|Exam Name||:||IBM SPSS Modeler - Business(R) Partner Data Analyst Associate|
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|Updated On||:||April 22, 2019|
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IBMSPSSMBPDA exam Dumps Source : IBM SPSS Modeler - Business(R) Partner Data Analyst Associate
Test Code : IBMSPSSMBPDA
Test Name : IBM SPSS Modeler - Business(R) Partner Data Analyst Associate
Vendor Name : IBM
Q&A : 25 Real Questions
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A successful facts-pushed corporation has to give the appropriate equipment for its statistics analysts, developers, and business 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 equipment for many up to date analysts and builders. however for all their merits, regular open-supply records programs also come with potentially time-drinking drawbacks.
IBM (information, web site) has simply launched an improve to the SPSS predictive analytics tool for you to allow clients to research text gathered from social 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 answer with skills that stretch as far as detecting the context of the words used. information from the social internet can even be merged with interior information to create even more correct intelligence about patrons, IBM says.
With its new ability to keep in mind emoticons, slang and different social media terminology, in addition to jargon from certain industries, it contains taxonomies from a hundred and eighty verticals starting from existence 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 business SPSS for US$ 1.2 billion ultimate October.
at the time IBM spoke of the acquisition would further extend its information on Demand (IOD) utility portfolio and business analytics capabilities. Forrester is interpreting the flow 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 social and enterprise analytics capabilities and really upped the ante for competitors. Modeler 14 top class edition, through analyzing sentiment in social media, can predict what consumers have an interest in, where they'll go, and what practices will maintain them fascinated.
IBM SPSS Modeler 14 top rate version workbench
It comes with new 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.
during this recognize, IBM cites the banking industry as an example where the semantic network protecting it may well distinguish between plenty of the terminology governing mortgage lending. It is aware 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 same element.company intelligent 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:
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.
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 use 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 choice making to clear up company concerns.
The MOL community has conventionally aggregated incidents and issues records mentioned by way 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 way 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 new analysis components the use 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 area 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 ability to leverage Big Data analytics and identify patterns offers critical competitive advantage to today's businesses.
Often used in combination with artificial intelligence and deep learning, machine learning uses sophisticated statistical modeling. These complex systems may reside in private cloud or public cloud. In any case, the passage of time boosts machine learning: as more data is added to a task and analyzed over time, ML produces more accurate the results.
The global machine learning market totaled $1.4 billion in 2017, according to BCC Research. It is estimated to top $8.8 billion by 2022, a jaw-dropping compound annual growth rate (CAGR) of 43.6 percent.
When considering which machine learning vendor to select, weigh these factors:
The machine learning landscape is changing rapidly. Machine learning start-ups are constantly jumping into the space. Established vendors are introducing a variety of offerings that use machine learning in one form or another. Sorting through the choices and options can prove confusing. We’ve identified eight top vendors in the machine learning space, based on the features they offer, analyst reviews, customer testimonials and independent research.
Alteryx offers a unified platform that addresses data, analytics and machine learning. The vendor strives to minimize the need for data scientists by creating new and improved partnerships among IT, analytics experts and lines of business through a collaborative approach.
The product is available in both a code-free and code-friendly format. It uses a drag-and-drop workflow interface and ties into third party demographic, firmographic and spatial data. The company is rated as a “Leader” on the Gartner Magic Quadrant (MQ). Alteryx is moving toward powerful automation and rule-based recommendations, Gartner noted.
Alteryx offers integration with numerous major partners, including Tableau, AWS, Teradata, Microsoft, DataRobot, Salesforce, Oracle, Cloudera and Qlik. Machine learning functions feature parallel model analysis with predictive analytics, along with the ability to automate workflows and various processes.
Read our in-depth review of AlteryxSee user reviews of AlteryxAWS SageMaker
The fully managed machine learning service allows data scientists and developers to build and train machine learning models and insert them into applications. This makes it possible to run parallel models, conduct flexible distributed training, and tackle A-B testing along with other tasks.
SageMaker is a fully managed AWS service that includes an integrated Jupyter authoring notebook instance. This aids in exploration and analysis by offering deep visibility into data sources. SageMaker offers a set of algorithms optimized for the environment and it supports proprietary algorithms and custom training scripts.
