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The Big Data ERA

Big Data is the biggest boon yet for people who take mission-critical decisions, and for machines that try to think like humans do. And help discover impossible-looking things like gravitational waves.

The 21st century alone has created 98% of all data that humankind has piled up since we started writing history. In 2010, Google's Eric Schmidt estimated that the world created around 5 Exabytes of information between the dawn of civilization and 2003. Now that same amount is created every two days, the world definitely needs huge numbers of Analysts and Engineers as well as Data Managers to take care of such an enormous amount of data. The fact is that all technology domains and industry sectors appear to have shrunk around Big Data. Software Programmers, Networking Professionals, Project Managers, Testers, and even Digital Marketers and Market Researchers are looking up to Big Data for giving them a quantum leap in their respective professions.

And we certainly need these professionals in a big way. How else will our cars go driverless, cardiac arrests be preempted, and aliens be tracked swifter, if at all? Who is going to equip the meteorology departments for more accurate and even real–time predictions on climate change, twisters, and earthquakes? We need Big Data dreamers and dream catchers to ensure pandemics can be diffused; supply chains can be sharpened, sped up, leaned down further; and robots can become agonizingly so human. And some say, Big Data can even make perfect babies and help choose great presidents!

But going back a little, we believe the very first warnings of data becoming big were flashed in 1944 even as the world was busy trying to end its 2nd big war. And by what he said then, Fremont Rider proved he was not a mere librarian of the Wesleyan University by any count. He was a yarn–spinner, some said, when they heard Rider scaring Yale out of its boots by his estimates that by 2040, the Yale library would creep across 6,000 miles, creating the requirements for 6,000 staffers for cataloging alone!

But the advent of the Big Data era was practically heralded in 2005, when Doug Cutting's open source Big Data cruncher software, Hadoop, was recast by the Yahoo! Spinoff, Hortonworks. Big Data has not looked back ever since, creating a demand for its professionals in millions and spawning hundreds of Big Data startups. By 2018, the USA alone is expected to face a shortage of 140–190k Data Scientists as well as 1.5 million Data Managers (McKinsey report in 2011 "Big Data: The next frontier for innovation, competition, and productivity")

Big Data appears the biggest godsend for the passionate and the ambitious this side of the century. And DASCA stands solidly right behind to help such professionals make the most of the Big Data era.


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Did You Know?

Even as Steve Bryson and Roger Mougalas may fight on who used or coined the term Big Data first, the thing itself has exploded and expanded to become much bigger than big.

The Future of Data Science Professions

The DASCA view of the rapidly changing and expanding Data Science career landscape serve as clear, loud hints on how professionals, employers, and educators should prepare themselves for leveraging the phenomenal Big Data promise.

Data Science has branched off into multiple specialized tracks in the last ten years to help global data factories rebuild themselves into producers of Business Intelligence for corporations, government agencies, and other organizations. Though at their core, all Data Science professional or specialization tracks still retain their traditional flavors, their methods and tools of managing big data have changed dramatically – as the scale, complexity, volume, velocity, and variety of data keep exploding through the roof – both, confounding, and exciting decision-makers at the same time.

The transformation of data into Big Data has not only added to the complexity of conventional data jobs, it has also generated new categories of interdisciplinary roles – and spawned an era of several new Data Science career tracks – many of these demand multi–dimensional competence.


The following are the most important Data Science career tracks to have emerged in the last decade:

Data Science+
Data Research & Analysis +
Data Engineering & Development+
Data Architecting+
Data Administration+