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E20-065 | Advanced Analytics Specialist Exam for Data Scientists

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E20-065 - Advanced Analytics Specialist Exam for Data Scientists - braindump

Vendor EMC
Exam Number E20-065
Exam Name Advanced Analytics Specialist Exam for Data Scientists
Questions 66 Q & A
Recent Update December 12, 2018
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E20-065 exam Dumps Source : Advanced Analytics Specialist Exam for Data Scientists

Test Code : E20-065
Test Name : Advanced Analytics Specialist Exam for Data Scientists
Vendor Name : EMC
Q&A : 66 Real Questions

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EMC EMC Advanced Analytics Specialist

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VMware enhances built-in hybrid cloud providing for more desirable deployment flexibility and selection, together with Kubernetes support | killexams.com Real Questions and Pass4sure dumps

VMware Inc. unveiled a new release of its built-in hybrid cloud providing to support purchasers more without difficulty deploy and function their cloud environments.

VMware brought Cloud basis three.5 to deliver customers with stronger deployment alternatives, new Kubernetes aid, and the newest improvements from VMware’s infrastructure and management portfolio. VMware Cloud foundation offers the quickest approach for clients to build an integrated hybrid cloud by means of providing a complete set of utility-described features for compute, storage, networking, safety, and cloud administration to run enterprise functions in hybrid cloud environments.

within the multi-cloud period, organizations are taking two tactics – hybrid cloud and native public cloud – to assist their increasing set of functions as well as their adoption of recent applied sciences such as computing device gaining knowledge of (ML), Kubernetes, and advanced analytics.

while every cloud model is wonderful, they're complementary, existing simultaneously inside the same corporation. At their core they share the normal force to bring creative capabilities lower back to the enterprise. VMware is investing in solutions that address multi-cloud environments, advancing the hybrid cloud alternatives from the information middle to the cloud to the part and addressing the wants of native public cloud operations.

VMware Cloud groundwork 3.5 will carry greater  flexibility in addition to guide a starting to be set of workloads and cloud initiatives. VMware Cloud groundwork 3.5 could be validated with Dell EMC vSAN capable Nodes on Dell EMC PowerEdge MX with further integration for composing compute, storage and community on this kinetic infrastructure expected in future releases.

This new release will convey composability points to VMware Cloud groundwork with HPE Synergy proposing clients the advantages of a composable deepest cloud infrastructure with even more simplicity and adaptability in managing SDDC environments.

additionally, the brand new unlock will characteristic integration with VMware NSX-T enabling the automated deployment and lifecycle management of the network virtualization utility as well as support for containerized applications and Kubernetes. VMware Cloud foundation 3.5 will construct on accurate of the wide set of recent capabilities and integrations delivered in VMware Cloud basis three.0 including the alternative of actual community switches (in comparison to outdated models that best supported a predefined checklist of supported switches).

VMware Cloud groundwork 3.5 will validate this latest unlock of VMware Cloud foundation with Dell EMC vSAN capable Nodes on Dell EMC PowerEdge MX, with improved integration expected over time. PowerEdge MX is Dell EMC’s kinetic infrastructure, which became designed for the utility-defined facts center, with customizable modules of compute, storage and networking. A confirmed, finished hyperconverged application solution, VMware Cloud foundation offers ecosystem help for all VMware vSAN ReadyNodes.

It also gives aid for composable features could be delivered through deep integration between VMware SDDC manager and HPE Synergy Composer which is powered by way of HPE OneView. purchasers can now dynamically compose actual substances the use of the VMware Cloud groundwork user interface to satisfy the needs of functions running on Workload Domains, increasing average operational effectivity and automation.

VMware Cloud basis 3.5 also integrates with VMware NSX-T 2.three to – simplifies the implementation of network and safety initiatives around Kubernetes. this will permit aid for containerized and cloud-native software use cases, and contains help for VMware PKS, the enterprise-grade Kubernetes answer.

previously, VMware Cloud foundation three.0 delivered support for multi-site use circumstances together with the use of vSAN Stretched Clusters as well as the short and simple circulation of workloads throughout inner most and public clouds the usage of NSX Hybrid connect.

VMware Cloud foundation three.5 will consist of help for recent VMware product releases together with vSphere 6.7 update 1, that includes basic and effective administration at scale, built-in safety for applications, infrastructure, statistics and entry in addition to elevated help for artificial intelligence (AI) and ML workloads, among other capabilities.