Sagemaker also features integration with Docker containers and Apache Spark. Together with AWS, it’s possible to use the integrated SageMaker console to launch and scale projects quickly. The platform supports HIPAA and other compliance standards.
Read our in-depth review of SageMakerSee user reviews of SageMakerGoogle Machine Learning Engine
Google Cloud Machine Learning (ML) Engine is a fully managed platform designed for developers and data scientists. The product allows users to construct high quality models using multiple machine learning frameworks, including scikit-learn, XGBoost, Keras, and TensorFlow.
The latter is a sophisticated deep learning framework that powers many Google products, from Google Photos to Google Cloud Speech. It allows users to deploy machine learning into production without the need for Docker containers. The highly scalable environment—which accommodates training on managed clusters, delivers a framework for automatically designing and evaluating model architectures. It is designed to accommodate existing workflows, and it scales to handle batch prediction and online prediction requirements.
Other key features include: automatic resource provisioning, the use of portable models, server-side processing and integrated cloud data storage.
Read our in-depth review of Google Machine Learning EngineSee user reviews of Google Machine Learning EngineIBM Watson Studio
IBM was a pioneer in the artificial intelligence (AI) and machine learning spaces. Today, Forrester ranks the company as a “Leader” among multimodal predictive analytics and machine learning solutions providers.
IBM Watson Studio is designed to accommodate a variety of independent platforms and various types of power users. This includes data engineers, app developers and data scientists. The result is strong collaboration capabilities. Among its top features: a robust architecture, strong algorithms and a powerful ability to execute machine learning.
The Watson Studio is part of an extensive collection of technologies and solutions that are available under the IBM Watson umbrella. It is designed to accommodate IBM’s SPSS modeling workflows but also plug in open source machine learning libraries and notebook-based interfaces. The solution is available in the cloud on or on the desktop. It incorporates visual drag-and-drop tools.
Read our in-depth review of IBM Watson StudioSee user reviews of IBM Watson StudioMicrosoft Azure Machine Learning Studio
Azure Machine Learning Studio has emerged as a leading solution in the managed cloud space. It delivers a visual tool that aids developers, data scientists and non-data scientists in designing machine learning pipelines and solutions that address a wide variety of tasks. The platform offers a browser-based, visual drag-and-drop authoring environment that requires no coding. Gartner ranks Microsoft a “Visionary” in its MQ. The solution offers a high level of flexibly, extensibility and openness.
Azure ML Studio also delivers comprehensive capabilities across the full range of descriptive, diagnostic, predictive and prescriptive analytic types. Microsoft is continuing to expand the features and capabilities within Azure Machine Learning. This includes promoting deep learning through the Microsoft Cognitive Toolkit (formerly CNTK) as well as the joint ONNX open standard for neural networks. Microsoft also plans to automate a growing number of functions within the Azure Machine Learning platform.
Read our in-depth review of Microsoft Azure Machine Learning StudioSee user reviews of Microsoft Azure Machine Learning StudioRapidMiner
RapidMiner offers data prep, machine learning and model deployment tools that deliver deep insight into data. It delivers a platform that is designed to tackle a variety of big data and analytics requirements.
In the machine learning space, the company has established itself as a heavyweight. It delivers a simple-to-use visual environment that incorporates automated model creation along with suggestions, guides and information about what approach might work best for a given situation. RapidMiner’s features and high level of flexibility have established it as a popular choice among machine learning solutions.
Data scientists and non-data scientists alike can easily inspect and customize projects and build on results. RapidMiner is ranked as a “Leader” in both the Gartner MQ and the Forrester Wave. It is among the highest rated data and machine learning solutions at Gartner Peer Insights.
Read our in-depth review of RapidMinerSee user reviews of RapidMinerSAP Leonardo
Leonardo serves as an umbrella for a variety of SAP data products and services. It incorporates AI and machine learning through a cloud platform. This includes a machine learning foundation that allows developers, data scientists and others to create, run and deploy algorithms and machine learning apps.
The technology extracts data from text, images, natural language and other media to generate computer-based predictions. The highly scalable platform includes predefined and pretrained machine learning services, but it also incorporates TensorFlow models. This allows users to adopt a flexible framework to training and tuning machine learning. Forrester ranks SAP as a “strong performer” in the overall predictive analytics and machine learning category and cites the breadth and depth of service as a strong point.
