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IBMSPSSMBPDA IBM SPSS Modeler - Business(R) ally Data Analyst Associate

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IBMSPSSMBPDA exam Dumps Source : IBM SPSS Modeler - Business(R) ally Data Analyst Associate

Test Code : IBMSPSSMBPDA
Test appellation : IBM SPSS Modeler - Business(R) ally Data Analyst Associate
Vendor appellation : IBM
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A successful facts-pushed corporation has to give the usurp paraphernalia for its statistics analysts, developers, and commerce conclusion clients. increasingly, this means leveraging open-source utility. The R or Python programming languages, together with the Apache Spark and Hadoop facts frameworks, are favored open-source paraphernalia for many up to date analysts and builders. however for complete their merits, regular open-supply records programs also attain with potentially time-drinking drawbacks.


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

IBM_logo_2009.jpg IBM (information, web site) has simply launched an improve to the SPSS predictive analytics utensil for you to allow clients to research text gathered from convivial media sources, together with Twitter, fb, blogs, wikis and RSS feeds.

The upgrade represents a substantial enhance in the SPSS Modeler facts mining and textual content analytics respond with skills that stretch as far as detecting the context of the words used. information from the convivial internet can even be merged with interior information to create even more redress intelligence about patrons, IBM says.

With its unusual skill to keep in mind emoticons, slang and different convivial media terminology, in addition to jargon from inescapable industries, it contains taxonomies from a hundred and eighty verticals starting from actuality Sciences to financial, coverage and purchaser Electronics. IBM SPSS Modeler 14 premium edition is designed to investigate any counsel that gives groups insights into how their items are being got.

IBM And SPSS

here's the primary foremost improve to Modeler on account that IBM purchased out predictive analytics commerce SPSS for US$ 1.2 billion ultimate October.

at the time IBM spoke of the acquisition would further extend its information on require (IOD) utility portfolio and commerce analytics capabilities. Forrester is interpreting the flood because the first salvo in a combat for supremacy within the enterprise intelligence market.

SPSS Modeler 14

And with this improve, IBM has introduced to its convivial and enterprise analytics capabilities and really upped the ante for competitors. Modeler 14 top class edition, through analyzing sentiment in convivial media, can predict what consumers own an interest in, where they'll go, and what practices will maintain them fascinated.

SPSS_workbench.jpgIBM SPSS Modeler 14 top rate version workbench 

It comes with unusual semantic networks and more than four hundred,000 phrases across most verticals as well as one hundred,000 synonyms and heaps of manufacturers enabling users to hyperlink sentiment and items without needing to construct up their personal definitions inside these one hundred eighty taxonomies.

SPSS_Add text.jpg

during this recognize, IBM cites the banking industry as an specimen where the semantic network protecting it may well distinguish between plenty of the terminology governing mortgage lending. It is awake of, as an example, that a “floating expense” is a similar as a mortgage mortgage and that variable and adjustable loan fees are without difficulty the very element.

company smart decisions

The final analysis here is more suitable company resolution making. the usage of natural Language Processing it takes key concepts, opinions and expressed sentiment from the web, including survey data and textual content and integrates that records into models designed to foretell client reaction as well as recommending improved enterprise choices.

by means of putting this along with the structured data that already exists within the enterprise, clients can uncover relationships between ideas and purchaser moves so as to construct better knowing of customer businesses and how they are reacting in a particular channel.

IBM BI enlargement

And as to why IBM went down this route within the first place, at the time of the SPSS acquisition Forrester (news, web site) referred to three causes:

  • persisted growth of its tips On require accommodate -- its smallest commerce enviornment -- that has seen it purchase out Ascential software, FileNet and Cognos. IBM’s imaginative and prescient of a smarter Planet combines instrumentation, interconnectivity and intelligence. devoid of SPSS, Forrester says, IBM would now not be capable of deliver on this.
  • Strategic circulate to counteract the acquisition of sun Microsystems by using Oracle and rescue itself forward of the different big weapons at a features degree.
  • Improves market placement through placing itself in a far better position than another vendor to bring integrated sensible solutions sooner or later, from actual world consciousness to resolution advocate equipment.
  • The newest edition of IBM SPSS Modeler information mining and textual content analytics workbench is now attainable global. textual content analytics workbench is simply obtainable in IBM SPSS Modeler top rate edition. 


