterprise Miner, Teradata Aster Analytics, Oracle's ORAAH and KNIME's Analytics Platform all interface and guide integration with R. a few of the carriers, including IBM, Oracle, Microsoft, RapidMiner and SAP, deliver a transforming into library of extensions to R and Python, enabling users to take expertise of free libraries.

Scope of the records to be analyzed

There are multiple facets of the scope of the data to be analyzed, including the problem of structured vs. unstructured advice, as well as entry to typical on-premises databases and facts warehouses, cloud-primarily based facts sources, and statistics managed in big records systems, comparable to Hadoop.

although, there are various levels of help for data managed inside less-generic information lakes -- either managed within Hadoop or in another NoSQL information administration device intended to provide horizontal scaling. The elements for distinguishing among the items must be in keeping with your company's specific requirements for getting access to and processing statistics volumes and information variety.

In consciousness of the turning out to be variety of enter sources and the range of underlying techniques used to apartment these records units, an additional set of rising facets it truly is being adopted by way of these companies contains data accessibility. IBM, RapidMiner, Alteryx, Oracle and Microsoft have all more desirable their equipment' statistics import, export and connectivity capabilities. These enhancements may still allow clients to entry a greater complete listing of facts sources whereas simplifying and speeding up the system of loading data into the products.

aid for scalability and excessive performance

The want for scalable performance is pushed by your organization's information volumes and urge for food for evaluation. Smaller groups with much less statistics could be in a position to tolerate products that won't have efficiency features that scale with the purchasable resources, such as the entry-stage models of the reduce-end tools, together with RapidMiner, KNIME, Microsoft R Open and Alteryx fashion designer, that can run on computing device programs and don't require extra server add-ons.

better organizations usually tend to have a more advantageous stock of information units to research, in addition to broader communities of clients. This introduces two further requirements -- excessive performance and facilitation of collaboration. The adaptability of a product to high-efficiency architectures is a very good indication of scalability, and most of the products can be adapted to the parallelism of Hadoop or employ any other means of achieving faster computation.

all of the products do have some support for Hadoop, together with IBM SPSS Modeler and SPSS data; RapidMiner's business element Radoop, which connects the Studio front conclusion and Server analysis engine to data stored in Hadoop; Oracle's big records Discovery and ORAAH equipment; and KNIME's huge facts Extensions and Cluster Execution add-ins.

IBM SPSS now also provides enhanced support for a couple of multithreaded analytical algorithms that may additionally pace performance. Teradata Aster Analytics addresses high-performance necessities through its hugely Parallel Processing structure. SAP's skilled Analytics version of SAP BusinessObjects Predictive Analytics can execute in-memory data mining for handling large-extent statistics analysis efficiently. Microsoft R Server leverages its ScaleR module, a finished library of big facts analytics algorithms that assist parallelization. Scoring algorithms carried out the usage of SAS enterprise Miner can also be deployed and finished within a Hadoop atmosphere.

moreover, integration with Apache Spark appears to be of growing magnitude. SPSS, KNIME, Oracle, RapidMiner and SAP all give access to Apache Spark libraries to help analytics purposes that deserve to scale with exploding information volumes. This allows for developed functions to take expertise of a high-efficiency cluster platform to distribute the workflow across the cluster.

Collaboration

As mentioned, the larger the firm, the greater seemingly there might be a necessity to share analyses, models and functions throughout diverse businesses and amongst many analysts. organizations that have many analysts distributed throughout the commercial enterprise might also seek elevated skill to share models and collaborate concerning the interpretation of outcomes.

IBM's SPSS Modeler Gold version gives collaboration capabilities, and RapidMiner's Server product gives guide for sharing and collaboration. Alteryx Analytics Gallery provides a mechanism for sharing subtle analytics applications within the cloud with members of a long firm. KNIME presents industrial extensions to assist team collaboration, in addition to extensions supporting operational collaboration, akin to faraway-scheduled execution, record generation, shared statistics house and a workflow repository. SAS enterprise Miner's customer-server architecture allows for enterprise users and information analysts to work collaboratively by way of sharing fashions and different work items.

Alteryx, KNIME and Teradata Aster have added capabilities to aid manipulate analytical workflows. also, probably the most companies have started to look at how you can permit their equipment to combine with others that may additionally have complementary practical sweet spots. as an instance, Teradata Aster now has an extension to combine with KNIME that makes it possible for clients to leverage the KNIME workflow editor and include Aster Analytics capabilities into these workflows.

vendor measurement and product integration

carriers can be in comparison in terms of their size. One might compare and contrast what could be stated as the mega-providers, whose large records analytics tools are just one product among a massive portfolio of equipment. in case you work for a bigger organization that customarily negotiates site-large, commercial enterprise licenses for the entire suite of a supplier's equipment from a mega-supplier such as IBM, SAS, SAP or Oracle can be an affordable alternative.

The huge providers promote massive statistics analytics equipment that are part of a a whole lot bigger device ecosystem. possibly, the products from a mega-seller could be as a minimum a little bit built-in and intended to work collectively. moreover, some people consider extra comfortable with greater carriers, with an expectation of steadiness and consistent consumer provider. having said that, you may additionally only be capable of acquire these big information analytics tools as a part of a a good deal higher utility licensing association.

