Looking for a career? ‘Big data’ analysts in high demand

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A swell of consumer data — from sales numbers to social media feeds — has bumped up demand for workers who can help businesses turn that information into profit, and Iowa’s universities are jumping in to help.

“You can’t pick up a newspaper or turn on the TV without somebody yelling ‘big data’ at you. It’s a reality,” said Nick Street, a professor of management sciences at the University of Iowa.

Within the last year, several Iowa universities have announced plans to start or expand academic programs to address the growth of data and the demand for workers who can tell businesses what it all means.

Drake University will roll out a new data analytics program this school year. The program comes along with plans for a $65 million, six-building complex revolving around science, technology, engineering and math.

“I think culturally we’ve become a data-driven world … we just need to have as much information as we can and figure out what’s important in that information,” said Daniel Alexander, the co-director of Drake University’s data analytics program.

“Where data science comes in is taking these vast sorts of unreadable databases and (distilling) them into something people can use.”

The University of Iowa has had a business analytics undergraduate program for a few years. Earlier this year, however, the university said it will start offering a master’s program in Cedar Rapids.

It also plans to start offering a certificate program in Des Moines.

“Everyone is collecting tons and tons of data. They don’t know what to do with it,” Street said. “They need to know how to turn it into money.”

In February, Iowa State University announced its own master of business analytics program.

‘Tease out their secrets’

Although the traditional view of “big data” involves countless numbers and rows in an Excel spreadsheet, professors at each university say they’re taking a different path.

Instead of just needing someone who can compile a bunch of figures, they said companies need analysts who can both understand the data and meaningfully interpret it to others.

“Big datasets don’t like to give up their secrets really easily, so we’re trying to train students who can collect data, who can develop these datasets, but more importantly can mine them, can understand them, can tease out their secrets,” Alexander said.

Getting at those secrets is important for all companies, especially if it leads to more sales, happier customers and a better bottom line.

“We’re looking for people that have the skills to take that data, turn it into information and then use it to make business decisions,” said Terry Lillis, chief financial officer for Principal Financial Group.

‘Crank this up’

There is already high demand for these jobs, Street and others said. It’s only slated to increase.

“Our corporate partners here are wanting more. They want us to crank this up so they can get those skills in their workplace at all levels,” Street said.

At Iowa State, Sree Nilakanta said that although the university already had classes teaching analytics, increased demand prompted a specific program.

“There is now a specific demand from companies saying, ‘We want analytics professionals,’ ” said Nilakanta, who chairs ISU’s information systems department. “It’s easier now to put a label on it.”

While some technology companies have used data analytics for years, other industries are realizing the larger implications.

“Google started hiring, Facebook started hiring and then everybody figured out that we need to get into this game,” Nilakanta said.

Fast-growing profession

The U.S. Bureau of Labor Statistics expects the employment of statisticians to grow 27 percent between 2012 and 2022, faster than the 11 percent average. Computer programmer employment is expected to grow by 8 percent.

“Everybody is looking for these types of individuals,” Lillis said.

The bureau doesn’t track specific “big data” jobs, instead splitting job projections among other fields, such as statisticians and computer programmers.

In a 2011 report, consulting firm McKinsey & Co. projected the United States would have a shortage of 140,000 to 190,000 people with “deep analytical skills” who would know how to analyze big data.

Job search site Glassdoor.com puts the national average salary for business analysts at about $65,000 a year.

Part of that increased demand, Street said, comes from the need to have people familiar with data in all parts of a company.

“The tradition is, you hire one or two Ph.Ds and you expect all kinds of brilliance to come out. Well, that’s not sustainable,” he said. “You need people to know how to think with data at every level of the organization, and that’s what they’re looking for.”

Read more @ http://www.desmoinesregister.com/story/tech/2015/08/02/iowa-universities-data-business-analytics-programs/31034415/

Source: Looking for a career? ‘Big data’ analysts in high demand

Study claims 1 in 4 cancer research papers contains faked data

You could be forgiven for thinking there’s a bit of a crisis going on in biomedical science these days. Tenured academic positions are few and far between—and are often dependent upon the researcher’s success in obtaining scarce funding. The pressure to succeed, measured by publications, is sometimes blamed for leading less-scrupulous scientists to break the rules. A new paper by Morton Oskvold, a Norwegian scientist, will fan those flames, as it makes the bold claim that 25 percent of cancer biology papers contain duplicated data. Is something rotten in our research labs?

There has been a real uptick in scientific misconduct in recent years, but it’s not going unchallenged. Post-publication peer review, where papers are critiqued publicly on the Internet by other scientists, is putting the literature to the test. And journals are taking a tougher line with authors to ensure that they include all the relevant details, not just the ones that make them look good.

