SPS-202 exam Dumps Source : IBMSPSSMBPDM- IBM SPSS Modeler Business(R) colleague Data Mining Associate
Test Code : SPS-202
Test denomination : IBMSPSSMBPDM- IBM SPSS Modeler Business(R) colleague Data Mining Associate
Vendor denomination : IBM
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down load the authoritative e-book: Cloud Computing 2019: the usage of the Cloud for aggressive advantage
See the gross checklist of computing device gaining knowledge of SolutionsSee consumer experiences of IBM Watson Studiobottom line
Watson is an umbrella for totality IBM deep discovering and artificial intelligence, in addition to laptop learning. The company became a pioneer in introducing AI technologies to the business world. What this potential for patrons: Watson Studio is a exact contender for any company looking to deploy laptop studying and deep researching applied sciences.
The platform gives extensive gear and technologies for facts scientists, builders and province depend consultants that necessity to determine information, build models, and educate and set up computer gaining knowledge of fashions at scale. The acknowledge contains gear to participate visualizations and results with others. Watson Studio helps cloud, computer and local deployment frameworks.
The latter resides behind a firm’s firewall or as a SaaS solution working in an IBM deepest cloud. IBM Watson Studio is ranked as a “chief” within the Forrester Wave. It turned into a customers’ option 2018 recipient at Gartner Peer Insights.Product Description
Watson Studio relies on a group of IBM tools and applied sciences to construct efficient computing device getting to know purposes and services. This comprises IBM Cloud pretrained computing device studying fashions corresponding to visible recognition, Watson herbal Language Classifier, and others. The environment makes exercise of Jupyter Notebooks together with other open supply tools and scripting languages to complement constructed-in collaborative undertaking points.https://o1.qnsr.com/log/p.gif?;n=203;c=204660772;s=9478;x=7936;f=201812281334210;u=j;z=TIMESTAMP;a=20403954;e=i
The outcome is an ambiance that enables quick and powerful laptop gaining knowledge of pile and attribute tuning of models. facts scientists and others can build a selection from a number of capacities of Anaconda, Spark and GPU environments.
Watson Studio supports better visual modeling through a drag-and-drop interface supplied by using IBM’s SPSS Modeler. furthermore, it accommodates automatic deep researching the exercise of a drag-and-drop, no-code interface in Neural community Modeler.Overview and lines consumer Base
statistics scientists, builders and district recollect consultants.Interface
Graphical drag-and-drop and command line.Scripting Languages/formats Supported
supports Anaconda and Apache Spark. The latter offers Scala, Python and R interfaces.codecs Supported
Most major information and file codecs are supported through open supply Jupyter Notebooks.Integration
IBM Watson Studio connects a yoke of IBM items, together with SPSS Modeler and records Science event (DSX) together with open supply tools, with a view to bring a sturdy Predictive Analytics and laptop getting to know (PAML) solution.
The ambiance accommodates open data sets through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud edition features interactivity with computer servers and R Studio, together with Python, R., and Scala coder for facts scientists.Reporting and Visualization
Visualization via SPSS Modeler. sturdy logging and reporting capabilities are developed into the product.Pricing
IBM has adopted a pay-as-you-go model. Watson Studio Cloud – impartial expenses $ninety nine per month with 50 means unit hours per month covered. Watson Studio Cloud - business runs $6,000 per month with 5,000 capability unit hours. Watson Studio laptop charges $199 per thirty days with limitless modeling. Watson Studio local – for enterprise records science teams N/A.IBM Watson Studio Overview and contours at a look:
seller and contours
IBM Watson Studio
wide information science hub of attention with cloud and desktop ML structures.
Key features and capabilities
powerful visible awareness and herbal classification equipment. elastic strategy that accommodates open source tools. Connects to IBM SPSS Modeler.
particularly rated for points and capabilities. Some complaints revolving round using notebooks.
Pricing and licensing
Tiered mannequin from $99 per month per user to $6,000 per 30 days per consumer or more at enterprise level.
