The last two decades alone have created 98% of all data that has been 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. With the world experiencing such a rapid influx of data, it needs huge numbers of Data Science professionals to handle it. The fact is that all technology domains and industry sectors are looking up to Data Science to guide them into the future. We need Big Data dreamers to ensure that cardiac arrests be preempted, supply chains can be sharpened, and robots can become closer to humans.
Whether it’s equipping the meteorology departments for more accurate and even real-time predictions on climate change, and earthquakes, or diffusing pandemics, Data Science professionals have the capacity to do it all.
Outsourcing Big Data AnalyticsView DASCA Insights
Data Science has branched-off into multiple specialized tracks in the last decade to help global data factories rebuild themselves into producers of Business Intelligence for corporations, government agencies, and other organizations. Though all Data Science professional or specialization tracks still retain their traditional foundations, the methods and tools used to manage big data have changed dramatically – as the scale, complexity, 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, but it has also generated new categories of interdisciplinary roles and spawned an era of Data Science career tracks – many of which demand multi-dimensional competence. The following are the most important Data Science career tracks to have emerged in the last decade:
Data Scientists blend their knowledge, experience, and insights on business, marketing, specialist subjects, and management with their expertise in research and statistics to explore, define, and innovate ways through which data can be used by organizations to make more accurate, meaningful, and real-time decisions.
They also architect the analytics ecosystem required to convert petabytes of Big Data into solid chunks of Business Intelligence for corporations and governments.
Big Data Analysts model and analyze huge chunks of data in sync with their objectives by using their skills in mining, researching and analyzing data on specialized statistical and mathematical analytics tools.
They deploy their knowledge and understanding of business and management fundamentals to generate insights from their analysis.
Big Data Developers/Engineers design, code, program, and build specialized software applications used in handling and managing Big Data.
They use their skills in programming and software testing to work on popular Big Data platforms such as Hadoop and Spark, and also have knowledge about domain and analytics tools and techniques.
Despite the fact that Data Architects have been around for some time now, it can be argued that they are only now entering the most lucrative phase of their career.
Big Data Architects do all the essential planning and coordinate data resources in an organization. Knowing about application architecture acts as a plus point to advance the career of a Data Architect.
They combine their experience, skills, and expert knowledge about data systems with their exposure to data management processes and data analytics to help build the infrastructure required to cluster, segregate, distribute, mobilize, and transfer Big Data among various stakeholders.