Integration with VMware NSX-T 2.3 will allow guide for container environments as well as a simplified workflow and a new search interface to enable administrators to look for objects and routine inside the NSX-T device and operate operations immediately.

The free up will also function VMware vRealize Automation 7.5 with broadened developer use case guide through VMware PKS integration for Kubernetes cluster management. VMware vRealize Operations 7.0 will permit shoppers to adopt a ‘self-riding’ approach to monitoring and managing their data facilities and cloud environments.

At VMworld 2018 Europe, Dell EMC announced a preview of VMware Cloud foundation on Dell EMC VxRail. The combination of Dell EMC VxRail and VMware Cloud groundwork is anticipated to present shoppers the simplest way to construct a private cloud with business-wealthy features together with multi-web page and multi-cluster with wholly automated deployment and lifecycle management of the entire stack.

VMware Cloud groundwork gives the least difficult route to the hybrid cloud through an integrated application offering that offers a constant, simple, extra at ease and agile cloud infrastructure that will also be deployed on-premises or consumed as a service from the general public cloud through VMware Cloud on AWS, IBM Cloud for VMware solutions, CenturyLink, FUJITSU Cloud service for VMware, NTT’s enterprise Cloud (ECL) powered with the aid of VMware Cloud basis, OVH and Rackspace private Cloud powered via VMware.

VMware additionally unveiled vRealize community insight four.0. The newest liberate can be able to troubleshoot software connectivity in hybrid and multi-cloud environments. New performance will permit valued clientele to troubleshoot the route between a virtual machine (VM) on-premises to an EC2 instance in Amazon net services (AWS).

VMware vRealize network insight 4.0 will also introduce complete visibility throughout underlay and overlay networks including sflow assist, new visibility for VMware NSX-T topology and events in addition to aid for Cisco ACI underlay and Cisco ASA firewall. accessible as packaged utility and as a VMware Cloud service, VMware vRealize network insight helps consumers to speed up application safety and networking throughout inner most, public and hybrid clouds. For VMware NSX customers, vRealize community perception helps pressure a success outcomes no depend the stage of NSX adoption.

VMware Skyline, a proactive customer guide expertise, automates statistics assortment to proactively review a customer’s VMware atmosphere. The provider gives prescriptive recommendations to improve typical environmental fitness and performance, and contains VMware Skyline Log aid, which eliminates the time-consuming technique of manually importing log data essential with the aid of Technical support Engineers to aid shoppers with solving technical challenges. VMware Skyline proactive findings and recommendations will be available within vRealize Operations in early 2019.

“VMware Cloud foundation presents CM.com–a Cloud Communications, funds and identity company—the best of both worlds,” noted Jan Saan, CTO at CM.com. “It serves as our device for full-stack applications, front-end purposes and databases. CM.com continues its high expense of innovation and is constructing new containerized functions. VMware Cloud groundwork’s support for PKS—the enterprise-in a position Kubernetes solution—could have an instantaneous business impact enabling us to do manner more with the same amount of americans.”

“VMware Cloud groundwork changed into the correct answer for us because we essential a platform that simplified our IT infrastructure, saved us time, and brought us ahead from our growing old data center to a contemporary one,” referred to Dustin Plank, IT supervisor at Nol-Tec systems. “Following an intensive overview technique, it become clear that the platform solved every little thing we essential it to do to enhanced support our enterprise and customers these days and moving ahead. As our business grows, it is poised to develop together with it with relative ease.”

“Leveraging our long-standing partnership with VMware and joint R&D efforts, HPE is the first to deliver composability facets to VMware Cloud foundation with HPE Synergy and HPE OneView, enabling seamless integration with SDDC supervisor to increase automation and simplicity in managing composable private cloud and application-defined information core environments,” observed Neil MacDonald, well-known supervisor Compute solutions GBU at Hewlett Packard commercial enterprise. “The potent combination of HPE and VMware Cloud foundation three.5 is a proven way for commercial enterprise consumers to accelerate their experience to the SDDC and hybrid cloud.”

VMware Cloud foundation 3.5 and VMware vRealize network insight four.0 are each anticipated to be obtainable with the aid of end of VMware’s fiscal yr 2019 (Feb. 1, 2019).