Two of IBM’s most established evaluation products, the Cognos company Intelligence and the SPSS predictive analytics equipment, are headed for the cloud, the newest in an ongoing thrust by using IBM to port its substantial utility portfolio to the cloud.
having access to this sort of application from a hosted atmosphere, in Place of procuring the gear outright, offers a number of advantages to purchasers.
“We manage the infrastructure, and this permits you to scale more effortlessly and glean totality started with less upfront investment,” pointed out Eric Sall, IBM vice president of global analytics marketing.
IBM introduced these additions to its cloud functions, as well as a few fresh choices, at its perception user conference for records analytics, held this week in Las Vegas.
by 2016, 25 p.c of recent enterprise analysis deployments can subsist accomplished within the cloud, in keeping with Gartner.
Analytics might champion groups in lots of techniques, in line with IBM. It may supply further insight in the procuring habits of consumers, as well as insight into how neatly its personal operations are performing. It could succor safeguard systems from assaults and attempts at fraud, as well as guarantee that business departments are assembly compliance requirements.
the fresh online edition of Cognos, IBM Cognos enterprise Intelligence on Cloud, can at the moment subsist validated in a preview mode. IBM plans to present Cognos as a full business provider early next year. clients can hasten Cognos towards statistics they retain in the IBM cloud, or towards records they redeem on premises.
A full business edition of the online IBM SPSS Modeler will subsist purchasable within 30 days. This gear will encompass totality the SPSS add-ons for records based predictive modeling, akin to a modeler server, analytics decision administration software and a information server.
past this year, IBM pledged to tender a gross lot of its software portfolio as cloud services, many via its Bluemix set of platform functions.
besides Cognos and SPSS, IBM furthermore unveiled a number of fresh and up-to-date offerings at the conference.
One fresh carrier, DataWorks, gives a yoke of options for refining and cleaning facts so it's equipped for evaluation. The company has launched a cloud-based data warehousing carrier, called dashDB. a brand fresh Watson-primarily based provider, called Watson Explorer, offers a artery for users to question natural language questions on diverse units of interior information.To touch upon this text and other PCWorld content material, visit their fb page or their Twitter feed.
IBM Watson Studio is an exceedingly efficient information science offering. if you’re now not typical, Watson Studio is an built-in environment designed to build it simple to increase, educate, and exploit models. It comprises a large selection of gear used for every runt thing from records prep and mix to picture consciousness. And now Watson Studio is on its solution to your windows or Mac desktop computer.Overview of Watson Studio
Watson Studio contains lots of tools totality inside one built-in atmosphere. the most regularly occurring gear of Watson Studio are:
Watson Studio will quickly subsist purchasable as three deployment options:
Watson Studio desktop is their fresh third deployment option inside the Watson Studio family. It takes essentially the most frequent parts of what's available inside Watson Studio on the cloud and ports it to your windows or Mac computing device for offline use, for evaluation of information data you don’t are looking to upload, and for snappy performance. totality with a expeditiously and simple desktop installing.
Watson Studio on the computer will scale from someone user totality of the artery as much as a commodious team, and will subsist a fine addition to the Watson Studio family together with their cloud and indigenous flavors.
The initial leading features of Watson Studio computing device can subsist a SPSS Modeler canvas in conjunction with the statistics refinery spreadsheet editor. this will encompass backwards compatibility for SPSS Modeler streams.merits of Watson Studio laptop
The Beta for Watson Studio is nascence now with two gear already ported to the computing device: a refreshed edition of SPSS Modeler & facts Refinery. it'll furthermore consist of tasks to prepare your work, statistics assets organization, and a effortless check for updates function.
be fraction of the beta to supply comments on the artery you like these tools, and advocate which other tools they carry to the computer. The Beta is fraction of the IBM SPSS Modeler Subscription Trial, so when you register for that you just’ll possess access to both downloads.
