A range of Big Data profession have emerged 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 the technical Big Data profession still retain their traditional flavor, the methods and the tools of managing 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 Big Data career tracks – many of these demand multi–dimensional competence.
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 how data can be used by organizations for making progressively 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.
More than 10% of the total global demand of 5 million Big Data professionals by 2017 is for Data Scientists.
Big Data Analysts model and analyze huge chunks of data in sync with desired objectives using their skills of mining, research, and analysis of data on specialist statistical and mathematical analytics tools.
They deploy their knowledge and understanding of business and management fundamentals to generate insights out of their analysis.
More than 60% of the total global demand of 5 million Big Data professionals by 2017 is for Big Data Analysts.
Big Data Developers/ Engineers design, code, program, and build specialist software applications used in handling and managing Big Data.
They use their skills in programming and software testing etc., to work on popular Big Data platforms like Hadoop and Spark, and are also required to have knowledge about business basics and analytics tools and techniques.
Around 500k Big Data Engineers would be needed worldwide by 2017 according to a conservative DASCA estimate.
Despite the fact Data Architects have been around for a couple of years, they are now quickly entering probably 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 in the career of Data Architect.
They blend 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 for clustering, segregation, distribution, mobility, and transfer of Big Data among various stakeholders.
Around 5% of the total global demand of 5 million Big Data professionals by 2017 is for Big Data Architects.
Big Data Administrators are responsible for erecting, managing, and maintaining the infrastructure and ecosystem of a Big Data management set up.
They blend their knowledge, experience, and skills on server administration, security, network planning with their exposure to data management processes to also handle clustering, segregation, distribution, mobility, and transfer of inbound and throughput data to ensure data keeps reaching intended points without disruption, contamination, and risk.
Approximately 300k Big Data Administrators are required worldwide in 2017, if we are to leverage the Big Data promise to its fullest.The other tracks are those of Data Management, Data Visualization, NoSQL management, and Data Warehousing management.
Would you learn a language that only a few can understand? Would you buy a pen that can write only on one brand of paper, made by one particular company? Would you learn music that you can play only on ONE brand of guitar?
Go for a certification that prepares you for most kinds of data science jobs, anywhere in the world, on almost any technology or platform and in any type of company.
That’s why a certification that is NOT by a technology company is the BEST certification for you.
This is because a technology company ONLY puts its own platform or tools in the curricula, and you do not get the opportunity to learn all other platforms or technologies being used in Data Science today.
So, you must always choose a data science certification that has a vendor-neutral curriculum based on a global standards’ framework, and equips you for cross-platform professional work.
If you have what it takes, Data Science presents the best career opportunities today:
The demand-supply gap for Data Scientists is huge globally, and for professionals with the right qualifications, training and experience, landing a Data Scientist's job is easier than most other roles at senior levels
Data science jobs across all levels usually pay higher salaries than other tech jobs at the same levels
Most organizations and companies require Data Scientists, so there is a growing demand
Data Science and Analytics are also jobs of the future, as the importance, need and demand for data scientists will only grow
Data Science is a growing field with use across all industries. For students of technology, statistics and disciplines like econometrics, there is probably no career better than Data Science. Even for students of other disciplines with no career interest in technology or data science, Data Science knowledge is still important, because it helps them perform better in their careers.
In the years to come, knowing Data Science will become as important for everybody as knowing computers and the Internet is today. Graduating students with knowledge and qualifications in Data Science are more attractive for all employers. There is no doubt that even for students looking at non-technical or non-data science careers, a qualification in Data Science is among the most important supporting qualifications to have.
For students majoring in information technology, computer engineering or computer science, there is probably no career better than Data Science because this field is:
Data Science is a growing field with use across all industries. Data analytics, one sub-domain, is critical for all organizations today, and this field has job opportunities open even for students majoring in non-technology disciplines like economics, statistics, mathematics and econometrics. The fact is that for students who love numbers and business and have an interest in computers and technology, there is probably no career better than big data analytics today.
On the other hand, even for students of other disciplines with no career interest in data science or technology, knowledge of Data Science – particularly of big data analytics – is still important, because it helps them perform better in their careers. Make no mistake – in the years to come, knowing Data Science will become as important for all jobseekers and students as knowing computers and the Internet is today. Trends around the world clearly indicate that graduating students with knowledge and qualifications in Data Science are more attractive for all employers. Increasingly, a qualification in Data Science is emerging as the most important supporting qualification to have even for students looking at not-technical or non-data science careers.
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