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     Mitsui O.S.k. strains and its entirely-owned consolidated subsidiary MOL tips programs, (MOLIS) to birth multi-dimensional evaluation of the causes for incidents and issues on its operated vessels, the consume of IBM's statistical analysis utility, "IBM SPSS Modeler".

    IBM SPSS Modeler is an advanced statistics analysis software that gives prospective evaluation from mass extent of information and helps more suitable altenative making to limpid up company concerns.

    The MOL community has conventionally aggregated incidents and issues records mentioned by course of its operated vessels to "visualize" protected operation. And any more, the community will advance more positive measures to avoid incidents and check the effects by course of analyzing correlations and causal relationship of statistics from diverse sources (as an example, operation information, crewmember data, vessel inspection facts, and so on).

     furthermore, it will build a brand unusual analysis components the consume of the text mining function, for some facets of unstructured records, equivalent to near misses gathered from crewmembers.

    earlier than this evaluation, the group held a three-month trial starting in July 2017 and developed analysis fashions that assess causal relationship of assistance on crewmembers, corresponding to downtime issues and years of onboard journey.

    The MOL community perpetually uses and applies ICT technology in a proactive method, with the intention guaranteeing safe, sturdy cargo transport and fitting the locality chief in protected operation.

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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 skill to leverage ample Data analytics and identify patterns offers captious competitive odds 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 chore 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 lonesome vendor: some of the ML vendors below tender a replete cloud computing platform, others are stand lonesome firms dedicated to ML. There's no hard and snappy rule, but the stand lonesome firms may be more incentivized to bid competitively for your business.
  • Type of machine learning: carry out you want to implement ML for retail? For healthcare? require your potential vendor what sectors they own invested in the most heavily.
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    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 consume machine learning in one configuration or another. Sorting through the choices and options can prove confusing. We’ve identified eight top vendors in the machine learning space, based on the features they offer, analyst reviews, customer testimonials and independent research.

    Jump to:

    Alteryx

    Alteryx offers a unified platform that addresses data, analytics and machine learning. The vendor strives to minimize the exigency for data scientists by creating unusual and improved partnerships among IT, analytics experts and lines of commerce 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 skill to automate workflows and various processes.

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

    AWS SageMaker

    The fully managed machine learning service allows data scientists and developers to build and train machine learning models and insert them into applications. This makes it viable to hasten 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 viable to consume the integrated SageMaker console to launch and scale projects quickly. The platform supports HIPAA and other compliance standards.

    Read their 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 character 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 exigency 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 ply batch prediction and online prediction requirements.

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

    Read their 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 stalwart collaboration capabilities. Among its top features: a robust architecture, stalwart algorithms and a powerful skill to execute machine learning.

    The Watson Studio is allotment 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 their 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 utensil 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 plane of flexibly, extensibility and openness.

    Azure ML Studio also delivers comprehensive capabilities across the replete sweep 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 touchstone for neural networks. Microsoft also plans to automate a growing number of functions within the Azure Machine Learning platform.

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

    RapidMiner

    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 ample 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 plane of flexibility own established it as a Popular altenative 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 their in-depth review of RapidMinerSee user reviews of RapidMiner

    SAP Leonardo

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

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

    Read their 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 altenative 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 skill to execute.

    VDMML connects to numerous other products and data sources. Forrester famed that SAS has emerged as the first truly multimodal PAML solution. It uses a wizard to automatically train a model, transmogrify 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 their 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

    Alteryx

    AWS SageMaker

    Google Machine Learning Engine

    IBM Watson Studio

    Microsoft Azure Machine Learning

    RapidMiner

    SAP Leonardo

    SAS Visual Data Mining and Machine Learning

    ML Focus

    Analytics and ML platform that allows users to build models in a lone 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 consume machine learning broadly.

    Broad and deep data science and ML platforms.

