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M2090-733 IBM SPSS Statistics Sales Mastery Test v1

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M2090-733 exam Dumps Source : IBM SPSS Statistics Sales Mastery Test v1

Test Code : M2090-733
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IBM IBM SPSS Statistics Sales

IBM SPSS statistics: Product Overview and insight | killexams.com Real Questions and Pass4sure dumps

See the whole checklist of properly Predictive Analytics companies

See person experiences of IBM SPSS records

base line

The basic slogan "no person ever obtained fired for buying IBM" holds true for the predictive analytics tool SPSS information. although it trails behind market leader SAS in income, it's a solid, time-honored option. It offers an fantastic array of statistical algorithms and modeling capabilities. while it remains aimed toward facts scientists as opposed to usual company clients, the brand new interface is easier to use, and it integrates extra simply with Microsoft workplace.

in short, it be for corporations that need to do advanced, in-depth work, as opposed to discover short answers – even though SPSS is in a position to quick response and facile statistics mining.

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

In certain, giant organizations that already do enterprise with IBM are specifically the best option for IBM SPSS facts. The interoperability between this utility and different points of the IBM platform will also be expected to be seamless.

Product Description

one of the most most fulfilling-promoting predictive analytics tools in the marketplace, IBM SPSS information earns excessive marks for usability with its these days more advantageous drag-and-drop interface. Now on edition 25, it be a mature, finished device that allows you to run advanced analytics and create visualizations from the same interface. multiple licensing and deployment alternatives can be found, as is a free 14-day trial.

Key points
  • data training
  • Bootstrapping
  • advanced statistics
  • Regression
  • custom tables
  • lacking values
  • classes
  • complicated samples
  • Conjoint evaluation
  • exact exams
  • Forecasting
  • decision tree
  • Direct advertising
  • Neural networks
  • variants obtainable

    Base, usual, expert and premium.

    Integrations

    Extensions enable integration with R and Python, and it also integrates with Microsoft development equipment and Excel.

    birth

    Cloud or desktop software.

    goal Market

    SMBs and businesses, as well as govt, schooling and healthcare businesses.

    Pricing and how to purchase

    Base subscriptions and add-ons can be found to buy online, with a free 14-day trial available. other styles of licenses require contacting IBM. pupil and college discounts are available.

  • Base Subscription $99 per user monthly
  • custom Tables & improve records clients Add-on $seventy nine per user per thirty days
  • complex Sampling & testing clients add-on $79 per person per month
  • Forecasting and choice timber users add-on $seventy nine per user monthly
  • Base v25 $1,295 per license per 12 months
  • ordinary v25 $2,850 per license per 12 months
  • knowledgeable v25 $5,730 per license per year
  • top class v25 $8,540 per license per yr
  • IBM SPSS information features Chart

    IBM SPSS information

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    Base, general, expert and top rate

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    ·   superior records

    ·   Regression

    ·   customized tables

    ·   lacking values

    ·   classes

    ·   complicated samples

    ·   Conjoint evaluation

    ·   actual tests

    ·   Forecasting

    ·   decision tree

    ·   Direct marketing

    ·   Neural networks

    Integrations

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    My Highlights from IBM consider 2018: information Science, SPSS, Augmented fact and the consumer event | killexams.com Real Questions and Pass4sure dumps

    I attended IBM’s inaugural think experience in Las Vegas remaining week. This event, IBM’s greatest (estimated 30,000+ attendees!), focused on making your business smarter and blanketed keynotes and periods on such themes as synthetic intelligence, statistics science, blockchain, quantum computing and cryptography. i used to be invited by using IBM as a guest to share some insights from the viewpoint of an information scientist. beneath are a number of highlights of the adventure.

    records Science the usage of IBM SPSS SPSS at 50

    50 Years of SPSS Innovation. click photo to amplify.

    IBM SPSS is IBM’s set of predictive analytics items that tackle the entire analytical method, from planning to facts collection to analysis, reporting and deployment. IBM celebrated the 50th anniversary of IBM SPSS with their new beta free up of IBM SPSS statistics 25, the biggest beta unlock in its historical past. The up to date version contains new developments like book-in a position charts, MS workplace integration, Bayesian statistics and advanced information. also, they added a brand new person interface which is fairly slick.