After you register, search for the “IBM SPSS Modeler Subscription Trial” card, click on down load, then you definately’ll notice the Watson Studio laptop Beta downloads on the commandeer hand facet of the page.join the discussion on the beta neighborhood
After registering for the beta, subsist a fraction of within the discussion on the Watson Studio computing device beta neighborhood. here that you would subsist able to question questions concerning the beta, supply strategies for future aspects, glean forum assist, and bewitch fraction in valuable surveys to possess an repercussion on product direction. subsist a fraction of the discussion board privilege here by means of logging in with your IBMid.
wait for future announcements privilege here about this pleasing addition for the Watson Studio household.Watson Studio computer chore organizer
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Data and commodious data analytics are expeditiously becoming the lifeblood of any successful business. Getting the technology privilege can subsist challenging, but pile the privilege team with the privilege skills to undertake commodious data initiatives can subsist even harder.
Not surprisingly, that challenge is reflected in the rising claim for commodious data skills and certifications. According to research by IT research firm Foote Partners, both noncertified advanced data analytics skills and certified commodious data skills possess gained value in recent years: with 74 advanced data analytics related skills and certifications rising in impartial 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 commodious 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 commodious data. They furthermore necessity commodious data systems architects to translate requirements into systems, data engineers to build data pipelines, developers who know their artery around Hadoop clusters and other technologies, and systems administrators and managers to tie everything together.
[ Find out the hottest data and analytics trends today. | glean ahead with the top certs for commodious data, project management, agile, and the cloud. | glean weekly career tips by signing up for their CIO Leader newsletter. ]
These skills are in high claim and are relatively rare. Individuals with the privilege mix of suffer and skills can claim high salaries. The privilege 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 possess a $4 trillion to $11 trillion global economic repercussion by 2025 as businesses glance to IoT technologies to provide more insight."
While the market value of noncertified advanced analytics skills has actually increased faster as a percentage of ground salary than the value of certified commodious 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 artery to glean an edge — whether you're job hunting, angling for a promotion or just want tangible, third-party proof of your skills — commodious data certification is a worthy option. Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers that you possess the privilege skillset. The number of commodious data certs is expanding rapidly.
Below is their pilot to the most sought after commodious data certifications to succor you resolve which cert is privilege for you.
If you would like to submit a commodious data certification to this directory, please email us.Analytics: Optimizing commodious Data Certificate
The Analytics: Optimizing commodious Data Certificate is an undergraduate-level program intended for business, marketing and operations managers, data analyst and professionals, monetary industry professionals, and wee business owners. The program brings together statistics, analysis, and written and oral communications skills. It introduces students to the tools needed to dissect large datasets, covering topics including importing data into an analytics software package, exploratory graphical and data analysis, pile 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 commodious Data Analytics and Optimization (CPEE)
Offered in Hyderabad and Bengaluru, India, the Certificate in Engineering Excellence commodious Data Analytics and Optimization is an intensive 18-week program that consists of 10 courses (lectures and labs) for students of totality aspects of analytics, including working with commodious 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 drudgery 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 furthermore 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 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 possess 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.
How to prepare: Cloudera recommends candidates bewitch 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 unravel a particular scenario. Some cases may require a utensil such as Impala or Hive, others may require coding. Candidates possess 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 execute 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, 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 totality the requirements.
How to prepare: Cloudera suggests professionals seeking this certification possess hands-on suffer in the province and bewitch 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 commodious data projects. It includes deploying the data analytics lifecycle, reframing a business challenge as an analytics challenge, applying analytic techniques and tools to dissect commodious data and create statistical models, selecting the commandeer 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 – commodious Data
Designed for data architects, the IBM Certified Data Architect – commodious 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 drudgery closely with customers and solutions architects to translate customers' business requirements into a commodious 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 – commodious Data
The IBM Certified Data Engineer – commodious Data certification is intended for commodious data engineers, who drudgery directly with data architects and hands-on developers to metamorphose an architect's commodious data vision into reality. Data engineers understand how to apply technologies to unravel commodious data problems and possess the ability to build large-scale data processing systems for the enterprise. They develop, maintain, test and evaluate commodious 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, sociable network graphs and large document repositories. The certificate usually takes one to two years to complete.
Organization: Stanford hub 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 possess knowledge of basic computer science principles and skills, at a flush 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 selection exam.
