Should High Schools Teach Big Data? - InformationWeek

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Should High Schools Teach Big Data?

Given the anticipated shortage of data scientists, some high school educators have jumped in to expose students to big data concepts.

Given the anticipated shortage of data scientists, should students start learning the precepts of big data in high school?

Changing when advanced database technology is taught has real-world implications, given the realities of today's job market. Both data analytics and big data skills are in high demand in private industry and government.

But there is a looming shortage of workers with these abilities. McKinsey & Co. sounded this alarm back in 2011 with its seminal report that predicted the U.S. would face a shortage of 140,000 to 190,000 workers with the skills to manage and analyze big data.

The popular technology job board has seen a spike in listings for "data scientist," up from just a handful a year ago to more than 35 at the start of October. While still an imprecise job designation, "data scientists" command high salaries compared to other IT job titles. (Separately, the unemployment rate for technology professionals dropped in the third quarter to 3.3%, as compared to 4.2% in the same quarter a year ago, according to the Bureau of Labor Statistics.)

These listings--many of which request a PhD in fields like mathematics, economics, or statistics--today cluster in financial services, retail, and e-commerce. Job listings using the phrase "big data" have increased from around 200 in January to nearly 800 in October.

"But increasingly, every industry is dealing with big data questions," said Alice Hill, managing director of and president of Dice Labs.

[ Do you know how to find big data job listings? See Data Scientist Jobs Hiding Under Less Sexy Titles. ]

Preparing the U.S. for future high-tech jobs, specifically ones oriented around data, was also a focus of TechAmerica Foundation's Big Data Commission.

Analytics and data science are central to making "business, the global economy, and our society work better," Steve Mills, senior VP and group executive at IBM, and co-chair of the Big Data Commission, said in a statement. "That's why it's critical that our country prepares a new generation of experts who know how to corral today's data deluge for world-changing insights."

The commission's new report, "Demystifying Big Data: A Practical Guide to Transforming the Business of Government," included the following recommendations for skills development: Strengthen and expand public-private partnerships to invest in skills-building initiatives for the federal workforce in the area of big data. These should include formal career tracks for IT managers; an IT leadership academy to provide big data and related training and certification; data-intensive degree programs; and scholarships to prepare a new generation of data scientists.

Big Data In High School?

Among those trying to push big data classes down to the high school level is Alex Philp, PhD. Philp is founder and CTO of TerraEchos, a developer of advanced intelligence and surveillance security systems. Philp has been working with the high schools in Missoula, Mont., and has even created a scholarship program at one--Sentinel High School--to introduce these topics to computer science students.

"[We] cooked up the idea of promoting a merit-based, micro-challenge grant at Sentinel," Philp explains. Currently, four student teams have been awarded grants. "Ultimately, I hope these high school students feed into opportunities at the University of Montana and then ultimately into the most exciting businesses and markets involving big data," Philp said. "This also relates to aspects of the overall economic competitiveness of our country and a renewed commitment to science, technology, engineering, arts, and mathematics (STEAM) competency in our country."

Meanwhile, at the university level, Philp has been working with Eric Tangedahl, IT director for the school of business administration at The University of Montana, to create a multidisciplinary course, now in its first year.

"The course draws students from computer science, management information systems, and math," Tangedahl said. "We felt that to tackle these problems, students have to work together in teams, teams with different skill sets," he said.

The three-hour class, now with 18 students, is half lecture about big data and half lab--specifically around IBM Infostream, a programming language that takes advantage of IBM's DB2 and WebSphere platforms.

Another high school on this path is the science and engineering magnet (SEM) school at Yvonne A. Ewell Townview Center in Dallas. SEM, ranked by Newsweek this year as one of the best high schools in the U.S., recently participated in IBM's annual Master the Mainframe Contest for high school students in the U.S. and Canada.

Marilyn Cadenhead, who teaches Advanced Placement computer science at SEM, has many students participating in the contest.

Cadenhead says her students use technology very effectively, and already have a computer-mediated learning style. "This is a digital age, very different from when I was in school," she wrote in an email. "I learned to type on a manual typewriter. If we as teachers do not engage [students] with the latest technology and teaching styles, we will be 'boring' and the students will not be motivated to learn."

"I want my students to know what is going on in the real world," Cadenhead concludes, explaining her enthusiasm for the IBM mainframe contest, as well the annual IBM Innovation summer camp that some SEM students attended this year.

IBM's Innovation summer camp Facebook page describes the program this way: "The students will gain hands-on experience with visualization, mobile application development, Linux, and DB2 coupled with demos of research underway at UTD in mind control, gaming, and robotics."

Other observers, however, point out that big data analysis is powerful precisely because it is about more than raw technology. The highly valued professionals in this space are those who ask the right questions, who can see business-relevant answers inside the data. That kind of maturity and domain expertise is beyond the capacity of high school students, they say.

The University of Montana's Tangedahl concurs. "I wouldn't throw high school student in and expect them to come up with a [big data] program for the Defense department," he said. But, he adds, "you've absolutely got to put the building blocks in place." He recommends adding data statistics and programming classes in high school to better prepare students for college-level classes, like his, that push teams of students to answer "real-world problems."

Like Tangedahl,'s Hill isn't sold on the idea of pushing big data instruction to high school students. She thinks students should learn relevant technologies, such as data interpretation, data extraction, and data modeling. "It's never too soon to learn those skills," she said.

But TerraEchos' Philp disagrees, arguing it is essential that educators encourage excellence in students at all ages, and not set limits.

"I've spent many years of my life working with thousands of students at various ages, attempting to raise the bar," he said. "High school kids are proposing [projects] that are as good as I'm seeing in college." With motivation, inspiration, and passion, he said, "my experience is, students rise to the occasion."

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User Rank: Apprentice
3/22/2015 | 9:54:10 AM
Focus is Wrong
At the end of the day we have put an end to this fad-ish culture and see the bigger picture - the likelihood that the Data Scientist jobs that are being created will be in abundance when students graduate is very low. Even if I saw a report from a respected consultancy projecting that a 13 year old studying big data that's likely to be able for a maths degree will be able to get a job in big data after a PhD (ie 14 years time) or after just a primary degree (10 years time), from experience in predicitive modelling, you can't really trust projected data, even simplistic projections much beyond 2-3 years.

What would be more beneficial is to stop this flawed idea of tailoring education to employment trends, which are usually misunderstood and temporary at best. I mean when I was in secondary school it was "oh, IT is the way to go", but by the time I'd graduated there had been TWO RECSSIONS in the field. People need to wake up to fluidity and prepare accordingly.


Better would be to address the fact that I have to explain to people why rebutting with "but there's loads of data science jobs out there" doesn't even adress my point, never mind rebutt it, or even validly (I mean look up the definitions of "argument", "rebuttal" and "but" if you're confused). Aalso prefferable is to teach data analysis to educate and end the current culture where the media run rings around the public by misquoting statistics for their own purposes.I mean, it can't be that difficult to change education so kids ask more questions that an experienced worker would? Not only is this more beneficial, but this is the trait emplohyers look for in data scientists that no course teaches - and it can be taught in the classroom, contrary to this popular myth that some things can only be taught "on the job". Just toughen up on the students and get across that attention to detail is key.
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