However, Gartner rates the company a “Niche Player” in its MQ. It noted that SAP has made considerable strides in the data and machine learning spaces but still lags behind others in terms of features and capabilities.
Read our in-depth review of SAP LeonardoSee user reviews of SAP LeonardoSAS Visual Data Mining and Machine Learning
A pioneer in the data mining and analytics spaces, SAS has established itself as a top choice among machine learning vendors. It offers Visual Data Mining and Machine Learning (VDMML), which ranked at the standout “Leader” in the Forrester Wave and as a top “Leader” in the Gartner MQ, though the research firm notes that the overall platform has lost some ground in terms of vision and ability to execute.
VDMML connects to numerous other products and data sources. Forrester noted that SAS has emerged as the first truly multimodal PAML solution. It uses a wizard to automatically train a model, convert it to code and plug in integrated features for automated machine learning. The unified environment also includes robust visual tools for creating models as well as deep neural networks.
In addition, data scientists can embed snippets of code directly into SAS programs to further enhance capabilities, including machine learning and deep learning output.
Read our in-depth review of SAS Visual Data Mining and Machine LearningSee user reviews of SAS Visual Data Mining and Machine LearningData Science and Machine Learning Vendors at a Glance:
Vendor and features
Google Machine Learning Engine
IBM Watson Studio
Microsoft Azure Machine Learning
SAS Visual Data Mining and Machine Learning
Analytics and ML platform that allows users to build models in a single workflow
Hosted platform that supports supervised and unsupervised ML and deep learning.
Hosted platform that runs ML training jobs and predictions at scale.
Broad data science focus with cloud and desktop ML platforms.
Automated ML platform running on hosted Azure Cloud.
Highly automated ML platform suited for firms aiming to use machine learning broadly.
Broad and deep data science and ML platforms.
Multimodal PAML solution with powerful wizards to support data mining and ML. Strong automation.
Key Features and capabilities
Offers more than 250 code free pre-built tools and supports open source tools and scripting.
Zero-setup integrated Jupyter authoring notebook instance. Access to rich array of open source frameworks and tools.
Automatic resource provisioning and monitoring for CPUs, GPUs and TPUs. Strong open source support for tools and scripting languages.
Strong visual recognition and natural classification tools. Flexible approach that incorporates open source tools. Connects to IBM SPSS Modeler.
Strong descriptive, diagnostic, predictive and prescriptive analytics tools and capabilities. Strong support for open source ML tools and scripting.
Offers more than 1,500 machine learning and data prep functions, and it supports more than 40 files types. Connects to Amazon S3 and Dropbox.
Fully supports a BYOM approach. Strong open source integration. Runs in SAP’s HANA public cloud.
Includes best practice templates and in-memory processing that supports supervised and unsupervised learning.
High ratings for simplicity and usability. Complaints about the lack of robust reporting and visualization features.
High marks for the interface, configurability and usability. However, some say that the familiarity with AWS is crucial.
High ratings for functionality and integration.
Highly rated for features and capabilities. Some complaints revolving around using notebooks.
High ratings at Gartner Peer Review. Some complaints about support.
Among the highest rated data science and ML solutions. Users describe it as powerful and “revolutionary” though there are complaints about the lack of GPU support.
Customers give SAP high marks for usability and performance. Some complain about the lack of visualization tools for external data.
High ratings. Users like the powerful features and capabilities built into the platform, though some remark that there is a substantial learning curve associated with the product.
Pricing and licensing
Tiered system based on products used. Ranges from $5,195 to $33,800 per seat annually.
Complex tiered pricing model based on ML notebook instances and cloud resources consumed.
Based on resources and training hours used. Ranges from 1 cent per hour to $31 per hour for various services.
Tiered model from $99 per month per user to $6,000 per month per user or more at enterprise level.
Tiered pricing based on users and usage. Ranges from near zero to tens of thousands per user annually.
Tiered pricing ranging from $2,500 per user per year to upwards of $10,000 per user per year.
Tiered model based on node hours consumed in the cloud. Billing blocks are charge in increments of €7.25.
Data and big data analytics are fast becoming the lifeblood of any successful business. Getting the technology right can be challenging, but building the right team with the right skills to undertake big data initiatives can be even harder.
