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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 February 15, 2019
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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 Advanced Analytics Specialist Exam

Six scorching big statistics Certifications for 2019 | killexams.com Real Questions and Pass4sure dumps

Written by means of Nathan Kimpel posted: 10 December 2018

page 1 of two

a brand new year procedures! if you have a serious professional activity in massive records, then make a big impact in your career outlook via specializing in one of these six certifications in 2019.

Get one of these credentials to take you interest in Big Data to the next level!It’s almost 2019. With the passing of yet another 12 months, many corporations are jumping onto the certification bandwagon and offering certifications in anything else and every thing ... and i love it!

Nothing is more gratifying and rewarding than getting a certification, a document that says you have mastered an IT subject matter, or on occasion simply that you just received a simple understanding of that topic. To make your 2019 vibrant and delightful, right here is an inventory of my proper six large facts certifications for the arriving year.

First up is the CCA information Analyst credential (exam CCA159) offered via Cloudera. i love Cloudera tests because they are hands-on and comprise real-world examples, and this test doesn't disappoint. it's particular since it is simple and focused. the place else are you going to get 8 questions with one hundred twenty minutes to reply them?

to claim Cloudera exams are challenging can be highly accurate — but once you bag one, nobody goes to question your prowess. each and every test carries between eight and 12 customer issues that involve a unique colossal information set and a CDH cluster. As cited above, you’ve received one hundred twenty minutes.

For each problem, you have to put in force a technical answer with a excessive degree of precision that meets all the requirements. You may additionally use any device or aggregate of equipment on the cluster — you get to decide on the tool(s) that are correct for the job. You ought to possess satisfactory competencies to investigate the issue and arrive at an superior method given the time allowed.

You deserve to be aware of what be sure you do and then do it on a live cluster, in the precise time allotment, while being watched via a proctor. Make no mistake: make sure you have loads of actual-world competencies earlier than you address this examination. in terms of fess, the CCA159 will set you again $295.

My 2nd recommendation is slightly unconventional and not really a “certification” per se, but i recommend getting a grasp’s diploma in information analytics. in case you have a real interest in big statistics, then i recommend leaping in with both toes to display future employers just how deep the hearth burns. a level will demonstrate that you've potential and adventure that goes beyond simply excelling at test taking.

i recommend searching round for a reputable faculty that does most (if not all) of its direction birth online. I also accept as true with your highest quality guess for funding an superior degree is through your existing corporation. Many employers participate in charge-sharing for larger education, and if yours doesn’t then you definitely may also nevertheless be capable of determine a deal.

Don’t be shy about asking either, as a result of to make huge records a truth, a crew of individuals executing the beginning needs to be well-knowledgeable. i admire this method of learning because it goes to set you other than the pack and put you head-and-shoulders above the rest of the individuals trying to wreck into huge information.

My third advice is EMC (Dell)’s statistics science certification track. Science is an overarching theme of working with massive records. information extraction is a science, information processing and data storage is a science, and, analyzing records is in reality a science.

you are going to need to take a couple of checks for this, however aim for the Dell EMC licensed specialist – records Scientist credential. The exam to center of attention on is E20-065 superior Analytics expert exam for information Scientist, which has two associate-degree prerequistes affiliate – records Science edition 1.0 and affiliate – statistics Science version 2.0.

The superior Analytics certification is designed to construct on the abilities developed at the affiliate degree and help aspiring facts Scientists proceed to conform and extend their skill sets

The main boom areas include superior analytical strategies, Hadoop (together with Pig, Hive, HBase), Social network analysis, herbal Language Processing, and Visualization methods. This certification is above and beyond others because of its center of attention on NLP.

herbal Language Processing (NLP) is a subfield of computing device science, assistance engineering, and synthetic intelligence concerned with the interactions between desktop and human (herbal) languages. In particular, NLP stories the way to program computers to technique and analyze enormous quantities of natural language information.

in case you need a fast track to a long-time period job, get in on the NLP flow. at this time, engineers are true-time translating languages and archiving all favourite languages all through the earth. this is enormously pleasing stuff and will have you on the cutting edge of AI.


Interview: moving to DataOps | killexams.com Real Questions and Pass4sure dumps

A contemporary Forrester record discovered that only 22% of groups see a return on investment (ROI) from information science. Given the tantalising alternatives that records science, artificial intelligence (AI) and superior analytics promise company, why do most initiatives fail?