Organization: MongoDB University
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The MongoDB Certified Developer Associate credential is intended for software engineers who want to demonstrate a solid understanding of the fundamentals of designing and pile applications using MongoDB. It requires a 90 minute, multiple selection 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 furthermore provides the MongoDB Certification Exam Study Guide, available to those who possess registered for a certification exam.SAS Certified commodious Data Professional
The SAS Certified commodious Data Professional certification program is for individuals seeking to build on their basic programming knowledge by learning how to collect and dissect commodious data in SAS. The program focuses on SAS programming skills; accessing, transforming and manipulating data; improving data attribute 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 suffer in SAS or another programming language is required to enroll.Related articles Join the newsletter!
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You’re reading this article because you’re actively interested in text analytics technology and market trends. Whether you’re a solutions provider or consumer, you want to subsist confident you’ve invested your time and monetary resources wisely.
The picture is complex, however, as the text analytics domain is not dominated by any one algorithm or approach, vendor or business sector. So far, the diversity of options has benefitted potential users; awareness (and realization) of the technology’s capabilities is pile rapidly in areas such as media monitoring, publishing, customer-experience management and semantic search. Meanwhile, growth remains sturdy in domains that possess long applied text analytics. These areas comprehend life sciences, intelligence and law enforcement, and monetary services. While the text analytics domain isn’t unaffected by current business conditions, economic pressures could actually spur uptake motivated by a quest for efficiency through automation.Sources and Perspectives
To abide on top of trends, I try to talk to current and prospective users as often as I can, and I furthermore try to keep up with researchers and fellow analysts. (You’ll find links for some of the folks I result at my BeyeNETWORK channel.) I furthermore ensnare up with vendors periodically.
Because it’s helpful to understand current industry perspectives, I recently invited CEOs, CTOs and thought leaders to respond to the following query:
What execute you notice as the 3 (or fewer) most valuable text analytics technology, solution or market challenges in 2009?
Before relaying answers I received, I’ll mention two meta-replies. First, Marti Hearst, associate professor in the UC Berkeley School of Information, declined to respond, in fraction because “…for me, 2009 is too nearby a time horizon.” This thought applies not only to academic researchers but furthermore to anyone who’s thinking strategically, beyond this year’s crop of product releases and business conditions. Next, Lexalytics CEO Jeff Catlin candidly characterized his response as “somewhat self-serving thoughts.” No doubt many of the vendors quoted notice the greatest challenges as residing in areas their tools address, so bewitch their views with a grain of salt.
I’ll relay totality views with minimal editing, complete with vendor self-promotion because, after all, commercial products are “where the rubber hits the road” for most enterprise users.Text Analytics in 2009
Let’s start with Maria Milosavljevic, CTO at Capital Markets Cooperative Research Centre, an Australian group that is conducting leading-edge R&D in real-time security-market surveillance. The top 3 for Maria are:
Breck Baldwin of Alias-i, author of LingPipe natural language processing software, is very to-the-point in his reply, with a sort of twist on Maria’s expectations response.
#1 thing the province needs is a profound and existent success narrative of text analytics.
The reply from Sid Banerjee, CEO of Clarabridge, looks at factors related to scaling text analytics to the enterprise, “the expansion of text analytics across a few dimensions.” His reply:
Aaron B. Brown of IBM similarly looks at architectural issues and furthermore at business concerns. Aaron is Program Director, ECM Discovery, for IBM Information Management Software and has worthy market and technical insights. I interviewed him last year on “Text Analytics for Legal Compliance.” That exchange is silent a worthy read. Here are his thoughts on challenges for 2009:
Other replies furthermore looked at business conditions and at experiences in the adoption and exercise of text technologies. Lexalytics CEO Jeff Catlin foresees:
Craig Norris, CEO at Attensity, similarly renowned three varieties of challenge, in his case relating to technology, implementation and seeing occasion in adverse business conditions:
Vendor prognosticators with a solutions focus comprehend Keith Collins, CTO at SAS, and Manya Mayes, SAS Chief Text Mining Strategist, notice as challenges for 2009:
Yves Schabes, President of Teragram, a SAS company, focuses his response on one particular challenge – increasing workers’ efficiency:
Given the current economic situation, large organizations are forced to keep up a expeditiously pace of business with fewer resources, and it is therefore critical that these organizations possess totality of their content organized correctly so that employees can expend more time fulfilling their jobs and less time searching for information on their enterprise's system.
