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I attended IBM’s inaugural believe event in Las Vegas closing week. This experience, IBM’s greatest (estimated 30,000+ attendees!), focused on making your company smarter and blanketed keynotes and classes on such issues as artificial intelligence, records science, blockchain, quantum computing and cryptography. i used to be invited with the aid of IBM as a guest to share some insights from the viewpoint of an information scientist. under are a number of highlights of the experience.statistics Science the usage of IBM SPSS
IBM SPSS is IBM’s set of predictive analytics products that tackle the complete analytical manner, from planning to information assortment to evaluation, reporting and deployment. IBM celebrated the 50th anniversary of IBM SPSS with their new beta release of IBM SPSS statistics 25, the biggest beta unencumber in its heritage. The up-to-date version comprises new developments like booklet-equipped charts, MS office integration, Bayesian information and superior records. also, they delivered a new consumer interface which is pretty slick.
i used to be introduced to SPSS data in college and have used it for every one of my research initiatives considering then. To be sincere, SPSS facts has aged more desirable than I even have! I even have already started the usage of the new version and am relatively excited in regards to the new facets and person interface. i will file about adventure in a later publish. check out SPSS with a free 14-day trial.improving the customer experience
recent experiences have estimated that 45% of retailers are anticipated to enhance using synthetic intelligence for consumer event within the next three years, and 55% of sellers are focused on optimizing the client event to raise consumer loyalty. moreover, eighty five% of all consumer interactions with a business will be managed without human interplay with the aid of 2020.
consumer journey administration (CXM) is the technique of realizing and managing valued clientele’ interactions with and perceptions about the enterprise/company. IBM understands that improving the client event is increasingly becoming information-intensive recreation, and the use of the mixed power of facts and nowadays’s processing capabilities can assist businesses mannequin the strategies that impact the consumer experience. I attended a couple of sessions to find out about how IBM is leveraging the energy of IBM Watson to aid their customers with Watson Commerce and Watson customer event Analytics options. These solutions use the vigour of synthetic intelligence (e.g., predictive analytics) to enrich how corporations can more desirable control customer relationships to enhance customer loyalty and movement their business ahead.information Science Meets more advantageous Analytics and Augmented fact
I noticed a pretty good demonstration of the intersection of records science, better analytics and augmented reality. Getting from information to insights is the aim of facts science efforts and, as information sources continue to develop, we can need greater how to get to those insights. Aginity is working with H2O.ai to exhibit find out how to enhance your predictions through augmenting public facts with stronger data (with derived attributes) and better analytics to make improved predictions. the usage of baseball information, Ari Kaplan of Aginity brought up that the advancements in predictive models could translate into hundreds of thousands of greenbacks per player. while his demo focused on the use of those applied sciences in baseball information, the concepts are generalizable to any business vertical, including finance, healthcare and media.
at the identical demonstration station, Alfredo Ruiz, lead of the Augmented reality software at IBM Analytics, confirmed me how his group (IBM Immersive Insights) is incorporating augmented reality into facts Science adventure to support agencies greater be mindful their ever-increasing data units. I’m eager for seeing how his efforts in marrying augmented fact and records science development.
I had the privilege of interviewing Ari Kaplan of Aginity who talked in regards to the work he's doing to enhance how Aginity and H2O.ai is improving the statistics science system. try what he has to assert below.
Don’t leave out this interview with Ari Kaplan, a true “Moneyball” and neatly generic round most important League Baseball, as he talks about the newest machine getting to know technologies powering these days’s baseball selections, and check out the super demo.
Posted by means of IBM information Science on Thursday, March 22, 2018statistics Science is a crew sport
I had the chance to talk with with many business consultants who come to statistics science from a different standpoint than I do. whereas I focus basically on the records and arithmetic features of facts science, lots of my data friends strategy information science from a technological and programming angle. in reality, for an upcoming podcast, Dez Blanchfield and that i had been interviewed by using Al Martin of IBM Analytics to focus on our respective roles in data science. This dialog turned into a lively one, and that i am looking forward to reliving that night as soon as the podcast is launched. The base line is that facts science requires such a diverse skill set that you simply actually need to work with other people who can complement your potential.