VMware Extends built-in Hybrid Cloud providing to convey improved Deployment Flexibility and selection including New Kubernetes guide | killexams.com Real Questions and Pass4sure dumps

-- VMware Cloud foundation 3.5 to deliver superior option in deployment options across deepest and public clouds and more suitable support for containers -- Dell EMC previews VMware Cloud basis for VxRail hyper-converged infrastructure (HCI) -- VMware vRealize network perception 4.0 to convey better utility troubleshooting for hybrid and multi-cloud environments

BARCELONA, Spain, Nov. 06, 2018 (GLOBE NEWSWIRE) -- these days at VMworld 2018 Europe, VMware, Inc. (NYSE: VMW) unveiled a brand new unencumber of its built-in hybrid cloud providing to support clients extra effortlessly installation and function their cloud environments. VMware added Cloud groundwork three.5 to give consumers with more advantageous deployment alternate options, new Kubernetes help, and the newest improvements from VMware’s main infrastructure and administration portfolio. VMware Cloud basis presents the quickest approach for valued clientele to build an built-in hybrid cloud through supplying a finished set of utility-defined capabilities for compute, storage, networking, protection, and cloud management to run commercial enterprise purposes in hybrid cloud environments.

In today’s multi-cloud era, organizations are taking two tactics – hybrid cloud and native public cloud – to aid their expanding set of purposes as well as their adoption of recent technologies such as laptop getting to know (ML), Kubernetes, and advanced analytics. whereas each cloud mannequin is exciting, they are complementary, existing simultaneously within the equal company. At their core they share the usual force to carry creative functions again to the business. VMware is investing in solutions that tackle multi-cloud environments, advancing the hybrid cloud alternatives from the facts middle to the cloud to the area and addressing the wants of native public cloud operations.

“VMware Cloud basis sits at the core of VMware’s hybrid cloud strategy because the easiest method to install and function the hybrid cloud. The providing gives you confirmed, complete cloud infrastructure and management for the broadest, most traumatic hybrid cloud environments--featuring consistent infrastructure and operations for private cloud, public cloud and facet eventualities,” pointed out John Gilmartin, prevalent supervisor and vp, built-in techniques enterprise Unit, VMware. “To assist assist valued clientele’ broad latitude of wants as well as offer protection to their existing investments, VMware Cloud groundwork 3.5 will offer purchasers elevated flexibility to adopt and integrate it into their hybrid cloud concepts.”

VMware Cloud basis three.5 to bring extra Deployment option, Kubernetes assist VMware Cloud foundation three.5 will bring even improved flexibility as well as help an ever-turning out to be set of workloads and cloud initiatives. VMware Cloud groundwork three.5 may be validated with Dell EMC vSAN in a position Nodes on Dell EMC PowerEdge MX with extra integration for composing compute, storage and network on this kinetic infrastructure expected in future releases. This new release will bring composability points to VMware Cloud groundwork with HPE Synergy featuring consumers the advantages of a composable deepest cloud infrastructure with much more simplicity and adaptability in managing SDDC environments. additionally, the brand new unlock will characteristic integration with VMware NSX-T enabling the computerized deployment and lifecycle administration of the network virtualization application as well as assist for containerized functions and Kubernetes. VMware Cloud basis three.5 will build on top of the huge set of new capabilities and integrations delivered in VMware Cloud basis 3.0 together with the alternative of actual community switches (compared to outdated types that simplest supported a predefined checklist of supported switches).

VMware Cloud basis three.5 will deliver:

-- improved Flexibility: ° New - VMware will validate this latest unlock of VMware Cloud groundwork with Dell EMC vSAN ready Nodes on Dell EMC PowerEdge MX, with extended integration expected over time. PowerEdge MX is Dell EMC’s kinetic infrastructure, which was designed for the software-defined statistics core, with customizable modules of compute, storage and networking. A proven, complete hyperconverged utility solution, VMware Cloud groundwork presents ecosystem aid for all VMware vSAN ReadyNodes. ° New - help for composable facets might be delivered via deep integration between VMware SDDC manager and HPE Synergy Composer which is powered by way of HPE OneView. purchasers can now dynamically compose physical materials the usage of the VMware Cloud basis user interface to meet the needs of functions operating on Workload Domains, thus increasing universal operational effectivity and automation. -- elevated consumer Use situations: ° New - Integration with VMware NSX-T 2.three – which simplifies the implementation of community and safety initiatives around Kubernetes – will permit support for containerized and cloud-native software use circumstances. This includes help for VMware PKS, the business-grade Kubernetes answer. ° up to now, VMware Cloud foundation three.0 introduced support for multi-web page use cases including using vSAN Stretched Clusters as well as the short and easy move of workloads across deepest and public clouds the use of NSX Hybrid connect. -- latest VMware Product innovations: ° New -VMware Cloud foundation 3.5 will consist of aid for fresh VMware product releases together with vSphere 6.7 update 1, that includes simple and effective administration at scale, developed-in security for purposes, infrastructure, information and entry as well as extended help for synthetic intelligence (AI) and ML workloads, among other capabilities. Integration with VMware NSX-T 2.3 will enable assist for container environments in addition to a simplified workflow and a new search interface to allow directors to look for objects and pursuits inside the NSX-T equipment and function operations without delay. the new release will also feature VMware vRealize Automation 7.5 with broadened developer use case support via VMware PKS integration for Kubernetes cluster management. VMware vRealize Operations 7.0 will permit clients to undertake a ‘self-using’ method to monitoring and managing their records facilities and cloud environments.

