The DASCA view of the rapidly changing and expanding big data career landscape serves as clear loud hints on how professionals, employers, and educators should prepare themselves for leveraging the phenomenal big data promise.
A range of Big Data professions has 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 technical Big Data professions 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, but it has also generated new categories of interdisciplinary roles – and created an era of several new Big Data career tracks – many of these demand multidimensional skills and competencies.
A career in data science is rewarding and lucrative since the demand for and availability of individuals with data-driven decision-making and data science skills has skyrocketed. It is an interdisciplinary field that uses scientific algorithms, processes, methods, and systems to extract insights from a broad range of AppDomains. Since 2012, the data science industry has witnessed a massive hike of 650 % far outpacing other industries.
Data science professionals require essential technical and non-technical skills such as evaluation of data, understanding the data structure, analyzing and visualizing data, writing concise codes, and most importantly work as part of a team.
These professionals also architect the analytics ecosystem required to convert petabytes of Big Data into solid chunks of Business Intelligence for corporations and governments.
The top job roles in data science are:
The demand for data science professionals is expected to increase by 28% by 2026.
A career in Big Data Research and Analytics is high in demand as it focuses on analyzing and uncovering useful data from hidden patterns and trends so organizations can make informed decisions and gain a competitive advantage.
Professionals in this field model and analyze huge chunks of data in sync with desired objectives using mining, research, and data analysis skills on specialist statistical and mathematical analytics tools. Furthermore, they deploy their knowledge and understanding of business and management fundamentals to generate insights out of the analysis.
The top job roles in big data research and analytics are:
More than 60% of the total global demand of 11.5 million data science jobs by 2026 is for Big Data Analysts.
A career in Big Data Engineering is one of the most talked-about in the technology industry as this field deals with storing big data correctly and safely, thereby interacting with massive databases and data processing systems in large-scale computing environments.
Organizations facing a data deluge, with large data lying around in multiple formats require data engineering professionals to build big data reservoirs and fault-tolerant distributed systems to process and store rapidly changing data streams.
Professionals in this field require Hadoop, Machine Learning, NoSQL, NLP, Statistical Modeling, and Python development skills to design, code, program, and build specialist software applications used in handling and managing Big Data.
Furthermore, these professionals work with IT teams and big data architects on formulating project goals along with designing predictive models, top-tier algorithms, and prototypes.
The top job roles in this field are:
Over 70% of companies globally will be hiring big data engineering professionals to handle massive data as they will shift from big to wide data by 2025
Big data architecture is a foundation for big data analytics. It is an overarching system used to handle large amounts of data so that it can be evaluated for steer data analytics, business purposes, and provide an environment in which data analytics tools can extract important business information from ambiguous data.
Big data architecture is designed to manage the processing, ingestion, and evaluation of data that is complex for traditional database systems.
Professionals in this field are responsible to provide the framework that replicates the needs of big data of an organization using skills to operate data, software, and hardware, cloud services such as Hadoop, MapReduce, oozie, MongoDB, and several other IT infrastructures to align the IT assets of a company with its business goals. Furthermore, these professionals work with domain experts to stay on track and put together a delivery plan.
The top job roles in big data architecting are:
The demand for big data architecting professionals is expected to increase by 9% than the average of all other occupations in big data between 2018 –2028
A career in Big Data Administration is high in demand as businesses require professionals to take care of the entire infrastructure and manage all the tools related to the big data ecosystem.
Professionals in this field are responsible for erecting, managing, and maintaining the infrastructure and ecosystem of a Big Data management setup. They blend their networking, Linux OS, Unix knowledge and skills in 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.
The top job roles in big data administration are:
Approximately 700k Big Data Administrators are required worldwide in 2026 if we are to leverage the Big Data promise to its fullest.
The best data science certification is one that:
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 the skills and qualities, data science can be a highly sought-after career with tremendous opportunities for advancement. The following are some key highlights of this career.
The global demand-supply gap for data science professionals with the right qualifications, training, and experience is huge. According to the US Bureau of Labor Statistics, by 2026, data science jobs are expected to grow by 27. 9 percent. Firms are actively looking for data science professionals while the supply is low.
Data science jobs across all levels usually pay higher salaries than other tech jobs at the same levels. According to recent Payscale reports, the average salary of a data scientist is USD 97,658 per year.
Data science professionals can work in a variety of industries. Any organization that uses data to drive decisions is an opportunity for you.
There will only be an increase in the importance, need, and demand for data science professionals as companies become more data-driven.
Data science is a fast-paced developing career choice with massive versatility and evolving job profile. 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 as knowing computers and the Internet is today. From predicting potential side effects of a medicine to optimizing a ship's path in the ocean, data science is everywhere in the real world. These advancements lead to more employment opportunities for freshers or young professionals.
Data science offers diverse job roles such as a data analyst, data scientist, big data engineer, and so on. To get into this field you must:
The fundamental non-technical abilities that employers typically want from a data science professional are as follows:
The future automated workforce will value soft skills, paving the way to sharpen and cultivate data science performance.
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