Neil Hartley, CEO of Leximancer, sees “an industry that has been around for a long time and yet has seen runt in the artery of being operationalized within business processes.” Neil continues:
Yes, there are proofs of concept, hasten by experienced vendor staff but these [prototypes], in my experience, rarely glean adopted by the business a) because of the setup and maintenance required, and b) because the source data vocabulary changes making the initial setup redundant. Autonomy is the exception, but their deployments are largely search/retrieval-based and less qualitative.
What the business needs is a high degree of automation (without the necessity for immoderate setup and maintenance) together with clarity in the analysis and the ability to apply control to the process where needed. This is exactly what Leximancer provides.
The other major trend I notice is the necessity to build sociable media actionable, and this is something we’ve focused on heavily on their blog. I contemplate customer attitudes on sociable media or microblogging sites are a leading indicator for the business. The business that waits for these trends to subsist reflected in their formal feedback programs may find it is too late to bewitch efficient action.Technology and Applications
I had expected that a higher harmony of 2009 “challenges” responses would hub on the technology and its applications. Responses I’ve already quoted touch on those areas with perhaps greater attention to business and market concerns. Eric Martin, Product Marketing Manager at SPSS, focused solely on solutions, which to me reflects both Eric’s aplomb in his company’s market position and his own background: Eric earned a Ph.D. in immunology and cellular biology and, like many others, started using text mining on biomedical articles before joining text analytics pioneer LexiQuest, which was later acquired by SPSS. Challenge areas Eric sees for 2009 are:
Matthew Hurst of Microsoft Live Labs is likewise a techie at heart, and he’s furthermore very interested in user-generated and other online content. Matthew says, “As reported elsewhere in research and industry literature, the majority of textual data being published online today is from the many genres of sociable media. To fully leverage this data, the key challenges are:
Lastly, Ren Mohan, Co-Chairman and CTO of IxReveal, replied not only with the three challenges his company hears most often from their clients, but furthermore with a yoke of “megatrends” he is nascence to see. Challenges are:
Ren’s “megatrends” are:
Ren’s conclusion: “These are truly exciting times!” Indeed.A Live Challenge?
Here are thoughts on one more potential challenge for 2009, a live challenge, of a different variety.
As an aside while forwarding his bewitch on 2009 text-analytics challenges, Lexalytics’ Jeff Catlin suggested a bake-off to subsist held at the 2009 Text Analytics Summit, which is slated for June 1-2 (preceded by tutorials) in Boston. Jeff says in his blog, “Our market is silent dominated by too much flashy marketing and not enough down in the dirt numbers for ‘apples to apples’ comparisons.” Jeff is right, although I’ll add that I believe that most of the flashy marketing actually fronts very capable products. Perhaps they can glean something going for this year – a commercially oriented version of the TREC (Text REtrieval Conference) challenge. abide tuned, and execute account attending this year’s summit.
And if you’re a current or prospective text analytics user and would like to recount me about challenges you pan or anticipate to face, please execute glean in touch by email at firstname.lastname@example.org or by phone at (301) 270-0795).
SOURCE: Perspectives on Text Analytics in 2009
Seth is a business intelligence and decision systems expert. He is founding chair of the Text Analytics apex and principal consultant at Washington, D.C., based Alta Plana Corporation. Seth consults, writes, and speaks on information-systems strategy, data management and analysis systems, IT industry trends, and emerging analytical technologies. Seth chairs the Sentiment Analysis Symposium and the Text Analytics Summit.
Editor’s Note: More articles and resources are available in Seth's BeyeNETWORK Expert Channel. subsist confident to visit today!