At VMworld 2018 Europe, Dell EMC announced a preview of VMware Cloud basis on Dell EMC VxRail. The mixture of Dell EMC VxRail and VMware Cloud basis is anticipated to offer shoppers the easiest way to build a non-public cloud with commercial enterprise-prosperous features including multi-web page and multi-cluster with thoroughly automated deployment and lifecycle management of the complete stack.

VMware Cloud foundation provides the least difficult route to the hybrid cloud via an built-in software providing that gives you a constant, essential, more cozy and agile cloud infrastructure that will also be deployed on-premises or consumed as a carrier from the general public cloud by the use of VMware Cloud on AWS, IBM Cloud for VMware options, CenturyLink, FUJITSU Cloud carrier for VMware, NTT’s business Cloud (ECL) powered through VMware Cloud groundwork, OVH and Rackspace private Cloud powered with the aid of VMware.

VMware vRealize network perception 4.0 allows for Troubleshooting for Hybrid Cloud Apps nowadays, VMware also unveiled vRealize community insight four.0. The newest unlock might be in a position to troubleshoot application connectivity in hybrid and multi-cloud environments. New functionality will enable shoppers to troubleshoot the route between a digital laptop (VM) on-premises to an EC2 example in Amazon net capabilities (AWS). VMware vRealize community insight four.0 will also introduce finished visibility across underlay and overlay networks including sflow aid, new visibility for VMware NSX-T topology and movements in addition to guide for Cisco ACI underlay and Cisco ASA firewall. accessible as packaged utility and as a VMware Cloud provider, VMware vRealize community insight helps customers to speed up software security and networking across inner most, public and hybrid clouds. For VMware NSX clients, vRealize community perception helps pressure a hit consequences no depend the stage of NSX adoption.

VMware Skyline Proactive aid for vRealize Operations VMware Skyline, a proactive customer help know-how, automates records assortment to proactively overview a client’s VMware environment. The service offers prescriptive suggestions to increase ordinary environmental fitness and performance, and comprises VMware Skyline Log help—which eliminates the time-consuming process of manually importing log information obligatory with the aid of Technical help Engineers to assist consumers with fixing technical challenges. VMware Skyline proactive findings and recommendations can be purchasable within vRealize Operations in early 2019.

helping costs“VMware Cloud groundwork presents CM.com--a Cloud Communications, payments and identity company—the best of each worlds. It serves as our device for full-stack functions, entrance-conclusion functions and databases. CM.com continues its high price of innovation and is constructing new containerized functions. VMware Cloud groundwork’s help for PKS—the enterprise-equipped Kubernetes solution—may have a right away enterprise affect enabling us to do approach extra with the equal quantity of people.” – Jan Saan, CTO at CM.com

“VMware Cloud foundation changed into the appropriate answer for us as a result of we obligatory a platform that simplified our IT infrastructure, saved us time, and brought us forward from our getting older information core to a modern one. Following an extensive evaluation manner, it was clear that the platform solved every little thing we necessary it to do to superior aid our company and clients nowadays and moving forward. As our enterprise grows, it's poised to develop along with it with relative ease.” – Dustin Plank, IT manager at Nol-Tec programs

“Leveraging our lengthy-standing partnership with VMware and joint R&D efforts, HPE is the primary to deliver composability features to VMware Cloud basis with HPE Synergy and HPE OneView, enabling seamless integration with SDDC supervisor to increase automation and ease in managing composable inner most cloud and application-defined facts center environments. The effective mixture of HPE and VMware Cloud groundwork 3.5 is a proven means for business customers to speed up their journey to the SDDC and hybrid cloud.” – Neil MacDonald, regular manager Compute options GBU at Hewlett Packard business

Packaging and Availability VMware Cloud groundwork three.5 and VMware vRealize community perception four.0 are each anticipated to be obtainable by conclusion of VMware's fiscal 12 months 2019 (Feb. 1, 2019).