Not surprisingly, that challenge is reflected in the rising demand for big data skills and certifications. According to research by IT research firm Foote Partners, both noncertified advanced data analytics skills and certified big data skills have gained value in recent years: with 74 advanced data analytics related skills and certifications rising in average 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 found 123 related certified and noncertified big 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 analyze big data. They also need big data systems architects to translate requirements into systems, data engineers to build data pipelines, developers who know their way around Hadoop clusters and other technologies, and systems administrators and managers to tie everything together.
[ Find out the hottest data and analytics trends today. | Get ahead with the top certs for big data, project management, agile, and the cloud. | Get weekly career tips by signing up for our CIO Leader newsletter. ]
These skills are in high demand and are relatively rare. Individuals with the right mix of experience and skills can demand high salaries. The right certifications can help.
"Advanced data analytics capabilities are just too critical 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 critical focus for data and analytics services. IDC is predicting a 30 percent CAGR over the next five years, while McKinsey is expecting IoT to have a $4 trillion to $11 trillion global economic impact by 2025 as businesses look 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 big data skills, according to Foote Research, Foote believes pay premiums for both noncertified and certified skills will steadily rise over the next 12 to 24 months.
If you're looking for a way to get an edge — whether you're job hunting, angling for a promotion or just want tangible, third-party proof of your skills — big data certification is a great option. Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers that you have the right skillset. The number of big data certs is expanding rapidly.
Below is our guide to the most sought after big data certifications to help you decide which cert is right for you.
If you would like to submit a big data certification to this directory, please email us.Analytics: Optimizing Big Data Certificate
The Analytics: Optimizing Big Data Certificate is an undergraduate-level program intended for business, marketing and operations managers, data analyst and professionals, financial industry professionals, and small business owners. The program brings together statistics, analysis, and written and oral communications skills. It introduces students to the tools needed to analyze large 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 Big Data Analytics and Optimization (CPEE)
Offered in Hyderabad and Bengaluru, India, the Certificate in Engineering Excellence Big Data Analytics and Optimization is an intensive 18-week program that consists of 10 courses (lectures and labs) for students of all aspects of analytics, including working with big 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 series 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 general analytics certification that certifies end-to-end understanding of the analytics process, from framing business 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.
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 series 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 have 120 minutes to implement a technical solution for each task. They must analyze the problem and arrive at an optimal approach in the time allowed.
How to prepare: Cloudera recommends candidates take the Cloudera Data Analyst Training course, which has the same 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 tool such as Impala or Hive, others may require coding. Candidates have 120 minutes to complete the exam.
How to prepare: There are no prerequisites required, but Cloudera says the exam follows the same 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 ability to perform core competencies required to ingest, transform, store and analyze 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, large 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 all the requirements.
How to prepare: Cloudera suggests professionals seeking this certification have hands-on experience in the field and take the Cloudera Developer Training for Spark and Hadoop course.EMC Proven Professional Data Scientist Associate (EMCDSA)
The EMCDSA certification demonstrates an individual's ability to participate and contribute as a data science team member on big data projects. It includes deploying the data analytics lifecycle, reframing a business challenge as an analytics challenge, applying analytic techniques and tools to analyze big data and create statistical models, selecting the appropriate 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 – Big Data
Designed for data architects, the IBM Certified Data Architect – Big 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' business requirements into a big data solution.
Organization: IBM Professional Certification Program
How to prepare: IBM recommends a series of seven multi-day courses on SPSS Modeler to InfoSphere BigInsights to prepare for the test.IBM Certified Data Engineer – Big Data
The IBM Certified Data Engineer – Big Data certification is intended for big data engineers, who work directly with data architects and hands-on developers to convert an architect's big data vision into reality. Data engineers understand how to apply technologies to solve big data problems and have the ability to build large-scale data processing systems for the enterprise. They develop, maintain, test and evaluate big 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
How to prepare: IBM recommends a series 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 large datasets like the Web, social network graphs and large document repositories. The certificate usually takes one to two years to complete.
Organization: Stanford Center 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 have knowledge of basic computer science principles and skills, at a level 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 choice exam.
Organization: MongoDB University
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 have 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 choice exam.
Organization: MongoDB University
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 have registered for a certification exam.SAS Certified Big Data Professional
The SAS Certified Big Data Professional certification program is for individuals seeking to build on their basic programming knowledge by learning how to gather and analyze big data in SAS. The program focuses on SAS programming skills; accessing, transforming and manipulating data; improving data quality 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.Related articles Join the newsletter!
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