For Harvinder Atwal, head of data strategy and superior analytics at expense assessment site MoneySuperMarket.com, part of the issue is that the mantra is inaccurate. He believes many company leaders have little idea of how to create cost with information.

“The C-suite doesn’t have in mind data,” he says. “They be mindful the need to hoard records and employ statistics scientists – but then they feel magic occurs.”

Atwal thinks there's additionally a misconception in what company sees because the function of data scientists. as an instance, although potent handle of finance is naturally crucial in business, nobody expects each person to be a finance expert. however in Atwal’s journey, there is an expectation in companies that records scientists can share their skills across the enterprise. “data scientists are expected to train the service provider a way to use records,” he says.

Then there's the AI silver bullet. “marketing thinks AI will solve all their issues,” he says. because facts scientists are not inexpensive and hoarding statistics is costly, this lack of knowing capacity that the cash invested in records projects seems to move in the wrong direction, he aspects out.

Atwal up to now labored at Dunnhumby as perception director for Tesco Clubcard. Describing how MoneySuperMarket.com makes use of analytics, he says: “Our mission is to make use of our information to aid customers keep money.”

He says MoneySuperMarket.com captures greater records about its shoppers than a typical site. The guidance gleaned comprises where they are living, what they pressure, where they go on holiday. The site additionally knows when a consumer’s assurance is up for renewal or when their utility is set to swap from a discounted to a standard tariff.

“we will keep americans £1,000 if they come to our web site, but it surely requires computing device discovering for customization,” says Atwal.

really, MoneySuperMarket.com recommends items fairly like how Amazon or Netflix recommends things. however people have very diverse attitudes about cash: some are extremely cautious, whereas others may well be more open to possibility.

For MoneySuperMarket.com, this skill that valued clientele simplest see presents of products which are important to them and fit inside their risk profile. The architectural alterations and approach that the enterprise has taken has allowed it to create 1,400 variant newsletters for its valued clientele, which Atwal says has resulted in a “respectable profits uplift”.

damaged IT processes

For Atwal, developing the specific mannequin is through a ways the easiest part of desktop discovering, however ninety% of the conversation looks to be about this. He says the precise difficulty for facts scientists is that they must do everything themselves – find information, clear the records, discover the application and installation it.

“it's caught in a twentieth century working model,” he says. “individuals don’t have entry to facts warehouses.”

This poses a real challenge for statistics scientists, says Atwal, who ought to request records entry from IT, negotiate with IT for the required compute substances, then look ahead to these supplies to be provisioned. They might also then need to install a question language.

“As an information scientist, you simply are looking to use statistics as brief as feasible,” says Atwal. In his journey, the rigmarole skilled by using facts scientists when doing their job capability that they frequently choose to pass IT and look at various, construct and deploy statistics fashions on their personal laptops.

but he doesn't agree with here's the right approach either. “information scientists will spend a lot of time constructing the perfect model on a laptop,” he says, however while it is being tuned on a computing device, the model isn't finding real uses for the business – so it is still remoted.

Atwal believes data scientists should be able to get comments when the mannequin is deployed for real, to enable them to boost it or build new records models in accordance with actual customer facts.

Simplify the records structure

When Atwal joined MoneySuperMarket.com in 2012, the enterprise became deploying SAS to deliver a single consumer view. “We made a call to movement over to AWS [Amazon Web Services], however had statistics retailers scattered far and wide the business,” he says.

even though it turned into fairly effortless to movement the web site, migrating the statistics warehouse became very complex, says Atwal. The company had a multi-cloud method, which intended it changed into no longer possible to make use of any services selected to AWS. instead, he says, MoneySuperMarket.com had to manage and deploy an open source application stack.

“We started constructing a stack in AWS with storage and analytics layers, deployed this in creation, then developed statistics items,” he says. however this become now not a simple method because it required database administrators, DevOps teams and agile records science. “We didn’t have the potential,” he provides.

When it became time emigrate from SAS, MoneySuperMarket.com took the probability to run a proof of theory on GCP, using Google’s serverless utility accessories, including large question, Kubernetes, Dataflow and TensorFlow.