-- read a perspective on modern VMworld 2018 Europe news from Joe Baguley, vp & Chief expertise Officer, EMEA at VMware, “VMworld 2018 Europe – Enabling a extra Digital, linked enterprise” -- asserting VMware Cloud foundation 3.5 -- vRealize community perception four.0: Hybrid Cloud Visibility & Analytics -- Introducing VMware vCloud Suite 2018 Platinum! -- VMware Skyline Updates from VMworld 2018 Europe -- learn more about VMware Cloud foundation -- learn more about VMware vRealize network perception -- connect with VMware on Twitter and facebook

About VMware VMware utility powers the world’s complex digital infrastructure. The business’s compute, cloud, mobility, networking and protection offerings give a dynamic and effective digital foundation to over 500,000 clients globally, aided through an ecosystem of 75,000 partners. Headquartered in Palo Alto, California, this 12 months VMware celebrates twenty years of breakthrough innovation benefiting enterprise and society. For extra tips please talk over with https://www.vmware.com/business.html.

VMware, VMworld, Cloud foundation, NSX-T, vSphere, ReadyNode, ReadyNodes, vSAN, vSAN Stretched Clusters, NSX, NSX Hybrid join, vRealize, vRealize Automation, VMware Cloud, vRealize Operations, network insight, and Skyline are registered emblems or emblems of VMware, Inc. within the u.s. and different jurisdictions. this article may also comprise hyperlinks to non-VMware websites which are created and maintained by third parties who are solely chargeable for the content on such sites.




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E20-065 exam Dumps Source : Advanced Analytics Specialist Exam for Data Scientists

Test Code : E20-065
Test Name : Advanced Analytics Specialist Exam for Data Scientists
Vendor Name : EMC
Q&A : 66 Real Questions

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Advanced Analytics Specialist Exam for Data Scientists

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Data Analyst vs. Data Scientist: Which is the Right Fit for Your Project? | killexams.com real questions and Pass4sure dumps

If you’re looking to leverage your data—whether it’s to make an important business decision, optimize your marketing, or forecast sales and growth—you might be wondering what kind of data professional you need. Do you need a full-blown data scientist who’s skilled with R, Python, machine learning, and statistical modeling? Or, do you need a data analyst who can dive into your data with SQL and pull visualizations and insights you can put to use?

The answer depends on a few things: the current state of your data, what you need from that data, and your overall business goals.

What’s the difference between a data analyst and data scientist?

Data scientists and data analysts aren’t interchangeable, but they do both have a common goal: to draw insights from data. While their skills will overlap (in many ways, data scientists are advanced analysts), generally data scientists will have a broader and deeper skill set, especially when it comes to their business acumen. They’ll have technical knowledge an analyst won’t necessarily need on a day-to-day basis, such as deep familiarity with Hadoop, advanced statistical modeling, and machine learning.

Both professionals can transform data into answers business owners need to make better decisions, but what they’re starting with and the skills required to reach those answers will vary. Data analysts can answer your business questions, but data scientists can help you formulate new questions to drive the business forward. And when it comes to complexity, chances are you’ll need a data scientist.

Before we help you determine which is right for your project, let’s quickly look at what each does.

Data Analysts

Data analysts take known data and glean actionable insights and answers to specific questions you have about your data. These are the pros who funnel insights from data into industries like education, healthcare, and travel to help businesses like airlines and hospitals run better, and deliver better service to customers.

Their value lies in their ability to make data (for example, data that’s been input into a CRM or exported from Google Analytics) more usable for you and your company. Generally, an analyst will

  • Clean and sort data
  • Uncover new patterns and correlations
  • Find actionable insights and package them up for business use
  • Use visualizations and interactive dashboards to present findings
  • Query data to meet specific needs
  • Create reports for key stakeholders
  • When it comes to unstructured data, analysts may work with a data scientist or data engineer to get help pulling new data sets for analysis.

    Data Scientists

    Why are most data scientists able to charge almost double the rate or salary as a data analyst? Data scientists have a broader and deeper skill set, especially when it comes to their business acumen. These pros create algorithms and models businesses use to predict future sales, make critical decisions, or launch products. They’re able to do more with more difficult data, including

  • Mining large amounts of structured or unstructured data
  • Data warehousing
  • Advanced programming, with R, SQL, Python, MatLab, and SAS
  • Statistical modeling
  • Develop machine learning and predictive analytics models
  • Work with the Hadoop ecosystem, including Hive and Pig
  • Formulating important business questions and hypotheses, then testing validity with math and statistics
  • A big difference is their ability to work with more complex, unstructured data—as in, data your company either doesn’t currently understand or can’t work with because it’s from multiple disconnected sources. If an analyst is primarily working with your “known data,” a data scientist is equipped to work with any of your company’s data that isn’t known or currently understood.