This inspiration that information science is a team game changed into placed on full screen in an entertaining session by which a couples therapist (Trisha Mahoney) helped unravel an argument between a knowledge science chief (Shadi Copty) and IT chief (Ryan Arbow). Asking probing questions, the counselor printed that the records science and IT chief had been at odds due to a lack of verbal exchange. She introduced them to IBM’s information Science adventure, an enterprise information science platform that allows them to with no trouble collaborate, use proper open supply tools and get their fashions into creation sooner.Analytics: Your competitive advantage
For me, IBM suppose 2018 changed into all about making your business smarter through analytics. truly, analysis shows that corporations that are more suitable in a position to carry the vigor of analytics to bear on their company complications may be in an improved position to outperform their analytics-challenged competitors. This thought become illustrated through keynotes, classes and conversations. through bringing diverse data science authorities together to leverage the equipment and strategies of AI and laptop/deep studying will aid you movement your business forward. in case you have been unable to attend the experience, you could watch replays of lots of the keynotes right here.
(Disclosure: IBM assisted me with commute costs to IBM believe 2018.)
IBM remaining week announced that the version 22 unencumber of SPSS information may be the closing liberate of the product to have any component that runs on system i (IBM i). it'll even be the final unlock with the potential to pull SQL records out of the DB2 for i database, which is arguably greater destructive for any IBM clients that might need to run some statistical analysis on their DB2/400 information.
SPSS facts is mature and multi-faceted application kit that gives clients quite a lot of advanced statistical evaluation capabilities. Surrounding the core evaluation element is a fleet of more than 50 add-on items that, amongst other issues, deliver document distribution capabilities.
SPSS records has not ever run on IBM i itself, even when it became owned by SPSS. earlier than it changed into bought with the aid of IBM, SPSS supported the IBM i platform with simply two main product traces: the exhibit OLAP and Reporting equipment (a few of which had been bought by support/programs) and the Clementine information mining application. however the core SPSS facts package changed into at all times supported on more “mainstream” structures, like Unix, Linux, home windows, and the S/390 mainframe, which is where SPSS data obtained its birth so many years in the past.
although, IBM did, apparently, assist the SPSS Collaboration and Deployment features component on the IBM i platform, which is interesting in its own correct. IBM rarely (if ever) touted this capability, which isn't brilliant on the grounds that it’s a narrowly concentrated area of interest product that plays a assist position to other items. In any adventure, IBM announced that the Collaboration and Deployment features component in SPSS data version 22 will now not help the “gadget I” (a reputation that IBM stopped using in 2010).
perhaps more damaging is the incontrovertible fact that SPSS information edition 22 marks the ultimate edition of the utility that can be in a position to use DB2 for i (DB2/four hundred) as a source for SQL statistics, in spite of the fact that the core SPSS statistics application is running (as it need to) on a further server platform.
IBM i specialists are aware of relocating construction information from DB2/four hundred onto other platforms the place it can also be analyzed. but, apparently so few SPSS facts customers had been using this ability that it no longer made sense to preserve it.
absolutely there are alternative routes to get information off the IBM i server and into SPSS records. in spite of everything, the SQL statistics common is supposed to bring more suitable information interoperability amongst platforms. however the proven fact that it’s now not value IBM’s effort to hold this SQL channel open to the IBM i server is not a good sign.
For greater tips see application Announcement 213-309.
There’s No “i” In French Open Tennis
aid/programs Buys showcase BI products from IBM
IBM to buy SPSS for $1.2 Billion
SPSS changes statistics Miner’s name, Drops gadget i help
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ARMONK, N.Y., March 10, 2017 /PRNewswire/ -- IBM (NYSE: IBM) these days announced it is proposing groups with greater self-provider analytics options that enable for advanced statistical capabilities for sooner choice making. IBM SPSS information Subscription gives companies the vigor of SPSS facts' predictive analytics capabilities with a versatile subscription fee option, including a less complicated buying, managing and licensing event.