This enabled the company to simplify its facts architecture. in accordance with Google’s reference structure, MoneySuperMarket.com became in a position to installation serverless and software-as-a-carrier know-how, which supposed there turned into no infrastructure to control, enabling the information science groups to pay attention to getting their work accomplished on GCP, says Atwal.

improving records science workflow

Atwal says most information scientists don't come from a utility building heritage and do not take into account application building superior practices. To enhance the investment that corporations make in information science, he believes new records fashions deserve to be created extra rapidly. This requires statistics scientists to use agile collaboration and to follow lean thinking to records analytics, whereas adhering to data rules and governance.

As in application building, where developers have used DevOps to provide code all of a sudden via regular iterations, automated tested is also obligatory. And like in utility construction, records scientists additionally need to be certain the records on which they base their model is appropriate, and that there's version manage in place to make certain alterations can be tracked, says Atwal.

These are among the main requirements in DataOps, which goals to pace up the manner of constructing new statistics fashions that obtain measurable business outcomes. Ideally, a knowledge scientist would wish to download a working ambiance and get to work promptly while not having to configure every thing in that ambiance. Containers supply companies a way to achieve this, says Atwal.

a few so-called information science systems are beginning to emerge that help DataOps. DominoData is the one MoneySuperMarket.com has deployed. Atwal says it presents a way to give self-carrier for its information scientists to work.

Atwal has spoken at a number of routine about how MoneySuperMarket.com has rearchitected its statistics analytics. His presentation, which covers nine steps to radically change facts science and flow corporations in opposition t DataOps, attracts on lean ideas that Toyota used to optimise motor vehicle manufacturing, and agile utility development practices.

subsequently, he says, information scientists deserve to be cognisant of enterprise approach. “enterprise has a speculation of what creates value,” he says. “feel about move and how at once that you would be able to get facts right into a product to get feedback from consumers.”


5 approaches We’re Maximizing Our B2B advertising budget | killexams.com Real Questions and Pass4sure dumps

This week I at last felt settled into the new yr: I haven’t hesitated writing that 9 for days, the early morning fitness center site visitors has thinned tremendously, and my resolutions are – well, I’ve relatively a lot forgotten those, which appears appropriate on target.

but TurboTax just reminded me that I’m nevertheless ready to finish one important a part of closing year: budgeting.

i will be able to’t set a private funds with out reviewing every little thing from last yr. Likewise, which you can’t set a finalized promoting funds for 2019 with no need time to assemble and overview your monetary efficiency from final year.

B2B advertising budget planning

if you’re managing on-line advertising for a B2B business – and not, say, patiently awaiting tax details from the executive – by way of now, you’ve confidently gotten the chance to collect that information and begin working the numbers.

however how do you put a practical but competitive finances that aligns along with your dreams and fits your universal approach—and get that finished fast? (It’s just about February, in spite of everything. Time is tickin’.)

We went to some of our very own consultants to ask.

right here’s what our account managers, paid search strategists, and weblog contributors are specializing in so as to maximize budgets for their B2B shoppers in 2019.

1. Reflecting on YoY efficiency

some of the first steps in atmosphere a strategic funds is having your desires and ambitions naturally defined. To get this settled, you have to evaluation and reply to your account’s efficiency in 2018.

“My starting factor,” Kristina Simonson, the digital marketing expert on WordStream’s advertising team pointed out, “is the 2018 information. I’ll review the records to peer what worked and what didn’t, after which I’ll compare that to historical year over yr performance, paying consideration to patterns of exchange in performance, corresponding to ad type, platform, and device.”

B2B advertising budget yearly spend changes by device

photo supply

via putting ultimate yr’s metrics in context, Kristina says, “I’ll collect insights on account boom, seasonality, and network performance from this review, then I’ll put that into action for the 2019 plan. The objectives and forecasts throughout channels for the next yr will be in accordance with this evaluate and analysis.”

2. Defining KPI Metrics

even if you’re operating campaigns to your personal B2B enterprise or managing paid search concepts for a shopper, you should check which metrics you should center of attention on to quite simply measure growth towards your dreams. this will support you investigate how to set your finances for the 12 months.