    When a business is making a critical decision, data scientists play a key role. They test theories and hypotheses, the results of which become eye-opening insights key stakeholders can use to make predict outcomes and make more informed decisions.

    Which Do You Need?

    Let’s look at a few questions to get you started:

  • What data are you analyzing?
  • How much of that data do you have? How much will it grow?
  • What’s the current state of that data? Is it structured and sorted, or highly unstructured?
  • What do you need from the data? Is this mission-critical, highly sensitive, or more informational?
  • Do you need algorithms or models designed to help you wrangle your data?
  • A good rule of thumb? Discuss your goals for your data with a professional. Chances are they’ll be able to assess if your project is in their wheelhouse, or if it requires more advanced skills. Get started by writing a thorough job post that clearly outlines your requirements here.

    Author: Carey Wodehouse

    Carey Wodehouse is an IT/development content writer at Upwork who is dedicated to making the complex world of web development a little easier to navigate.  Residing in Richmond, VA, Carey has worked for clients ranging from online retailers and global market research firms to financial corporations.… View full profile ›


    How automation is changing data science and machine learning | killexams.com real questions and Pass4sure dumps

    Almost any article you read about how automation will affect our future can be classified into one of two narratives. The first one is that it will definitely lead to a better future, as it always had since the industrial revolution. Of course, some people will lose their jobs, but as history shows, new jobs will be created. And not just new jobs, but better jobs. The other narrative is that this time is different. The robots are becoming more and more intelligent and capable. And the number of jobs and industries they’ll destroy will far exceed the number of jobs they create. Of course, it’s impossible to tell which of the two narratives will become a reality. What we can tell is that these narratives share similar inception: more and more parts of our jobs and lives are being automated.

    Take for example the process of driving. For many years now, we have been taking small parts of the driving process and automating them. For a better driving experience, we built cruise control. For route planning, we developed the GPS. Now, we are able to tackle more complex problems like lane merging and emergency braking. In the next couple of years, we will most certainly have fully autonomous cars driving on the roads. Just last week Waymo, Alphabet’s self-driving subsidiary, officially received the very first California permit to test their vehicles in the state without a human behind the wheel.

    All these developments illustrate an important lesson: automation happens in steps. And this process can be observed in many jobs. The only major variable is the timeframe at which it happens. If you’re a doctor, it’s going to take a while until major parts of your job are automated. If you’re an entry-level accountant, the timeframe is much smaller. Just as with driving, gradual automation is happening in data science as well. Although a complex topic, I’ll be focusing on two important developments that are changing the data science landscape:

  • Data science platforms
  • Automated machine learning
  • Before I start talking about data science platforms, let me give you a short introduction into what data science is. In its essence, it is a field that uses tools taken from computer science, statistics, and machine learning to extract insights from data. In other words, if you have some data and you want to make some decision or predictions based on it, you use data science. However, extracting information from big data sets can be expensive. To implement any type of big-data project, a company must build a data infrastructure first. Think of it as different pieces of technology that can run all the tools a data scientist needs. The issue is that for many years building such an infrastructure was like building a car just from parts.  Possible, but you needed people with highly specialized skills, and it took a lot of money and a time. Fortunately, this is changing. What we have seen in the past few years is the appearance of platforms that automate this process. Take for example various cloud-based platforms that make it much easier to develop and maintain big-data infrastructures, from my own team to others in the market like Amazon Web Services (AWS), Google, Microsoft, and Anaconda.

    Automated big data platforms are only part of the story. Although they enable us to set up and maintain data infrastructures more easily, somebody still needs to write lines of code to clean the data and experiment with machine learning models. This process can be quite time consuming and needlessly complex. To understand what I mean, let me walk you through the data science workflow.

    Usually, every data science project consists of three parts: data processing, modeling, and deployment. When it comes to how time is spent in each of these parts, we have this rule of thumb:  50-60 percent is spent on just processing the data, and the rest is spent on modeling and deployment. As you can imagine, this is a very inefficient use of a data scientist’s time. But it’s necessary as real-world data is messy and algorithms can’t deal with messy and unstructured data sets. Modeling, on the other hand, is an iterative process. For any specific problem, it’s impossible to know beforehand which exact algorithm is going to be the best. As a result, a data scientist has to try out many different algorithms until he arrives at a well-performing one.