With the introduction of SPSS statistics Subscription (SPSS facts), corporations, businesses and particular person users are actually in a position to leverage predictive analytics capabilities to deliver the maximum volume of price to the conclusion person. The simplicity of SPSS information resonates during the consumer experience – making it easier for every thing from downloading software, to managing licensing, to updating your software. further facets of SPSS records Subscription include:
SPSS statistics is the realm's leading statistical application used to remedy company and analysis issues through capacity of ad hoc analysis, speculation checking out, geospatial analysis, and predictive analytics. It also provides companies the potential to straight away take into account large and complex datasets through the use of superior statistical procedures that make sure excessive accuracy for deeper insights, bendy deployment alternate options and greater company agility.
IBM SPSS records Subscription may be obtainable for trial and buy on March 28 at https://www.ibm.com/us-en/market/spss-statistics-subscription.
Media Contact: Joseph Gallo IBM Media family members firstname.lastname@example.org
source IBMlinked hyperlinks
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This article first appeared in IEEE Software magazine and is brought to you by InfoQ & IEEE Computer Society.
Embedded analytics and statistics for big data have emerged as an important topic across industries. As the volumes of data have increased, software engineers are called to support data analysis and applying some kind of statistics to them. This article provides an overview of tools and libraries for embedded data analytics and statistics, both stand-alone software packages and programming languages with statistical capabilities. I look forward to hearing from both readers and prospective column authors about this column and the technologies you want to know more about. —Christof Ebert
Big data has emerged as a key concept both in the information technology and the embedded technology worlds.1 Such software systems are characterized by a multitude of heterogeneous connected software applications, middleware, and components such as sensors. The growing usage of cloud infrastructure makes available a wealth of data resources; smart grids, intelligent vehicle technology, and medicine are recent examples of such interconnected data sources. We’re producing approximately 1,200 exabytes of data annually, and that figure is only growing.2,3 Such a massive amount of unstructured data presents enormous and mounting challenges for business and IT executives.
Big data is defined by four dimensions: volume, source complexity, production rate, and potential number of users. The data needs to be organized to transform the countless bits and bytes into actionable information—the sheer abundance of data won’t be helpful unless we have ways to make sense out of it. Traditionally, programmers wrote code and statisticians did statistics. Programmers typically used a general-purpose programming language, whereas statisticians plied their trade using specialized programs such as IBM’s SPSS (Statistical Package for the Social Sciences). Statisticians pored over national statistics or market research usually only available to select groups of people, whereas programmers handled large amounts of data in databases or log files. Big data’s availability from the cloud to virtually everybody changed all that.
(Click on the image to enlarge it)
As the volumes and types of data have increased, software engineers are called more and more often to perform different statistical analyses with them. Software engineers are active in gathering and analyzing data on an unprecedented scale to make it useful and grow new business models.1 As an example, consider proactive maintenance. We can continuously monitor machines, networks, and processes to immediately detect irregularities and failures, allowing us to correct them before damage occurs or the system comes to a standstill. This reduces maintenance costs in both material cost as well as human intervention. Often, processing and making sense of data is just part of a bigger project or is embedded in some software, configuration, or hardware optimization problem. Luckily, the community has responded to this need by creating a set of tools that bring some of statisticians’ magic to programmers—in fact, these are often more powerful than traditional statistics tools because they can handle volumes that are scales of magnitudes larger than old statistical samples.Technologies for Embedded Analytics and Statistics
There’s a wealth of software available for performing statistical analysis; Table 1 shows the most popular ones. They differ in the statistical sophistication required from their users, ease of use, and whether they’re primarily stand-alone software packages or programming languages with statistical capabilities.