For Brett McHale, founding father of Empiric advertising and marketing, obviously defining KPI metrics for his consumers has been a protracted-term venture in his practise for 2019. “each person likes to peer stronger floor metrics (charge per conversion, etc.),” Brett observed. “My price to shoppers truly shows when it comes to client acquisition can charge (CAC). I spent loads of time with data and analytics tools towards the end of last 12 months to supply my valued clientele a full graphic of what their funnel looks like with the intention to see a favorable return on funding. This also allows me to clarify my choices improved when it comes to finances and pacing. In 2019, data visibility, funnel visibility, and development to CAC metrics are my leading priorities for my latest purchasers.”

three. Prioritizing Multi-Channel techniques

When finalizing your price range, be certain you’re accounting for multi-channel processes this year.

“My desirable priority for 2019 is to make sure my shoppers are embracing and imposing multi-channel tactics,” Director of customer approach at Clix advertising and marketing Joe Martinez shared.

cross networks ads branding

informed for You

Webcast, March 13th: a way to prompt high-price valued clientele Leveraging herbal Language Processing and desktop gaining knowledge of

We found that advertising on fb resulted in a 34% enhance in brand searches on Google.

“Your target viewers isn’t just on Google or simply on fb. obviously they're on many structures throughout their journey to purchase. embrace promoting on underutilized channels corresponding to Quora, Waze native, Apple Search ads and more to work against more affordable CPAs or just staying on correct of your clients’ minds.”

Hannah Cohen, senior digital advertising strategist here at WordStream, is calling to look at various multi-channel techniques this year, and she’s hoping to identify the most a hit budget allocation. “I even have considered first-rate success in conversion numbers on monitor for B2B, but very low satisfactory inside that community,” she observed. “I are looking to profit an understanding of fine between reveal & Search – and query what sort of finances the client desires to set apart for branding alone.”

Others are including selected channels of their plans. WordStream revenue Engineer Emily true pointed out, “One component that became correct of mind for me in crafting our 2019 approach become LinkedIn advertising, since it has advanced targeting that allowed me to healthy my adverts to americans with certain titles at specific agencies. they have some facts and figures on their site about the ROI of B2B advertising through their platform.”

4. Planning on Seasonality

in case you’re working with B2B bills, you’re probably already time-honored with seasonality. traffic may drop when individuals depart their desks for summer season vacations or conversions could have just taken successful all over the fourth quarter when groups are closed for the holidays. Or possibly you’ve created some seasonal magic with a Valentine’s Day referral application that drives a predictable mid-February spike in income.

B2B advertising referral program

photograph source

The seasonal fluctuation will rely upon your enterprise or your business. So whilst you’re planning your spend for the yr, make certain to budget for this seasonality.

“For my B2B debts, i admire to confirm with the client the knowledge seasonality of the enterprise for the total yr,” Joe spoke of. “for instance, we cannot assume all B2B agencies decelerate in December or pick up in January. Many B2B companies I’ve worked with aren’t littered with exact calendar dates however how the markets of other industries are doing. make sure you work along with your consumers to basically understand the nature of their enterprise and the place you'll want to basically be searching backyard of the bills. this will better support you cope with the U.S.and downs so that you can’t control at once in the advert channels.”

Brett turned into additionally concerned with these fluctuations for 2019 planning. “With seasonality and expanded competitors on social channels (due to the vacations) out of the way, many shoppers are witnessing a beautiful dramatic drop in CPA for Q1,” he talked about. “With this in mind it’s vital to examine metrics 12 months-over-year to get a good suggestion of how your consumers rebounded after q4 inside each channel.”

5. targeting excellent Leads

This last budget-planning strategy is basically a given: B2B advertisers are usually concentrated on producing leads, but this 12 months the consultants we spoke to are concentrated more especially on generating improved leads who're attractive, qualifying, and changing.

b2b advertising budget challenges

graphic source

“My largest issue for B2B valued clientele is lead pleasant,” Hannah spoke of. “My accurate precedence is gaining CRM information and/or syncing their CRM with the platform they advertise on every time viable. That method, i will see which campaigns are producing leads that turn into purchasers.”