Some of this comes in response to high-profile publications like one from researchers at the biotech company Amgen, who tried to reproduce the findings of 53 “landmark” preclinical cancer research papers but were only able to do so for six of them.

Oskvold’s paper, published in Science and Engineering Ethics, looked at cancer biology papers published in three journals (International Journal of Oncology, Oncogene, and Cancer Cell) during 2013. He selected 40 papers from each journal at random and then systematically examined the data in each, looking for images (or elements in images) that appeared more than once. In papers where these elements were found, Oskvold then dug deeper, also looking at other publications from the same authors to see if there was evidence of reused data.

The images Oskvold focused on are photographs of Western blots (where proteins are separated by weight and labeled with antibodies) and microscope images (again, often labeled with fluorescent antibodies).

The results are rather startling—a quarter of the papers showed identical images in two or more figures, a finding that was consistent across all three journals. However, once one digs a little deeper into the results, some of the findings that Oskvold calls problematic turn out to be a bit less clear-cut. That’s because the data duplications fall into one of two categories. Just over half of the papers with duplications pass off the same image as two completely different experiments. That is clearly outside the bounds of acceptable behavior for scientists, and bravo to Oskvold for calling them to account.

This kind of thing is not OK. The same data is presented as different experiments in two different papers.
This kind of thing is not OK. The same data is presented as different experiments in two different papers.
This is a much less clear case. Many researchers would tell you there was no problem splitting up a gel into sub-figures like this.
This is a much less clear case. Many researchers would tell you there was no problem splitting up a gel into sub-figures like this.

But in the other cases, the duplications are data from the same experimental conditions. For example, using a subset of a Western blot in one figure, then another subset (including the same control) in a second figure. Oskvold calls the publication into doubt because it raises uncertainty about whether or not sufficient experiments were actually performed—it’s not enough to do it once, shout “eureka!” and send off the manuscript. But many other scientists take issue with this hardline view, something evident from a lengthy discussion of Oskvold’s findings at PubPeer (Oskvold is Peer 1).

There are legitimate reasons for reusing the same data in more than one figure. As mentioned, budgets are tight, reagents aren’t cheap, and it’s often prudent to run a Western blot with eight or ten (or more) samples at once. However, dumping all this data out at once might not be the most effective way of communicating a researcher’s results; using subsets of an experiment to communicate specific points may be more effective. In fact, there’s evidence of exactly this kind of duplication in one of Oskvold’s own publications.

Oskvold contacted each of the journals about his findings, as well as the authors for the 29 papers where he found duplication (he also started PubPeer threads for each one). Only one of the authors responded (accepting responsibility for mixing up the images), along with a second unverified author (who claimed the journal made the error during page layout). He didn’t hear back from any of the three journal editorial boards.

While we don’t think that the initial claim—a quarter of cancer research is fake—is accurate, the fact that it’s closer to one in eight should still be troubling. A lot of responsibility rests with the authors who write these papers, as well as the reviewers and journal editors who accept them for publication. With bandwidth and storage as cheap as they are now, there’s no good reason why one shouldn’t be asked to submit the raw data for each experiment when submitting a paper.

Sadly, the pressure to puff up one’s findings probably isn’t going away any time soon. So, unless there’s an organized strengthening of standards, problems like these probably won’t go away either.

To read the original article on ars technica, click here.

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Big data analytics startup Sqrrl raises $7M

Sqrrl, a Cambridge-based big data analytics startup, has raised $7 million in Series B funding and also unveiled a new software aimed at detecting and responding to cybersecurity threats.

This brings total funding to date for the company to $14.2 million, with investors including Rally Ventures, Atlas Venture and Matrix Partners.

The company says it makes software to uncover hidden patterns, trends and links in data. On Wednesday, Sqrrl also announced the launch of its new software, Sqrrl Enterprise 2.0, which focuses “on the challenges posed by cybersecurity threats and vulnerabilities that nearly every organization faces today.”

“Sqrrl is at the intersection of two of the most important trends facing the enterprise: cybersecurity and Big Data,” said Zenas Hutcheson, partner at Rally Ventures. “Sqrrl’s technology can help both Fortune 1000 companies and government agencies prevent themselves from becoming the next cyber incident headline story.”

The company’s customers include several undisclosed Fortune 500 companies and large government agencies.

Thirty-five employees work at the company’s headquarters in Cambridge and Sqrrl plans to hire 25 more this year, according to Ely Kahn, co-founder and Director of Business Development at Sqrrl.

Originally posted via “Big data analytics startup Sqrrl raises $7M”

Source: Big data analytics startup Sqrrl raises $7M by anum