Smaller providers, corresponding to KNIME, Alteryx and RapidMiner, have revenues which are generally in keeping with licensing and guide for a small number of large data analytics products. A smaller dealer may deliver nearer contact with their product management and innovation teams, and you'll be capable of affect the direction of the product roadmap or better performance.

A smaller seller may even be more bendy in terms of fee and the elements included within the licensing arrangement. You should recognize, however, that working with a smaller dealer does latest some possibility in terms of steadiness, the supplies purchasable for aid and the chance that the company may be bought, that may affect the customer relationship.

The greater carriers are obviously attentive to consumer wants for integration with different techniques, despite the fact that commonly facilities on other items within each and every supplier's stock. for example, SAP Predictive Analytics has improved integration with SAP HANA and BusinessObjects Cloud. SAS commercial enterprise Miner has brought nodes to execute code in a SAS open, cloud-ready, in-reminiscence Viya ambiance. Microsoft presents SQL Server R services, an R setting up that runs alongside SQL Server and allows users to combine Microsoft R Server statistics with SQL Server and Microsoft's other business intelligence equipment.

budget for licensing and maintenance

nearly all the providers promote diverse models or variants of their items, with a number charges for acquisition and complete cost of operation. IBM, Oracle, RapidMiner, Teradata and Microsoft sell variations at distinct tiers, with the license cost proportional to the points, capabilities and freedom from boundaries in terms of the volumes of statistics to be analyzed or the number of processing nodes the product can use.

KNIME and RapidMiner deliver free and open source types of their items, either charging for support features or for versions helping business-category functions. KNIME, RapidMiner and Alteryx have surprisingly low licensing fees for a smaller variety of users. if you are considering SAS or SAP, you should contact them for pricing alternatives.

The marketplace for massive data analytics application may also be a confusing location, however confidently this article has helped you keep in mind the benefits massive information analytics application can provide your organization, and assisted you in differentiating between the specific tools examined right here.


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IBM SPSS Modeler Professional(R) Certification

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No result found, try new keyword!the average analytics professional reports primary use of five tools – the leaders being, open source statistical programming language R (which is supported by Alteryx), IBM SPSS Statistics, the SAS p...

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

Tim Bohn and Ricardo Balduino

Piarco Airport, Trinidad in the 1950s, Copyright John Hill, Creative Commons Attribution-Share Alike 4.0

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

In Part 2, we started our exploration with IBM SPSS Modeler and APIs from The Weather Company. With this post, we look at IBM’s Watson Studio.

Tools used in this use case solution

Watson Studio 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, Watson Studio 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, we followed roughly the same steps we used in the SPSS model from Part 2, only this time we wrote python code in a Jupyter notebook to get similar results. We 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 our model. Since we did this work for part 2, we made use of the analysis here.

Flights data — We gathered data for 2016 flights from the US Bureau of Transportation Statistics website. The website allowed us to export one month at a time, so we ended up with twelve csv (comma separated value) files. Importing those as dataframes and merging into a single dataframe was straightforward.

Weather data — With the latitude and longitude of the 10 Most Weather-Delayed U.S. Major Airports, we used one of the Weather Company’s API’s to get the historical hourly weather data for all of 2016 for each of the 10 airport locations and created a csv file that became our data set in the notebook.

Combined flights and weather data — To each flight in the first data set, we added two new columns: ORIGIN and DEST, containing the respective airport codes. Next, we 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, we used the combined flights and weather data from the previous step and performed some cleanup. We deleted columns of features that we didn’t need, and replaced null values in rows where flight cancellations were not related to weather conditions.

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

Next, we 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.

Figure 2 — One-hot encoding expands 4 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 cancelled.

Figure 3 — Historical data: distribution of cancelled (1) and non-cancelled (0) flights

To address that skewedness in the original data, we tried oversampling the minority class, under sampling the majority class, and a combination of both — but none of these approaches worked well. We 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 our 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, we notice a balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

Figure 4 — Distribution of cancelled and non-cancelled flights after using SMOTE

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

At this point, we used the Random Forest Classifier for our model. It did the best when we used SPSS so we used again in our notebook. We have several ideas for a second iteration of our 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, we 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.

Figure 5 — Test/Validation Results Figure 6 — ROC curve for training data set

Figure 6 is the ROC curve from the training data set. Figure 5 shows us that the results from the training and test data sets are pretty close, which is a good indication of consistency, though we realize that with some tuning it could get better. Nevertheless, we decided that the results were still good for the purposes of our discussion in this blog, and we stopped our iterations here. We 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 Watson Studio. SPSS offers a no-code experience while Watson Studio 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 Watson Studio 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 our shoulders and make suggestions. The community is active and has already contributed dozens of tutorials, data sets and notebooks. If we had added Watson Machine Learning, we could very easily have deployed and managed our model with an instant REST endpoint to call from any application. If our data was changing, we could have WML review our model periodically and retrain it with any new data if our 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 we did in SPSS, but it also gave quite a bit more flexibility and freedom. We 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.


IBM Launches Apache Spark-Based Data Science Experience | killexams.com real questions and Pass4sure dumps

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