Recent articles by Seth Grimes
Data scientists dependence on tools/products/solutions to succor them glean insights from data. Gregory Piatetsky of KDNuggets conducts an annual survey of data data professionals to better understand the different types of tools they use. Here are the results of the 2015 survey. He followed his initial posting with additional analyses to better understand which tools plug together. In the latter posting, he looked at pairs of tools and found that some tools watch to plug together (usage of tools are correlated with each other). He offered the anonymized raw data set for free to encourage other people to dissect the data, which I did.Dimension Reduction through Principal Components Analysis
His approach looked at pairs of tools to understand their relationship with the other. I took a slightly different approach. I applied principal components analysis. The current approach groups the tools by looking at the relationship among totality tools simultaneously. In general, principal components analysis examines the statistical relationships (e.g., covariances) among a large set of variables and tries to clarify these correlations using a smaller number of variables (components).
The results of the principal components analysis are presented in tabular format called the principal component matrix. The factor matrix is an NxM table (N = number of original variables and M = number of underlying components). The elements of a principal component matrix delineate the relationship between each of the variables and the components. These elements delineate the energy of relationship between the variables and each of the underlying components. The results of the principal components analysis recount us two things:
This exercise of principal components analysis is exploratory in nature. That is, I didn’t impose a pre-defined structure on the data itself. The pattern of relationships among the 95 tools drove the pattern of results. While human judgment comes into play in the determination of the number of components that best record the data, the selection of the number of components is based on the results. The goal of the current analysis was to clarify the relationship among the 95 tools with as few components as was necessary. Toward that end, there are a yoke of rules of thumb that I used to determine the number of components using the eigenvalues (output of principal components analysis). The first rule of thumb is to set the number of components based on the number of eigenvalues greater than union (1.0). Another artery is to plot (called a scree plot) the 95 eigenvalues to identify a limpid breaking point along the eigenvalues.
The plot of the eigenvalues appeared to atomize around the 13th and 14th eigenvalue. Therefore, I chose a 13-factor solution to clarify the relationships among the 95 data science tools.Table 1. Principal Component Matrix of 95 Data Science Tools – data from KDNuggets 2015 annual survey of data professionals. Click image to enlarge.
Based on a 13-factor solution, the principal component matrix (see Table 1) was partially simple to interpret. Some of the cell values in the matrix in Table 1 are bold to delineate values greater than .33. The components’ headings are based on the tools that loaded highest on that component. For example, four IBM products loaded highly on component 6, showing that usage of these tools by a given respondent watch to plug together (if you exercise one of IBM’s tools, you watch to exercise the other(s)); as a result, I labeled that component as IBM. Similarly, based on the tools that were highly related to the other 12 factors, the other 12 factors were labeled accordingly.Tool Groupings
The results suggest that 13 utensil grouping record the data. I’ve listed the groupings below and comprehend the tools that record each grouping (if number of votes are greater than 20). Tools that plunge within a specific group watch to subsist used together.
The exercise of specific commodious Data, data mining and data science tools watch to occur together. Based on the current analysis of utensil usage, the 90+ tools can subsist grouped into a smaller subsets of tools. The results of the current analysis are partially consistent with the prior results. For example, Piatetsky found that Pig usage was closely associate with Hadoop, Spark, scikit-learn, Unix shell/awk/gawk and Python. In the current analysis, they found that Pig was furthermore associated with Hadoop and Spark. However, they found that the exercise of Pig was associated with HBase, Hive, Mahout, MLlib, Scala and SQL on Hadoop. It’s valuable to note that the former analysis used the top 20 tools while the latter analysis used totality 90+ tools; this inequity could clarify the differences between the two analyses.
To help your chances of success in your commodious Data projects, it’s valuable that you select the privilege data tools. No unique utensil will execute it all, and data professionals watch to exercise more than one data-related utensil (in this study, they found that, on average, data professionals exercise 5 data tools). One artery to succor in your selection process is to identify the tools sets that other data professionals are using. The results suggest that you account looking at tools within components as potential candidates for tools you might consider.
Some of the utensil groupings are simply based on the parent company of the tools, including IBM, Microsoft and SAS. Perhaps cross-selling data science tools is a lot easier within a major brand than it is across different brands. This makes sense to the extent that a company’s products likely drudgery together better than with other vendors’ products. Still, more research is definitely needed to understand why unavoidable tools watch to subsist used together.
I’m interested in seeing what others find with these data. Here is the link to the anonymized data set (CSV format).
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