    Another source of inefficiency is that it can be difficult to take a model and package it in a way that can be launched in production. Many times the machine learning pipeline that was built during the modeling part needs to be broken apart and reconstructed to make sure its production safe. To address all these inefficiencies, in the past few years analytics vendors have started developing products that take the entirety of this workflow and integrate into one end-to-end platform. Think of these platforms as operating systems for data science. The big innovation that these platforms bring is that first that they automate a lot of the data processing part. Second, they make it very easy to keep track of all the developed models and their parameters.  And they make it easier to launch algorithms and models into production.

    To give you a few examples, Alteryx has a smart and easy to use data science platform. They’re a company that had their IPO a little more than a year ago and have already seen their share price double. Others players in the sector include KNIME, RapidMiner, and H20.ai. Just as cars have all this technology that is making drivers more efficient, so do these platforms promise to help people navigate the data science workflow better.

    However, that still doesn’t mean you can get rid of your data scientist. There’s still a need for a person who can build and interpret models. The issue is that these skills are hard to come by. And they’re also expensive to pay for. One of the biggest hurdles companies face when trying to build advanced analytics projects is the lack of skilled data scientists. This is where my next topic comes into play: automated machine learning. Some of these analytics companies have gone a step further and started integrating automated machine learning systems into their platforms. The kind of systems where with just some minimal intervention you can drop your data in and get collection models out. The biggest advantage these systems bring to the table is that they make predictive analytics open to a much wider audience. They can be incredibly powerful tools in helping non-technical employees to solve simpler prediction problems like customer churn. Although there are more and more companies that offer automated machine learning solutions, like DataRobot, I would like to mention a couple of interesting open source Python projects that anyone can try out.

    The first one of these tools is Featuretools which is an amazing library for automatically building features. If you’re unfamiliar with feature engineering, it is the process of building input variables for a machine learning model:

  • While Featuretools takes care of only a small but important part of the model building pipeline, the following two projects are actual automated machine learning libraries able to test and build advanced machine learning models automatically.
  • One of these libraries is TPOT which uses genetic programming to come up with a performant machine learning pipelines automatically. Although it’s quite user-friendly, it can take a long time to come up with solutions.
  • The other library is auto-sklearn which is based on meta-learning.
  • A very interesting concept that uses knowledge gained on thousands of previous datasets and machine learning models to decide which models and parameters could work best for a given data set.
  • Although these projects are still in their infancy, they can give a great insight into how automated machine learning systems work and the benefits they can bring in the data science process.
  • With all these tools geared towards automating parts of the data science workflow, the big question that arises is: "can the job of a data scientist fully automated?" The shortest answer is no. Automated machine learning does not eliminate the hard parts of a data scientists’ job, such as listening to clients, understanding the business problem, and figuring out how to craft a solution. It automates the repetitive and time-consuming parts of the job. What these tools can achieve however is to help analytical toolsets become more widely adopted by the general public. And we have already seen this happening with a new breed of 'citizen data scientist', people from non-technical backgrounds, working on and delivering analytical projects through the use of data science tools.

    Photo credit: Lightspring / Shutterstock

    Andras Palfi is a Data Scientist at Bigstep. On November 15, he will lead a webinar on "Recommender Systems, ranging from simple systems to complex ones". To learn more and register, click here.


    Analyst, Scientist, or Specialist? Choosing Your Data Job Title | killexams.com real questions and Pass4sure dumps

    There’s a world of job opportunities in data, big data, data analytics, and the business improvement they can bring. A prediction for the United States in 2018 from the McKinsey Global Institute showed a shortfall of deep analytic skills in the amount of more than 140,000 people, not to mention the 1.5 million people needed with the ability to interpret data analysis to make data-driven business decisions. If the numbers are right, it looks like demand will clearly exceed supply.

    There are several possible data job titles involved, including data scientist, data analyst, business analyst, and data specialist. It’s important to hone in on the one that best matches your capabilities and aspirations, possibly leaving the door open for further career development later. However, be aware that people may use titles in different ways, sometimes even interchangeably. So, look at the description for each title below to make sure that at your next work appraisal or hiring interview, you agree on what the job role entails — as well as its name.