Lead fine is also on Kristina’s listing of priorities for the 12 months. “My intention for 2019 is to drive leads, but that’s a ordinary goal, now not a new approach. This year, though, I wish to focal point specially on driving top notch leads.”

however what does that seem like? Hannah’s allocating extra finances on focused on. “I’m planning to structure segmented audiences in keeping with CRM data and goal demographic to help my client attract better leads.”

focused on is also in Kristina’s plan. “To get more advantageous leads, I need to more advantageous have in mind our customer’s adventure and, in line with this, goal advertisements more effectively. I additionally wish to increase the adverts themselves. We need inventive excellence to appeal to the very best quality of leads viable.”

comply with Céillie Clark-Keane:

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Advanced Analytics Specialist Exam for Data Scientists

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IBM boosts data scientists with certification and apprenticeship program | killexams.com real questions and Pass4sure dumps

IBM Corp. is attempting to give the data science profession a boost by championing a new certification and launching an internal apprenticeship program today that gives young people with no information technology experience a chance to become professional data scientists.

The U.S. currently has more than 150,000 unfilled data science jobs, according to last summer’s Linkedin Workforce Report. That’s a problem for companies that want to take advantage of the sprawling assortment of data analytics tools as well as for those such as IBM that have products to sell them.

IBM worked with The Open Group, a global consortium of about 625-member companies that develops open, vendor-neutral technology standards and certifications, on what the company said is a first-of-its-kind data scientist certification. It’s based on peer reviews and demonstrated practical experience, rather than standardized tests.

“It provides an objective, reliable measure of data science capabilities and qualifications,” Martin Fleming and Seth Dobrin wrote in a post published today on IBM’s Think blog. Fleming is IBM’s chief analytics officer and chief economist and Dobrin is the company’s chief data officer.

IBM will be the first company to offer the certification to its own employees, providing three levels of certification and support by its internal badge program.

Certifications are common in technical fields where virtuosity can be measured by metrics such as test scores. However, they can be controversial in professions that place a premium on “soft” skills that aren’t easily quantified. Data science demands a combination of technical skills and creativity, so the peer review process is intended to measure both.

IBM also said it has launched an internal apprenticeship program as part of its “new collar” initiative, which aims to hire young employees who lack traditional backgrounds or even college diplomas. IBM says between 10 and 15 percent of its current new hires don’t have traditional four-year degrees.

The 24-month program provides a combination of education, mentoring and practical experience with employees working toward level one certification as an Open Group-certified data scientist. The first cohort of five students was hired last week from a pool of hundreds of applicants. IBM intends to expand the program aggressively across the U.S. but wouldn’t specify any targets for the number of people who will be hired.

Rather than looking for degrees, IBM is after people “who are highly curious, have a spirit of a continuous learning and have analytics capabilities,” said Ana Echeverri, an IBM data science growth strategies lead. In contrast to remote or even classroom learning, she said, “they have the chance to be part of a great company. It’s life-changing.”

The program is a registered apprenticeship with the U.S. Department of Labor, which gives IBM access a variety of funding, credentialing and tax benefits. In a tight labor market, such programs also make business sense, Echeverri said.

Building skills internally is cheaper than paying for them on the open market, and graduates of the program tend to stay with the company. “We’ve found that these people are just as competent as those with advanced degrees,” said Echeverri, who holds a bachelor’s degree in computer engineering, an MBA and a master’s in analytics.

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The Connection Between Data Science And Business In Big Data | killexams.com real questions and Pass4sure dumps

Making sense of all the noise is difficult in the era of data. Businesses have invested billions to hire data scientists, and data has already changed various fields involving retail and healthcare.

On the other hand, Kaggle’s survey found out that many data scientists feel the lack of explaining data science to others as one of the barriers faced at work. Moreover, data science, according to Hugo Bowne-Anderson’s research requires from the scientists skills to learn on the fly and soft skills in order to fulfill business questions, explaining difficult results to non-technical shareholders.

Bridging the Gap Between Data Scientists and their Colleagues in Business

Such gaps between tech and business professionals exist for a long period of time. A team approach helps to cover those gaps and allows to take advantage of data science.

A major software development in Ukraine helps to deal with today’s tech challenges and apply big data in a meaningful way. It is easy to stay on track with data-driven decisions and advanced analytics.

What A Company Needs To Do

1. Organizations have to check whether they have needed expertise. It involves the skills to operate big data platforms and to apply different analytics techniques such as machine learning, and using tools like Python and R.