With the exception of D3, all entries in the table provide facilities for carrying out advanced statistics, such as multivariate and time-series analysis, either by themselves or via libraries. Each one, though, has a particular focus that will better suit working on a given target problem. Python’s Pandas package, for instance, has good support for time-series analysis because part of it was written to cater to such analysis regarding financial data.The Python Statistics Ecosystem
The most popular general-purpose programming language for doing statistics today is Python. It’s always been a favorite for scientific computation, and several excellent Python tools are available for doing even complex statistical tasks. The fundamental scientific library in Python is NumPy. Its main addition to Python is a homogeneous, multidimensional array that offers a host of methods for manipulating data. It can integrate with C/C++ and Fortran and comes with several functions for performing advanced mathematics and statistics. Internally, it primarily uses its own data structures, implemented in native code, so that matrix calculations in NumPy are much faster than equivalent calculations in Python. SciPy, which builds on top of NumPy, offers a number of higher-level mathematical and statistical functions. SciPy deals again with NumPy’s arrays; these are fine for doing mathematics but a bit cumbersome for handling heterogeneous data with possibly missing values. Pandas solves that problem by offering a flexible data structure that allows easy indexing, slicing, and even merging and joining (similar to joins between SQL tables). One attractive setup involves using iPython, an interactive Python shell with commandline completion, nice history facilities, and many other features that are particularly useful when manipulating data. Matplotlib can then visualize the results.
The World Bank is a trove of information, and it makes a lot of its data available over the Web. For more sophisticated analysis, the public can download data from the World Bank’s Data Catalog or access it through an API. The most popular dataset is the World Development Indicators (WDI). WDI contains, according to the World Bank, “the most current and accurate global development data available, and includes national, regional and global estimates.” WDI comes in two downloadable forms: Microsoft Excel and commaseparated values (CSV) files. (Because Microsoft Excel files aren’t suitable for programmatic analysis, we deal with the CSV files here.)
Figure 1. A program for calculating World Development Indicators correlations using Python. The program collects the top 30 most measured indicators, calculates the Spearman pairwise correlations, and shows the results graphically.
The WDI CSV bundle is a 42.5-Mbyte zipped archive. After downloading and unzipping it, you’ll see that the main file is called WDI_Data.csv. A good way to get an overview of the file contents is to examine it interactively. Because we’ll be using Python, the best way to interact with the tools that we’ll use is by launching a session of iPython, and then loading the data:
In : import pandas as pd
In : data = pd.read_csv(“WDI_Data.csv”)
The result, in data, is a DataFrame containing the data. Think of a DataFrame as a two-dimensional array with some extra features that allow for easy manipulation. In a DataFrame, data is organized in columns and an index (corresponding to the rows). If we enter
In : data.columns
we’ll get an output that shows the names of the columns: the country name, the code for that country, an indicator name, and an indicator code. These are followed by columns for each year from 1960 to 2012. Similarly, if we enter
In : data.index
we’ll see that the data contains 317,094 rows. Each row corresponds to the values of one particular indicator for one country for the years 1960 to 2012; years without values in a row indicate no measurement in that year for that indicator in that country. Let’s see first, how many indicators there are
In : len(data[‘Indicator Name’].unique())Out: 1289
and second, how many countries there are
In : len(data[‘Country Name’].unique())Out: 246
Now we have a problem to solve: Are the indicators independent among themselves, or are some of them related to others?
Because we measure indicators by year and by country, we must more precisely define the problem by de-ciding which parameters to retain as constant. In general, we get the best statistical results as our samples increase. It makes sense then to rephrase the problem: For the year in which we have most measurements, are the most measured indicators independent among themselves, or as some of them related to others? By “most measured indicators,” we mean those that have been measured in more countries. It turns out that we can find the answer to the question in about 50 LOC. Figure 1 contains the full program.
Lines 1–10 are imports of the libraries that we’ll be using. Line 11 reads the data. In line 13, we give the number of most measured indicators that we would like to examine. In line 15, we find the zero-based position of the first column with yearly measurements. After that, we’re able in line 17 to find the column with the most measurements (the year 2005). We then remove all data for which measurements aren’t available. In lines 20–26, we get the most measured indicators.