    Multimodal PAML solution with powerful wizards to advocate data mining and ML. stalwart 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 flush array of open source frameworks and tools.

    Automatic resource provisioning and monitoring for CPUs, GPUs and TPUs. stalwart open source advocate 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. stalwart advocate 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. stalwart open source integration. Runs in SAP’s HANA public cloud.

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

    User comments

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

    High marks for the interface, configurability and usability. However, some protest 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 record it as powerful and “revolutionary” though there are complaints about the want of GPU support. 

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

    High ratings. Users fancy 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 freight in increments of €7.25.

    N/A


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    Data and ample data analytics are snappy becoming the lifeblood of any successful business. Getting the technology prerogative can be challenging, but building the prerogative team with the prerogative skills to undertake ample data initiatives can be even harder.

    Not surprisingly, that challenge is reflected in the rising require for ample data skills and certifications. According to research by IT research firm Foote Partners, both noncertified advanced data analytics skills and certified ample data skills own gained value in recent years: with 74 advanced data analytics related skills and certifications rising in unprejudiced value by 6 percent in 2015, followed by 116 advanced data analytics related skills and certifications increasing 4.8 percent overall in market value in 2016. Additionally, Foote Partners research create 123 related certified and noncertified ample data skills seeing a 0.3 percent gain in value in the first quarter of 2017.

    Organizations are on the hunt for data scientists and analysts with expertise in the techniques required to dissect ample data. They also exigency ample data systems architects to translate requirements into systems, data engineers to build data pipelines, developers who know their course around Hadoop clusters and other technologies, and systems administrators and managers to tie everything together.

    [ Find out the hottest data and analytics trends today. | regain ahead with the top certs for ample data, project management, agile, and the cloud. | regain weekly career tips by signing up for their CIO Leader newsletter. ]

    These skills are in high require and are relatively rare. Individuals with the prerogative coalesce of experience and skills can require high salaries. The prerogative certifications can help.

    "Advanced data analytics capabilities are just too captious for staying competitive," David Foote, co-founder, chief analyst and chief research officer of Foote Partners, said in a statement released with the research. "They've expanded in popularity from a few industries to nearly every industry and market. And there is the Internet of Things, the next captious focus for data and analytics services. IDC is predicting a 30 percent CAGR over the next five years, while McKinsey is expecting IoT to own a $4 trillion to $11 trillion global economic impact by 2025 as businesses scrutinize to IoT technologies to provide more insight."

    While the market value of noncertified advanced analytics skills has actually increased faster as a percentage of base salary than the value of certified ample data skills, according to Foote Research, Foote believes pay premiums for both noncertified and certified skills will steadily climb over the next 12 to 24 months.

    If you're looking for a course to regain an edge — whether you're job hunting, angling for a promotion or just want tangible, third-party proof of your skills — ample data certification is a remarkable option. Certifications measure your information and skills against industry- and vendor-specific benchmarks to prove to employers that you own the prerogative skillset. The number of ample data certs is expanding rapidly.

    Below is their guide to the most sought after ample data certifications to befriend you settle which cert is prerogative for you.

    If you would fancy to submit a ample data certification to this directory, tickle email us.

    Analytics: Optimizing ample Data Certificate

    The Analytics: Optimizing ample Data Certificate is an undergraduate-level program intended for business, marketing and operations managers, data analyst and professionals, monetary industry professionals, and little commerce owners. The program brings together statistics, analysis, and written and oral communications skills. It introduces students to the tools needed to dissect big datasets, covering topics including importing data into an analytics software package, exploratory graphical and data analysis, building analytics models, finding the best model to explore correlation among variables and more.

    Organization: University of Delaware

    Price: $2,895 course fee

    How to prepare: A basic background in statistics and some prior college coursework is recommended.

    Certificate in Engineering Excellence ample Data Analytics and Optimization (CPEE)

    Offered in Hyderabad and Bengaluru, India, the Certificate in Engineering Excellence ample Data Analytics and Optimization is an intensive 18-week program that consists of 10 courses (lectures and labs) for students of complete aspects of analytics, including working with ample data using Hadoop. It focuses on R and Hadoop skills, as well as statistical modeling, data analytics, machine learning, text mining and optimization. Students are evaluated on a real-world capstone project and a sequence of quizzes.

