You could wait for the next promotion, but that would take some time.

If changing job is your next step, then this is the right time to explore the number of opportunities in data science technology.  Currently, the buzzword in the tech industry is big data. It is not only contributing $15 trillion to global GDP by 2030 but is also going to generate $47 Billion in the next two years.

Exciting, right?

To add to it, companies like Facebook and Amazon are investing heavily in big data to make sense of thousands, if not, millions of data sets to get valuable insights from it.

Well, so how can you benefit from it?

With more investment, there is going to be an even higher demand for specialists who can take complex data and provide valuable insights into it.

Organisations value such experts and they are also well paid. A six figure salary is only the starting, and you can also switch between different roles that interests you in the course of your career. Some of them are data engineer, big data engineer, business analyst specialist, etc.

Using big data to provide simple solutions to customer problems by looking at past customer data is also sought after by retail companies. Not to be left behind are education, manufacturing, and others like construction industry. It has led to high demand and a shortage of supply of big data experts. In the next two years along there is going to be a shortage of 140K to 190K people with in-depth analytical skills.

So, are you ready to take your career to the next level? The following infographic can give you the knowledge of interesting big data careers.

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Olivier Dumon, MD of Academic and Government Markets at Elsevier, kicked things off by talking about their transition from print publishing to digital analytics, and how the acquisition of Mendeley and the partnership with UCL tie into Reed Elsevier’s future strategy for innovation.

“Eventually data will surpass crude oil in importance,” said Claude Kirchner from Inria (a public research body dedicated to digital science and technology), talking about the rising popularity of MOOCs (Massive Open Online Courses) and the widespread benefits that can be achieved by gathering insights from big data into the process of learning itself.

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For the better part of half a century, educators have struggled with how best to assess student learning and make curricular changes based on such assessments. In recent history, we’ve used testing (and more recently standardized testing) in order to figure out what we need to teach to which students.

This process is incredibly clunky and not very accurate. Take for example testing a fourth grade class on their knowledge of the civil rights movement. The fact that Phillip scored significantly below the class average tells us nothing about what we can do to actually help Phillip learn the material. Our best guess with this kind of assessment is to tell Phillip to “study harder next time.” Educators call such testing summative assessment because it’s a snapshot of the endpoint of a learning process.

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By extension, a considerable amount of energy and resources have recently gone into finding ways to apply technology and big data in education. For instance, two companies are claiming predictive analytics can determine a teacher’s success before he or she teaches a single lesson. Similarly, some college administrators claim that by using big data algorithms, they can predict which applicants will be “high yield” students before they’ve even been accepted. And, most notably, Common Core uses big data to standardize curriculum across all socioeconomic levels.

These examples point to some exciting applications for big data and technology in education, but focusing on bigger data and more metrics may not always be the right approach. More than big picture objectives, trends, or data points, students are unique individuals with varying strengths and weaknesses. As such, they deserve support at a personal, individual level. When technology is applied in ways that can focus on a unique individual over a large group, a teacher can monitor student understanding in real time — and that’s the personalized approach to instruction our education system should strive for.

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Misfolding proteins that cause Alzheimer’s disease and the mysteries of dark matter and dark energy are just a few of the projects that generate massive amounts of data. To unlock the answers that the data holds, researchers will have to work together across different disciplines and countries.

“In this era of big data and big science, universities must serve as a crossroads for collaboration more than they ever have,” said Shirley Ann Jackson, president of Rensselaer Polytechnic Institute, during a general session at the 2014 Internet2 Global Summit in Denver on Tuesday, April 8.

This crossroads for collaboration doesn’t just mean that researchers should talk to each other. It also means collaboration between research and IT in a way that doesn’t always happen, said Michael McRobbie, president of Indiana University. Researchers need the support of IT leaders who take the time to understand what’s needed technologically and can then provide it.

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But something has been nagging at the back of my mind for a while now about all of these data. It’s not the usual thing…I’m not particularly concerned about privacy. I don’t spend time worrying about the commoditization of learning. And I don’t buy into the conspiracy theories about the so-called “big data” companies getting rich off the backs of kids and teachers.

What I do think about is the abilities of the people who are analyzing and reporting on those data to really understand what the data mean. I wanted to share three stories of data misuse or misinterpretation that I’ve encountered over the course of my career to give you an idea of why this concerns me. Needless to say, some of the details have been modified slightly to protect people’s identities.

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InBloom’s company spokesperson told Politico the nonprofit was “pushing forward with our mission,” though at the moment there are no known state partners.

InBloom’s trajectory has shined a spotlight on the public’s sensitivity around what happens to student data. When it first began as a mammoth ed-tech project in 2011 by the Council of Chief State School Officers, the Bill and Melinda Gates Foundation and the Carnegie Corporation called the Shared Learning Infrastructure, the purpose was to provide open-source software to safely organize, pool, and store student data from multiple states and multiple sources in the cloud. That included everything from demographics to attendance to discipline to grades to the detailed, moment-by-moment, data produced by learning analytics programs like Dreambox and Khan Academy. An API — application programming interface — would allow software developers to connect to that data, creating applications that could, at least in theory, be used by any school in the infrastructure.