The actual statistical calculations start from line 28, where we prepare a table of ones to hold the result of the correlation values between each pair of indicators. In the loop that follows, we calculate each pairwise correlation and store it in the table we prepared. Finally, in lines 41–52, we display the results on screen and save them to a PDF file (see Figure 2). We take care to reverse the vertical order of the correlation matrix so that the most important indicator comes on the top of the matrix (lines 41 and 49).
The diagonal has perfect correlation—as it should, because we’re examining the same indicators. In addition to that, we do see that there are indicators that correlate with each other—some positively, even strongly so, and some negatively or very negatively.
Figure 2. World Development Indicators correlations matrix with Python created from the program in Figure 1.More Advanced Components in the Python Ecosystem
As Python has attracted interest from the research community, several specialized tools have emerged. Among them, Scikit-learn builds on NumPy, SciPy, and matplotlib and offers a comprehensive machine-learning toolkit. For very big datasets that follow a hierarchical schema, Python offers PyTables, which are built on top of the HDF5 library. This is a hot topic, and DARPA awarded US$3 million in 2013 to Continuum Analytics as part of the XDATA program to develop further Python data analytics tools. You can expect the ecosystem to keep evolving steadily over the next few years.The R Project for Statistical Computing
R is a language for doing statistics. You can think of Python bringing statistics to programmers and R bringing statisticians to programming. It’s a language centered on the efficient manipulation of objects representing statistical datasets. These objects are typically vectors, lists, and data frames that represent datasets organized in rows and columns. R has the usual control flow constructs and even uses ideas from object-oriented programming (although its implementation of object orientation differs considerably from the concepts we find in more traditional object-oriented languages). R excels in the variety of statistical libraries it offers. It’s unlikely that a statistical test or method isn’t already implemented in an R library (whereas in Python, you might find that you have to roll out your own implementation). To get an idea of what it looks like, Figure 3 shows the same program as Figure 1 and adopts the same logic, but using R instead of Python. Figure 4 shows the results.
Figure 3. A program similar to that in Figure 1 that calculates World Development Indicators correlations using R.Combining, Federating, and Integrating Embedded Analytics Technologies
The examples we give in this article are typical of the way different applications can be merged to handle big data. Data flows from the source (in some raw format) to a format acceptable to our statistical package. The package must have some means of manipulating and querying data so that we can get the data subsets that we want to examine. These are subject to statistical analysis. The results of the statistical analysis can be rendered in textual form or as a figure. We can perform this process on a local computer or via the Web (in which case data crunching and processing is performed by a server, and parameters, results, and figures go through a Web browser). This is a powerful concept, because a host of different settings, from an ERP framework to car diagnostic software, can export their data in simple formats like CSV—in fact, we would see a warning sign whenever we encounter a piece of software that doesn’t allow exporting to anything but closed and proprietary data formats.
To analyze your data in any way you will, you must first have access to it. So you should by all means select technologies that facilitate the exchange of data, either by simple export mechanisms or via suitable calls, for instance through a REST (representational state transfer) API.
Data is getting bigger all the time, so you must investigate whether the tool you’re considering will be able to handle your data. It’s not necessary for you to be able to process all the data in main memory. For instance, R has the big memory library, which lets us handle huge datasets by using shared memory and memory-mapped files. Also, make sure that the software package can handle not only big input but also big data structures: if table sizes are limited to 32-bit integers, for instance, you won’t be able to handle tables with 5 million entries.
In the examples above, the alert reader will have noticed that we’ve spent more code manipulating the data to bring it to the appropriate format for statistical analysis than on the statistical analysis per se, which was provided anyway by functions already written for us. Our examples were somewhat trite, so these ratios of preprocessing to actual processing might have been especially top-heavy, but the examples highlight the fact that data manipulation is usually as important (and demanding) as the analysis. In effect, real proficiency in R and NumPy/SciPy doesn’t come from mastery of statistics but from knowing how to work efficiently with the data structures they offer. And this is essentially work for programmers, not statisticians. Further reading is available elsewhere.4-7
Figure 4. World Development Indicators correlations matrix with R.References
1C. Ebert and R. Dumke, Software Measurement, Springer, 2007.2K. Michael and K.W. Miller, eds., Computer, vol. 46, no. 6, 2013.3T. Menzies and T. Zimmermann, eds., IEEE Software, vol. 30, no. 4, 2013.About the Authors
Panos Louridas is a consultant with the Greek Research and Technology Network and a researcher at the Athens University of Economics and Business. Contact him at email@example.com or firstname.lastname@example.org.