2. Companies need to bridge the increasing communication gap between the engineering teams and data science, and the related business functions that lean on them for data-driven decisions.

These two steps are important because data analytics is only helpful if it may generate insights. In most cases, data scientists are ill-equipped to explain the results of their work.

READ

Before Starting, Consider 5 Reasons Your Big Data Project Will Fail

What organizations demand is persons with a hybrid skill set that mixes awareness with data science, deep knowledge of big data platforms, basic techniques and analytical tools, and an ability to present technical insights in a way that allows to easily implement them.

How to Not to Get Lost in Translation

Different departments in the company generate data separately and distributes it in isolation. Good alternatives that treat these limitations at both the individual and the structural levels, in a way that combines employee skill sets and new technologies have appeared. That’s why it’s valuable to create Enterprise Data Hubs where data are concentrated and are accessible for anyone to explore and analyze.

To handle those systems, your company needs both analysts and data engineers who may synchronize and understand, facilitate and help to build communication between IT department, business assets, and big data.

Let’s deeply consider what enterprise architects and big data business analysts can bring to the table.

1. Enterprise architects. They create enterprise-wide and responsive data architecture that can help with aligning IT strategies and business goals. Moreover, enterprise architects provide large-scale program review, track technology life cycles and identify how individual technologies will change. Their jobs demand to maintain a connection with various employees across the company in order to assign custom data storage and develop solutions. They develop the environment to translate big data into business insights that are meaningful.

2. Big data business analysts. Big data has changed the role of the business analyst. Nowadays, they may no longer base decision-making on the market research and trends. Big data business analysts lean on the insights brought from data analytics to develop the business.

READ

Why Every Organization Should Share Charitable Giving Data

Many companies have started to experiment with DevOps functions which involve operations and IT across multiple departments. These developments show how business processes boost and how to use new technologies.

Developing a Better Data Science Operation

A good data operation based on automation, teamwork, and advances in visualization techniques. These steps will help you to build needed data science operation.

Project management. A successful project manager needs to have good organizational and strong diplomacy skills, allowing to fulfill cultural gaps by gathering different talents and getting all of them to speak the same language.

Data wrangling. Abilities that are important are gathering and cleaning data, building systems, and developing and supporting algorithms and other statistical engines. People with significant talent will search opportunities to accelerate operations with predictable visual output that will boost the information-design process.

Data analysis. The skill to develop hypotheses and test them, take advantage of data, and relate it to a specific business context is essential. Context setting and critical thinking are the key skills for the analysis of data as well.

Data Scientists and Business Professionals Need to Speak Each Other’s Languages

So, bridging the gap between skilled data scientists and other departments is valuable for each organization. Businesses need people that can transform difficult technical insights into simple and clear ideas. These tips will help you to gain profit from all the data you have.


In Analytics Economy, Demand for Data Scientists Outpaces Supply, McKinsey Says | killexams.com real questions and Pass4sure dumps

As analytics and automation transform industry, demand for data-savvy employees is far outstripping the available supply, according to a new report from the McKinsey Global Institute, the research arm of global consulting firm McKinsey.

The implications for K-12 schools?

Teaching more statistical reasoning and quantitative skills to younger students will help them be better prepared for the tech- and data-driven jobs of the future, said Michael Chui, a partner at MGI, in an interview.

"The ability to understand what probability means is now a basic life skill," Chui said. "It has tremendous implications for us as workers and as citizens."

The McKinsey Global Institute report, titled "The Age of Analytics: Competing in a Data-Driven World," makes the following case about the nation's data-science talent pipeline: In response to employer demand, higher-education institutions have begun offering more degree programs in data science and analytics (the researchers counted 100 such master's-level programs in business analytics alone.) And students have "flocked" to such opportunities, with the number of degrees granted in such fields growing by 7.5 percent between 2010 and 2015.

Still, demand for data scientists is growing much faster, by perhaps as much as 12 percent per year. 

One indicator of the resulting gap in the labor market: average wages for data scientists rose by about 16 percent between 2012 and 2014, compared to a 2 percent increase in wages overall. In a survey conducted by McKinsey earlier this year, business leaders also said finding and retaining analytics talent was far more difficult than in other areas.

The net effect, the McKinsey Global Institute concludes, is that the U.S. economy could be short as many as 250,000 data scientists by 2024.

