Why 'Data Scientist' Will Continue To Be 'the Sexiest Job Of the 21st Century' As technology evolves, the exact skills and nature of jobs in data science will probably vary
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The world today is powered by data. Whether you are innovating for the future or working to improve the decision-making process, data lies at the heart of it all. With rapid digitalization, the volume of global data is ballooning rapidly. Estimates suggest that the total amount of data created, captured, copied, and consumed in the world is likely to touch 149 zettabytes by 2024, compared to just the two zettabytes generated in 2010.
Industries across the board have come to rely on data-driven insights as a way to guide their business strategy and execute day-to-day operations. Advertisers use data to decide where to invest their marketing dollars. Product designers use data-driven insights to come up with new products that are aligned with customer expectations. Data also forms the backbone of the financial services industry. In healthcare, data visualization and healthcare are helping to substantially improve patient outcomes without adding to the workload of physicians.
Not surprising then that data-related job roles and opportunities have mushroomed across the globe. Even Harvard Business Review calls the role of a data scientist as "the sexiest job of the 21st century".
Data scientists manage, mine, and clean unstructured data and prepare it for practical use. The data is then used to deliver results that impact business outcomes.
However, as the demand increases and the landscape evolves, there is a growing gap between the needs of organizations and the abilities of job candidates to fulfill those needs. Some recent studies show that there are currently close to 100,000 vacant data science jobs in India in 2020.
Upskilling to ride on the data science boom
There is plenty of discussion out there on the astronomically high salaries that data scientists command. But before jumping onto the bandwagon, it is important to evaluate your skills and strengths, and understand if they match the needs of these roles. While data scientists could hold undergraduate degrees in a variety of disciplines—be it engineering, computer science, statistics or even liberal arts—these alone might not be enough to be successful in this field.
While demand for data science talent is through the roof, there are not enough skilled professionals available to take on those roles. One primary reason for this is the lack of clarity on the skills required for different roles within the field of data science. Most companies look for individuals possessing certain specialized skill sets rather than the "jacks-of-all-trades'. In order to prepare for the best opportunities and avoid getting tagged as a "generalist', one needs to first appreciate the nuances that make these different roles unique. For instance, how is a data scientist different from a data engineer or a data analyst? Contrary to popular perception, these roles are not interchangeable.
For instance, a data scientist is one who employs advanced data techniques such as clustering, neural networks, decision trees to help derive business insights. Apart from the requisite coding skills, data scientists typically need to be adept at programming languages such as Java, Python, SQL, R, and SAS. In addition, they require working knowledge of Big Data frameworks such as Hadoop, Spark, and Pig. Data scientists also need to be familiar with new technologies such as deep learning, machine learning, etc.
As technology evolves, the exact skills and nature of jobs in data science will probably vary. However as the volume of data continues to grow exponentially, industries will increasingly rely on professionals who are well-equipped to manage the data and deliver useful insights that guide business strategy and operations. Data science is here to stay for the foreseeable future as a top career domain, and professionals with the most updated and relevant skill sets will continue to be in demand.