    Data Scientist

    Often called “unicorns,” people with all of the requisite skills to fill this role are rare indeed. Essentially, a data scientist makes models for data-driven decisions, looking to the future and demonstrating initiative and innovation to build new solutions where necessary.

    Besides strong technical skills (for instance, use of Hadoop, programming in R and Python, math, and statistics), data scientists should also be able to tackle open-ended questions and undirected research in ways that bring measurable business benefits to their organization. Ideally, they are inquisitive by nature and can relate simultaneously to data, to organizational needs, and to the business audience that needs to hear about their results.

    As you can imagine, data scientists with all these aptitudes can be of considerable value to their employers. They are the link between the data resources available to an enterprise and executives looking for opportunities to make the business better, faster, and stronger. Depending on the size and objectives of the enterprise, they may report to a Chief Scientist, CTO (Chief Technical Officer), CMO (Chief Marketing Officer), or even directly to the CEO.

    Data Analyst

    Data analysts use existing tools and algorithms to solve data-related problems rather than inventing new ones like data scientists might do. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. They also put together custom database queries to answer the questions of business users, implement new metrics from existing data, strive to improve data quality, and contribute to correct acquisition of new data.

    There is no absolute rule about the use of this analyst job title. Data analysts in one organization might be called data scientists or statisticians in another. They are also often expected to combine technical know-how with industry knowledge, overlapping with the business analysts we'll discuss below. Overall, however, what often characterizes them is a focus on data collection, manipulation, and analysis, using standard formulas and methods, and acting as gatekeepers of an organization’s data.

    Data analysts might report to a CIO, a Chief Data Officer (CDO), or possibly to a data scientist or business analyst team leader. While salaries for data analysts are often reasonably high, salaries for data scientists may be higher still. This may reflect the requirement on data scientists to create models to improve the future, compared to the role of data analysts to use data to describe the past and the present instead.

    Business Analyst

    Usually experts in their industry, business analysts must also have a reasonable knowledge of manipulating data and specifying systems while being able to communicate well at different levels. In a general sense, the starting point for business analysts is the assessment of the operational and functional needs of their organization. They then translate those needs into system specifications and look for the most attractive financing options for such systems.

    Database design is often an important part of the business analyst role. This includes database modeling, metrics definitions, dashboard design, and creating and publishing executive reports. ROI (return on investment) is also a key concern, as business analysts apply their data-related activities to finance, marketing, and risk management, for instance.

    Business analysts may work together with data scientists and data analysts in areas such as metric definitions and database design. The distinction between all three categories can become blurred, for example, if a business analyst also provides code for new business systems and applications. Business analysts often work within matrix organizations, reporting to a line manager like a CIO or CFO, for instance, and to a functional manager like a project leader.

    Database Specialist

    As their name suggests, database specialists possess in-depth knowledge of databases. They work with information security software to prevent data breaches and assist business operations by organizing whatever volume of data is needed. They make sure data is correctly stored, protected, cleaned, transformed, and aggregated to meet business requirements. This includes compiling and installing database systems, scaling to multiple machines, and implementing disaster recovery plans.

    Database specialists may also look after other data repositories used by the organization such as data stores, marts, warehouses, and lakes. Besides data management skills and optimization of the data architecture to meet business requirements, they provide a robust platform for data analysts and data scientists to obtain the data they need for their own models and investigations. With strong technical abilities, database specialists are likely to be at ease with both SQL databases like MySQL and PostgreSQL and NoSQL technologies such as MongoDB and Redis. Frequently part of the IT department, database specialists may report to an IT team leader or to the CIO. Salaries can be comparable to either those of data analysts or data scientists, with stronger software engineering skills tending to lead to higher levels of remuneration.

    One Data Job Title Today Can Bridge to Another Tomorrow

    The roles above intersect in several places. For example, although a data scientist offers modeling, storytelling, visualization, and statistics skills, compared to a database specialist’s know-how in system implementation, data storage, and database administration, they overlap in areas of programming and math. Likewise, a data analyst may focus on standard SQL data stores, analytics, statistics, and business intelligence functions, compared to a data scientist involved in new data acquisition and manipulation with advanced statistics, but they both typically share a curiosity about data, a desire to obtain insights, and an ability to “tell a story” to business audiences about data.

    It may, therefore, be possible to transfer from one role from another and to change “internal customers,” for example, from a more technical audience to a more business-centric one. The platform you or your organization uses for preparing and managing data, modeling and designing algorithms for analytics, and visualizing both data and the results of the analyses can contribute to this process. 



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