    Organization: International School of Engineering (INSOFE)

    Price: ₹3000 (INR) application fee and a program fee of ₹3,25,000 + 15 percent service tax.

    How to prepare: INSOFE admits students based on performance on its entrance exam and prior academic background and work experience.

    Certification of Professional Achievement in Data Sciences

    The Certification of Professional Achievement in Data Sciences is a non-degree program intended to develop facility with foundational data science skills. The program consists of four courses: Algorithms for Data Science (CS/IEOR), Probability & Statistics (STATS), Machine Learning for Data Science (CS), and Exploratory Data Analysis and Visualization (STATS).

    Organization: Columbia University

    Price: $1,858 per credit (a minimum of 12 credits, including the four courses, are required to complete the program). In addition, there is an $85 non-refundable application fee for the on-campus program and $150 for the online program. The online program also includes an additional non-refundable technology fee of $395 per course.

    How to prepare: An undergraduate degree and prior quantitative and introductory to computer programming coursework are required.

    Certified Analytics Professional

    The Certified Analytics Professional (CAP) credential is a universal analytics certification that certifies end-to-end understanding of the analytics process, from framing commerce and analytic problems to acquiring data, methodology, model building, deployment and model lifecycle management. It requires completion of the CAP exam and adherence to the CAP Code of Ethics.

    Organization: INFORMS

    Price: $495 if you are an INFORMS member, or $695 if you're not. Team pricing is available for organizations. 

    How to prepare: A list of study courses and a sequence of webinars are available through registration.

    Cloudera Certified Associate (CCA) Data Analyst

    A SQL developer who earns the CCA Data Analyst certification demonstrates core analyst skills to load, transform and model Hadoop data to define relationships and extract meaningful results from the raw output. It requires passing the CCA Data Analyst Exam (CCA159), a remote-proctored set of eight to 12 performance-based, hands-on tasks on a CDH 5 cluster. Candidates own 120 minutes to implement a technical solution for each task. They must dissect the problem and arrive at an optimal approach in the time allowed.

    Organization: Cloudera

    Price: $295

    How to prepare: Cloudera recommends candidates snitch the Cloudera Data Analyst Training course, which has the very objectives as the exam.

    Cloudera Certified Associate (CCA) Spark and Hadoop Developer

    The CCA Spark and Hadoop Developer credential certifies a professional has proven their core skills to ingest, transform and process data using Apache Spark and core Cloudera enterprise tools. It requires passing the remote-proctored CCA Spark and Hadoop Developer Exam (CCA175), which consists of eight to 12 performance-based, hands-on tasks on a Cloudera Enterprise cluster. Each question requires the candidate to solve a particular scenario. Some cases may require a utensil such as Impala or Hive, others may require coding. Candidates own 120 minutes to complete the exam.

    Organization: Cloudera

    Price: $295

    How to prepare: There are no prerequisites required, but Cloudera says the exam follows the very objectives as the Cloudera Developer Training for Spark and Hadoop course, making it excellent preparation for the exam.

    Cloudera Certified Professional (CCP): Data Engineer

    The CCP: Data Engineer credential certifies the skill to accomplish core competencies required to ingest, transform, store and dissect data in Cloudera's CDH environment. It requires passing the remote-proctored CCP: Data Engineer Exam (DE575), a hands-on, practical exam in which each user is given five to eight customer problems each with a unique, big data set, a CDH cluster and four hours. For each problem, the candidate must implement a technical solution with a high degree of precision that meets complete the requirements.

    Organization: Cloudera

    Price: $400

    How to prepare: Cloudera suggests professionals seeking this certification own hands-on experience in the field and snitch the Cloudera Developer Training for Spark and Hadoop course.

    EMC Proven Professional Data Scientist Associate (EMCDSA)

    The EMCDSA certification demonstrates an individual's skill to participate and contribute as a data science team member on ample data projects. It includes deploying the data analytics lifecycle, reframing a commerce challenge as an analytics challenge, applying analytic techniques and tools to dissect ample data and create statistical models, selecting the usurp data visualizations and more.