    i used to be added to SPSS records in college and have used it for each one of my research initiatives since then. To be sincere, SPSS facts has aged stronger than I have! I even have already begun using the new edition and am fairly excited in regards to the new elements and consumer interface. i'll report about journey in a later post. try SPSS with a free 14-day trial.

    enhancing the consumer event

    recent experiences have estimated that 45% of marketers are anticipated to raise the use of artificial intelligence for client event in the next three years, and fifty five% of agents are focused on optimizing the customer experience to boost client loyalty. moreover, eighty five% of all customer interactions with a company might be managed with out human interplay by way of 2020.

    consumer experience administration (CXM) is the manner of realizing and managing purchasers’ interactions with and perceptions concerning the business/company. IBM is aware that enhancing the customer experience is more and more becoming information-intensive recreation, and the use of the mixed power of statistics and nowadays’s processing capabilities can aid businesses model the procedures that have an impact on the consumer journey. I attended a few periods to learn about how IBM is leveraging the energy of IBM Watson to aid their purchasers with Watson Commerce and Watson customer event Analytics solutions. These options use the energy of synthetic intelligence (e.g., predictive analytics) to increase how agencies can stronger manage customer relationships to enhance consumer loyalty and move their company ahead.

    information Science Meets more suitable Analytics and Augmented reality

    These facts gurus from Aginity, IBM Analytics, H2O.ai and IBM Immersive Insights are improving how you get from records to insights.

    I noticed a very good demonstration of the intersection of information science, better analytics and augmented reality. Getting from records to insights is the purpose of facts science efforts and, as records sources proceed to develop, they are able to want more advantageous the right way to get to those insights. Aginity is working with H2O.ai to demonstrate the way to enrich your predictions by means of augmenting public data with enhanced information (with derived attributes) and enhanced analytics to make greater predictions. the usage of baseball statistics, Ari Kaplan of Aginity pointed out that the improvements in predictive fashions may translate into thousands and thousands of dollars per participant. whereas his demo concentrated on the use of those technologies in baseball information, the concepts are generalizable to any industry vertical, including finance, healthcare and media.

    on the equal demonstration station, Alfredo Ruiz, lead of the Augmented reality software at IBM Analytics, showed me how his crew (IBM Immersive Insights) is incorporating augmented reality into records Science journey to support corporations more suitable have in mind their ever-expanding facts sets. I’m eager for seeing how his efforts in marrying augmented fact and facts science progress.

    I had the privilege of interviewing Ari Kaplan of Aginity who talked in regards to the work he is doing to increase how Aginity and H2O.ai is improving the records science manner. take a look at what he has to assert under.

    Don’t leave out this interview with Ari Kaplan, a true “Moneyball” and neatly familiar around most important League Baseball, as he talks about the latest laptop researching applied sciences powering today’s baseball decisions, and take a look at the remarkable demo.

    Posted by IBM information Science on Thursday, March 22, 2018

    statistics Science is a crew recreation

    Bob, Al and Dez. graphic by means of Dez Blanchfield

    I had the chance to talk with with many industry experts who come to facts science from a different point of view than I do. whereas I focus essentially on the facts and arithmetic elements of statistics science, lots of my information friends strategy information science from a technological and programming perspective. in fact, for an upcoming podcast, Dez Blanchfield and that i have been interviewed with the aid of Al Martin of IBM Analytics to focus on their respective roles in data science. This conversation become a active one, and that i am anticipating reliving that evening once the podcast is launched. The final analysis is that data science requires such a diverse ability set that you simply really want to work with other individuals who can complement your capabilities.

    I’m with statistics professionals (and actors) Trisha Mahoney, Ryan Arbow and Shadi Copty.

