Every year, trends in data science are changing significantly, inviting thousands of IT and Computer science professionals to explore new avenues in the industry. The distinction between computer science and data science actually dates back to the early years of AI innovations. It slowly gained prominence in the 80s and finally made its biggest impact on computer applications in the 90s. Back in the early 1990s, data science projects mostly consisted of computer science engineers who would spend hours, weeks, and months building new codes and applications for new-age Artificial Intelligence software. Today, data science professionals are taking up a major chunk of jobs that would have earlier been hired for computer science engineers and application DevOps and analysts. What changed in the last few years or so? How did all this discussion on data science vs computer science jobs begin in the first place? And, is it something we should all be worried about?
Computers, IT and Data Operations
The way Big Data is handled in the organization has opened up the gap between computer science and data science. Data Ops is the barrier that separates the two from each other even as they may seem related specializations in the overall scheme of Enterprise Data Management. One can’ survive for long with the other missing, and it’s increasingly getting more evident why large organizations are trying to invest big time in Data Ops backed by solid IT architecture. Nobody really cares about having a computer science engineer today, because of the availability of new applications in AI, machine learning, and automation. If someone stated that AI will come for the jobs, well, it’s showing up in the way computer science jobs are getting automated and outsourced to AI software that can do practically everything that CS analysts and engineers would be doing, but much faster, and with lot more accuracy and less of fuss.
How is Data overpowering computer science?
AI is not only replacing programmers but also doing a lot more than just writing code at the machine level. AI applications created by data science teams are replacing core IT roles of the 90s already by bringing in automated managed services. And, with the coming of age of Hybrid Cloud systems, this process has only accelerated due to the heightened need for agile IT modernization and digital transformation of various business processes such as legal and compliance, marketing and sales, finance and accounting, and HR.
If we compare job titles and their salary bandwidth, data science professionals are outperforming and outpacing their peers with computer science degrees by miles.
If you are a coding professional, you have a sea of opportunities in data science, but wait – don’t give it up on the computer science domain either as these two remain highly competitive and serve a niche set of specialties that have been ruling the technology industry for some years now.
No-code is the future of computer science
If you are currently looking for a job in the data science industry, should you be thinking about data science vs computer science trends?
According to research on what data scientists think about mis webmail, a majority of the respondents agreed that it is no longer a benchmark for data analysts to carve a niche for themselves in computer science. Data science has outgrown its predecessor by shifting the ground for data-driven solutions required to harness and analyze data through software development, SaaS, and Cloud transformation journeys.
Experts in the industry are rather looking for data engineers who have their fundamentals set in stone for Operating Systems, Networking and Security management, and database operations. From a perspective of a trained data scientist, data science is increasingly moving away from computer science basics, and there is a lot happening in the Open Source environment where coders are building applications for “No Code” users.
Human programmers are building automation software powered by machines such as servers and computers to teach other computerized machines “how to code”. In 2022, the data science versus computer science debate could finally be laid to rest. Data science is winning the race, sans doubt. You should pick data science.