    Organization: Dell EMC Education Services

    Price: $600 for video-ILT streaming; $5,000 for instructor-led

    How to prepare: EMC offers a training course, available as either a video or an instructor-led course.

    IBM Certified Data Architect – ample Data

    Designed for data architects, the IBM Certified Data Architect – ample Data certification requires passing a test that consists of five sections containing a total of 55 multiple-choice questions. It demonstrates a data architect can work closely with customers and solutions architects to translate customers' commerce requirements into a ample data solution.

    Organization: IBM Professional Certification Program

    Price: $200

    How to prepare: IBM recommends a sequence of seven multi-day courses on SPSS Modeler to InfoSphere BigInsights to prepare for the test.

    IBM Certified Data Engineer – ample Data

    The IBM Certified Data Engineer – ample Data certification is intended for ample data engineers, who work directly with data architects and hands-on developers to transmogrify an architect's ample data vision into reality. Data engineers understand how to apply technologies to solve ample data problems and own the skill to build large-scale data processing systems for the enterprise. They develop, maintain, test and evaluate ample data solutions within organizations, providing architects with input on needed hardware and software. This certification requires passing a test that consists of five sections containing a total of 53 multiple-choice questions.

    Organization: IBM Professional Certification Program

    Price: $200

    How to prepare: IBM recommends a sequence of nine multi-day courses to prepare for the test.

    Mining Massive Data Sets Graduate Certificate

    Designed for software engineers, statisticians, predictive modelers, market researchers, analytics professionals, and data miners, the Mining Massive Data Sets Graduate Certificate requires four courses and demonstrates mastery of efficient, powerful techniques and algorithms for extracting information from big datasets fancy the Web, convivial network graphs and big document repositories. The certificate usually takes one to two years to complete.

    Organization: Stanford seat for Professional Development

    Price: $18,000 tuition

    How to prepare: A Bachelor's degree with an undergraduate GPA of 3.0 or better is required. Applicants should own information of basic computer science principles and skills, at a plane sufficient to write a reasonably non-trivial computer program.

    MongoDB Certified DBA Associate

    The MongoDB Certified DBA Associate credential is intended to demonstrate that operations professionals understand the concepts and mechanics required to administrate MongoDB. It requires a 90 minute, multiple altenative exam.

    Organization: MongoDB University

    Price: $150

    How to prepare: There are no prerequisites, but MongoDB suggests candidates complete an in-person training or one of its online courses (M102: MongoDB for DBAs; M202: MongoDB Advanced Deployment Operations). MongoDB also provides the MongoDB Certification Exam Study Guide, available to those who own registered for a certification exam.

    MongoDB Certified Developer Associate

    The MongoDB Certified Developer Associate credential is intended for software engineers who want to demonstrate a solid understanding of the fundamentals of designing and building applications using MongoDB. It requires a 90 minute, multiple altenative exam.

    Organization: MongoDB University

    Price: $150

    How to prepare: There are no prerequisites, but MongoDB suggests candidates complete an in-person training or one of its online courses (M101J: MongoDB for Java Developers; M101JS: MongoDB for Node.js Developers; M101N: MongoDB for .NET Developers; M101P: MongoDB for Developers). MongoDB also provides the MongoDB Certification Exam Study Guide, available to those who own registered for a certification exam.

    SAS Certified ample Data Professional

    The SAS Certified ample Data Professional certification program is for individuals seeking to build on their basic programming information by learning how to assemble and dissect ample data in SAS. The program focuses on SAS programming skills; accessing, transforming and manipulating data; improving data character for reporting and analytics; fundamentals of statistics and analytics; working with Hadoop, Hive, Pig and SAS; and exploring and visualizing data. The program includes two certification exams, both of which the participants must pass.

    Organization: SAS Academy for Data Science

    Price: $9,000 for classroom (Cary, NC), $4,725 for blended learning (combination of 24/7 online access and instructor-led training)

    How to prepare: At least six months of programming experience in SAS or another programming language is required to enroll.

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