    This inspiration that statistics science is a team recreation became placed on full display in an interesting session through which a couples therapist (Trisha Mahoney) helped unravel an argument between an information science chief (Shadi Copty) and IT chief (Ryan Arbow). Asking probing questions, the counselor revealed that the facts science and IT leader were at odds because of an absence of communique. She brought them to IBM’s records Science experience, an enterprise statistics science platform that enables them to easily collaborate, use accurate open supply equipment and get their fashions into creation quicker.

    Analytics: Your aggressive expertise

    For me, IBM suppose 2018 was all about making your business smarter through analytics. basically, analysis shows that businesses which are superior able to deliver the vigour of analytics to bear on their enterprise problems should be in a higher place to outperform their analytics-challenged rivals. This theory turned into illustrated through keynotes, periods and conversations. through bringing diverse statistics science specialists together to leverage the tools and methods of AI and computer/deep discovering will assist you circulate your company forward. if you were unable to attend the event, that you would be able to watch replays of many of the keynotes right here.

    (Disclosure: IBM assisted me with shuttle fees to IBM suppose 2018.)


    IBM to cease supporting SPSS facts component on IBM i | killexams.com Real Questions and Pass4sure dumps

    IBM remaining week announced that the version 22 release of SPSS information could be the remaining release of the product to have any part that runs on device i (IBM i). it'll also be the remaining free up with the capability to pull SQL statistics out of the DB2 for i database, which is arguably extra harmful for any IBM consumers that might want to run some statistical evaluation on their DB2/four hundred data.

    SPSS statistics is mature and multi-faceted software kit that offers clients loads of advanced statistical evaluation capabilities. Surrounding the core analysis part is a fleet of more than 50 add-on products that, among different things, provide record distribution capabilities.

    SPSS facts has not ever run on IBM i itself, even when it was owned by using SPSS. before it changed into purchased by way of IBM, SPSS supported the IBM i platform with just two main product lines: the show off OLAP and Reporting tools (some of which have been got by using aid/techniques) and the Clementine statistics mining software. but the core SPSS facts kit become at all times supported on greater “mainstream” structures, like Unix, Linux, home windows, and the S/390 mainframe, which is where SPSS facts obtained its delivery so a long time in the past.

    despite the fact, IBM did, curiously, support the SPSS Collaboration and Deployment features component on the IBM i platform, which is exciting in its personal right. IBM infrequently (if ever) touted this skill, which isn't brilliant considering that it’s a narrowly concentrated area of interest product that plays a support role to other items. In any event, IBM introduced that the Collaboration and Deployment functions component in SPSS statistics version 22 will now not guide the “gadget I” (a name that IBM stopped the use of in 2010).

    possibly extra harmful is the incontrovertible fact that SPSS facts version 22 marks the last version of the application that can be able to use DB2 for i (DB2/four hundred) as a source for SQL statistics, notwithstanding the core SPSS information software is operating (because it ought to) on an additional server platform.

    IBM i experts are aware of relocating construction data from DB2/400 onto other platforms where it will also be analyzed. however, apparently so few SPSS statistics clients were the usage of this skill that it now not made sense to retain it.

    obviously there are other ways to get statistics off the IBM i server and into SPSS information. in any case, the SQL records usual is meant to deliver better facts interoperability amongst structures. however the undeniable fact that it’s no longer value IBM’s effort to hold this SQL channel open to the IBM i server isn't a superb signal.

    For more suggestions see application Announcement 213-309.

    connected experiences

    There’s No “i” In French Open Tennis

    aid/programs Buys show off BI items from IBM

    IBM to purchase SPSS for $1.2 Billion

    SPSS changes data Miner’s identify, Drops system i help

                         submit this story to del.icio.us               publish this story to Digg    publish this story to Slashdot


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    IBM SPSS Statistics Sales Mastery Test v1

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    Embedded Analytics and Statistics for Big Data | killexams.com real questions and Pass4sure dumps

    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 they 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. They 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.