In February 2013, just a little over a year ago, SLI relaunched as an independent nonprofit named InBloom. The company had nine state partners, Colorado, Delaware, Georgia, Illinois, Kentucky, Louisiana, Massachusetts, New York and North Carolina, representing 11 million students. At SXSWEdu, they made a splashy public debut the following month, hosting parties and panel discussions as an official sponsor, a gathering focused just as much on business as on education.

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But imagine the underside to this seeming success story. What if the data collected by the software never disappeared and the fact that one had needed to take remedial classes became part of a student’s permanent record, accessible decades later? Consider if the technical system made predictions that tried to improve the school’s success rate not by pushing students to excel, but by pushing them out, in order to inflate the overall grade average of students who remained.

These sorts of scenarios are extremely possible. Some educational reformers advocate for “digital backpacks” that would have students carry their electronic transcripts with them throughout their schooling. And adaptive-learning algorithms are a spooky art. Khan Academy’s “dean of analytics,” Jace Kohlmeier, raises a conundrum with “domain learning curves” to identify what students know. “We could raise the average accuracy for the more experienced end of a learning curve just by frustrating weaker learners early on and causing them to quit,” he explains, “but that hardly seems like the thing to do!”

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Edutech solutions have long promised to make teachers’ work easier and improve learning outcomes – but what’s new is the enthusiasm that higher education CIOs and administrators express for Big Data analytics. (You know you’re nearing the top of the hype cycle when non-technical types start asking for your opinion on the ‘next hot technology’.)

Here are three key areas that tertiary campuses are focusing their attention on as they seek to harness their digital assets using Big Data analytics:

1. Student Engagement
The University of Tasmania (UTAS), a public Australian university with a student population of 26,800, has recently deployed a Learning Management System (LMS) that tracks learning engagement.

“The LMS allows us to monitor how often a student logs on to the system, which pages were visited, how much time was spent on each page and the individual’s progress over time,” UTAS CIO Jeff Murray told me.

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Some of these big data applications are in the classroom, such as the creation of adaptive learning courses that guide individual students through lessons in the way most likely to promote individual success, but other applications are aimed squarely at administrators and the schools they work for. Those data apps help principals and superintendents hire the teachers most likely to improve educational outcomes, track trends in educational giving and, at the college level, correlate majors and classes taken to long-term career success. The message: Big data can be transformative for education.

Designed for use from the upper elementary grades into college, McGraw-Hill Education is developing digital curriculum and course materials that take data gathered from 2 million students and use artificial intelligence to create adaptive learning experiences tailored to individual student needs. As a student reads and answers questions, the system tracks what the student doesn’t know and then presents that material. The system knows what questions to ask, and in what order, to maximize long-term retention.

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However, the road to becoming computer and information research scientists is a long one that requires individuals to pick up numerous skills along the way. Many colleges and universities understand that to prepare students for careers such as these, more has to be done. Fortunately, technology giant IBM also recognizes the importance of equipping today’s degree seekers with the knowledge and skills they’ll need to be tomorrow’s data scientists.

IBM’s Efforts

Overall, IBM has formed partnerships with more than 1,000 academic institutions worldwide, according to an August 2013 press release. Through this global network of schools, which includes Georgetown University, the University of Missouri, Dublin City University and the National University of Singapore, IBM aims to enhance students’ knowledge of big data and analytics. The academic initiative’s goal is to ready learners for the 4.4 million big data jobs that are expected to exist by 2015.

In an interview, Jim Spohrer, director of IBM Global University Programs, explained how important big data is becoming to professionals in multiple sectors.

“You’ve got all this big data, then the question becomes who has the expertise to use it,” Spohrer said. “All professionals – doctors, lawyers, scientists – have to learn how to use big data better.”

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Moderated by Nick Morgan, Executive Director of the Strategic Data Project at Harvard’s Center for Education Policy Research, the mouthful of a panel — Opening Doors to Advance Analytics for Your Agency — provided equal parts perspective from research, industry, and practice on transitioning to a data-driven education. Here are a few takeaways from the panel, which also included Atnreakn Alleyne, Data Fellow at the Delaware Dept of Education and Jeff Watson of Versifit Technologies:

Data, Insights, and Action

Finding utility in data is just as much about asking the right questions as having good data. Alleyne explained that in Delaware, they share monthly data briefs with the public to ensure they’re tacking the questions that matter. Of course it can be an unpleasant experience at times for administrators when the public sees empirical evidence that the least academically prepared students in Delaware are more likely to be placed with inexperienced teachers, or that low-SES students are less likely to enroll in college even when they are deemed college and career ready. But this level of sharing and community building can significantly influence policy and allocation of resources for traditionally low-performing school populations.

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