"Providing people with the skills to get jobs is one goal of education," Chui said.

"At least in the medium term, if students gain data-science credentials and have the skills, they will be in demand."

'All the Math You Can Possibly Take'

The legitimacy of the so-called "skills gap" in STEM subjects (science, technology, engineering and math) has been much debated in recent years.  But when it comes to the specific field of data science, there is little doubt that "the recent 'big data' trend has sparked demand for data scientists in all areas, from health care to retail," the federal Bureau of Labor Statistics wrote in 2015.

Tech companies are certainly part of that equation. Just last week, for example, social-networking behemoth Facebook launched an initiative to help educate students, teachers, and the public about "artificial intelligence," a kind of blend of computer science , data science,, and neuroscience  in which algorithms are used to design "intelligent" machines that can recognize patterns and images, translate languages, and learn some kinds of logical reasoning. (A series of videos featuring Vann LeCun, the company's head of AI research, further explains the field.)

In a blog post, Facebook listed a number of tips for students to prepare for such fields. Chief among them: "Take all the math you can possibly take," including probability and statistics. (And while you're at it, the company recommends, make sure you take some computer science, and try to squeeze in engineering, economics, philosophy of knowledge, and the latest brain research, too.)

It's not just tech companies that are seeking such skills. The fields of transportation (think: self-driving cars), retail, advertising, and health care are among those already undergoing radical transformations. Manufacturing and the public sector (including public education) have also started dipping in their toes. 

Even those jobs that aren't replaced or rendered obsolete will be dramatically changed, McKinsey argues. The reason: as data-driven automation yields new advances in machines' ability to process natural language, recognize patterns, and even sense human emotion, everyone from administrative assistants to lawyers to industrial engineers will see core aspects of their daily work evolve or disappear.

The Importance of Statistics

As a result, the McKinsey Global Institute researchers argued, for every data scientist that employers hire, they will need even more "translators," able to connect new information to real-world business problems.

"These are the people who know enough about data science to be good consumers of data, but who also have enough domain knowledge to bring the resulting insights into organizations and effect change at scale," Chui said.

While Facebook makes the case for more math of all kinds, Chui is particularly keen on making sure students are taught the building blocks of data science at an ever-younger age (one instructional strategy: games involving dice for elementary students).

Three years ago, he outlined his argument in a piece published by Business Insider. 

US students are shuttled along a familiar path in mathematics: first stop algebra, then geometry and trigonometry, and finally, the ultimate destination, calculus.

This time-honored curriculum seems increasingly out of touch in a world that is flooded with noisy and voluminous data. The majority of students need to be immersed in the more practical discipline of statistics, which has greater relevance for the jobs being generated by a digital economy.

The key to making sense of all the data now at our disposal is statistics. At leading companies, decisions once driven by HiPPOs (the Highest-Paid Person's Opinion) are increasingly made by conducting experiments that draw on the core skills of statisticians.

Rather than relying on gut instinct, businesses now find ways to test hypotheses and use statistical methods to analyze the results, applying the classic scientific method to decision-making.

A quick look at the total number of students taking related Advanced Placement exams highlights the ways in which K-12 schools have been relatively slow to respond to such changes: 

  • More than twice as many students still take the AP Calculus exam than the AP Statistics exam each year. In 2016, for example, 433,146 students took the former, compared to 206,563 who took the latter.
  • The number of students taking AP Statistics has increased by more than 34 percent over the past five years. While that's faster than the growth in the number of students taking AP Calculus (20 percent), it pales in comparison to the 122 percent increase in the number of students taking the AP Computer Science exam over the same period.
  • Chui would like to see those numbers change. Human beings are naturally pretty bad at understanding things like probability, he said. But in a rapidly changing labor market, in which machines will perform not only rote labor and calculations, but many more advanced tasks, humans are going to need such higher-order conceptual skills more than ever.

    "The two questions I ask people are, 'When was the last time you had to take an integral, and when was the last time you had to make a decision based on a large, incomplete, and inconsistent set of data?'" he said.

    "Usually, the answers are 'a long time ago' and 'since breakfast.'"

    See also:

    Follow @BenjaminBHerold Follow @EdWeekEdTech for the latest news on ed-tech policies, practices, and trends.



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