Christof Ebert is managing director at Vector Consulting Services. He’s a senior member of IEEE and is the editor of the Software Technology department of IEEE Software. Contact him at email@example.com.
This article first appeared in IEEE Software magazine. IEEE Software's mission is to build the community of leading and future software practitioners. The magazine delivers reliable, useful, leading-edge software development information to keep engineers and managers abreast of rapid technology change.
Are Republican-led companies less friendly to female employees? A look at large corporate law firms in the U.S. says yes.
For the study, business professors Seth Carnahan (University of Michigan) and Brad Greenwood (Temple University) examined individual political donations from partners at America's 200 largest law firms between 2007-2012, then weighed these against outcomes for female associates at the firm. The goal was to determine how much managers' personal political ideologies correspond to levels of organizational gender parity.
"In general, women are much less likely to be promoted, and much more likely to leave their firms," said Carnahan, an assistant professor of strategy at Michigan's Ross School of Business. "We found that this gender gap gets smaller when male bosses are more liberal, but it gets larger when male bosses are more conservative."
"Researchers have long argued that a manager's political ideology, situated on a liberal-conservative continuum and defined as a 'set of beliefs about the proper order of society and how it can be achieved,' can influence organizational outcomes such as investments in corporate social responsibility initiatives, targeting by LGBT activists, and allocation of resources among business units," the authors note in their paper. "If these preferences influence managerial decision-making, a manager's political ideology may drive considered choices or unconscious biases that have an important influence" on treatment and promotion of female employees.
As one way to test this, Carnahan and Greenwood explored merger-and-acquisition deals which U.S. law firms were involved in from 2007 through 2012—a sample that included 5,702 deals involving 16,860 partners and 18,215 associates at US law firms. Even after controlling for things such as an associate's number of years with a firm, their law-school ranking, shared law-school ties between associates and partners, and law-firm location, they found a "negative interaction between donations to Republicans and the selection of female associates" to serve with partners on client teams.
Compared to politically moderate partners, conservative male partners were 2.7 percent less likely than other partners to choose female associates for their deal teams. Liberal male partners were 0.8 percent more likely than moderates to choose a female associate for their teams.
The authors also pinpointed associates at America's top 200 law firms in 2006, and followed them until they received a promotion to partner or exited the company. Women made up about 45 percent of all associates. With or without controls factored in, Republican leadership in a practice area corresponded negatively to female promotion rates in that area and positively with turnover rates.
These conservative law-firm heads "are probably not consciously discriminating against women," Carnahan said in a statement, "but their beliefs could influence their willingness to invest in female subordinates. And this could happen on both sides of the spectrum. You could have conservative managers who don't promote women enough and you can have liberal managers who promote women more than they otherwise should."
"It is important to emphasize that we don't know the right level of diversity for each office, each organization," said Carnahan. "Our results should not be interpreted as 'anti-conservative' or 'pro-liberal.'"
In the paper, Carnahan and Greenwood suggest that "the most valuable opportunity for future work is to examine whether ideologically driven gender role preferences affect firm performance."
A rapidly growing body of research suggests that gender diversity in corporate ranks can be a boon for business performance. It's not necessarily a matter of women being superior managers, however, or there being some delicate, ideal gender divide within companies that makes things run more smoothly. As Carnahan and Greenwood note, both conservative managers who "under support" female staff and liberal managers who "over support" ideals of gender equality can be linked to less well-performing organizations or business units. The bottom line, they say, is that organizations are likely to pay for making hiring decisions based on "ascriptive preferences" rather than talent and skill.
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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