    Three entries in Table 1 stood out to us: R, Python, and D3 (Data Driver Documents). R is a language geared toward statistics. Python is a general-purpose programming language that has proven popular with scientists and researchers who want to do scientific and statistical computations. D3 is a JavaScript library that lets users create visualizations with which they can interact (for instance, by zooming in, zooming out, collapsing, and expanding) using a Web browser. R, Python, and D3 are well suited for embedded statistics for several reasons:

  • Because these are stand-alone programming languages, they’re relatively easy to interface with other systems via standard language mechanisms or by importing and exporting data in various formats.
  • Scripts in Python and R can be embedded directly into larger analytical workflows.
  • Python and R programs can be directly used to build applications that read data from various sources and interact directly with the user for analysis and visualization via the Web.
  • Through D3, users can elevate analytics to a higher level by interactively manipulating statistical graphics via Web browsers.
  • These are much closer to a programmer’s frame of mind than specialized statistical packages are.
  • 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.

    An Example

    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, they 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 [1]: import pandas as pd

    In [2]: 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 they enter

    In [3]: 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 they enter

    In [4]: 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 [5]: len(data[‘Indicator Name’].unique())Out[5]: 1289

    and second, how many countries there are

    In [6]: len(data[‘Country Name’].unique())Out[6]: 246

    Now they have a problem to solve: Are the indicators independent among themselves, or are some of them related to others?

    Because they measure indicators by year and by country, they must more precisely define the problem by de-ciding which parameters to retain as constant. In general, they get the best statistical results as their samples increase. It makes sense then to rephrase the problem: For the year in which they have most measurements, are the most measured indicators independent among themselves, or as some of them related to others? By “most measured indicators,” they mean those that have been measured in more countries. It turns out that they 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, they give the number of most measured indicators that they would like to examine. In line 15, they 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). They then remove all data for which measurements aren’t available. In lines 20–26, they get the most measured indicators.

    The actual statistical calculations start from line 28, where they prepare a table of ones to hold the result of the correlation values between each pair of indicators. In the loop that follows, they calculate each pairwise correlation and store it in the table they prepared. Finally, in lines 41–52, they display the results on screen and save them to a PDF file (see Figure 2). They 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, they 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 they 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 they 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 their statistical package. The package must have some means of manipulating and querying data so that they can get the data subsets that they 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. They 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, they would see a warning sign whenever they 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. Their 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 louridas@grnet.gr or louridas@aueb.gr.

    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 christof.ebert@vector.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.


    Bosses Who Donate to Republicans Are Less Likely to Promote Female Staff | killexams.com real questions and Pass4sure dumps

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    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 they 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. 


    Your guide to the top big data certifications today | killexams.com real questions and Pass4sure dumps

    IDG

    IDG

    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 their 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 their 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.

    Organization: INFORMS

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

    How to prepare: A list of study courses and a 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.

    Organization: Cloudera

    Price: $295

    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.

    Organization: Cloudera

    Price: $295

    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.

    Organization: Cloudera

    Price: $400

    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

    Price: $200

    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

    Price: $200

    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

    Price: $150

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

    Price: $150

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

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    References :


    Issu : https://issuu.com/trutrainers/docs/m2090-733
    Dropmark : http://killexams.dropmark.com/367904/11445738
    Wordpress : http://wp.me/p7SJ6L-h0
    Scribd : https://www.scribd.com/document/356941796/Pass4sure-M2090-733-Braindumps-and-Practice-Tests-with-Real-Questions
    weSRCH : https://www.wesrch.com/business/prpdfBU1HWO000ZBOO
    Dropmark-Text : http://killexams.dropmark.com/367904/12025388
    Youtube : https://youtu.be/JxfJbR8oElU
    Blogspot : http://killexams-braindumps.blogspot.com/2017/10/review-m2090-733-real-question-and.html
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    Vimeo : https://vimeo.com/241508091
    publitas.com : https://view.publitas.com/trutrainers-inc/ensure-your-success-with-this-m2090-733-question-bank
    Google+ : https://plus.google.com/112153555852933435691/posts/NyGVnesy8x8?hl=en
    Calameo : http://en.calameo.com/account/book#
    Box.net : https://app.box.com/s/xyjffuymxbqmuhp73sdfb33nkaf93638
    zoho.com : https://docs.zoho.com/file/2q0x2827c63f0e1ac4299a6256a98f88e6f6c






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