Almost every company, or so we are told, would be 100% aligned to become an AI Machine Learning solution provider or a consumer. That’s where the link between academia and industry requirements comes into the picture.
There are two things that are driving the modern enterprise economy. First, electricity, and the second is the internet-based data. While the first source of power has been around for a couple of centuries now, the second has come about in the last 40 years or so. However, in the last decade, data has outclassed, outperformed every other resource, when it comes to pushing the dollar value of the business chain, including the job market. It wouldn’t be a hyperbole to call data as the new “oil” of the 21st century even as trillions of data points are located every day that could change our future with a single stroke of application.
If you are fresh in this industry, starting with a data science course in Gurgaon could prepare you for the future that is still so unpredictable, but not far from being an exciting one.
Data Science: the Three Directions
Once you choose a data science course, there are three ways you can plan your career based on the application and target industries.
These are:
Data Management: Mining, Analytics, Security, Storage, Computing and Governance
AI and Machine Learning Applications
Regulations and Democratization
In 2018-2019, it was a 50-50 situation for tech companies when it came to using data analytics and intelligence in identifying their own data goals.
The focus areas for data science courses are strictly limited to the first two branches, as these are also the areas that have seen maximum funding and investments from the investors. According to an independent industry intelligence report, the growth of data science roles continues to be dynamic and driven towards making the industry more self-reliant, safe, and transparent. Nearly 60 percent of the companies are either hiring for in-house data scientists or are training and/or up-skilling they’re existing IT human resources in one or more data science expertise.
By 2025, 90% of the global IT companies would have collectively spent 200 billion dollars in developing a strong culture of data science-driven economy that values the role of data engineers, analysts, scientists, developers, and solo-coders unlike ever before.
With more and more MNCs tying up with AI and Machine Learning course providers in helping close the gap between theoretical curriculum and practical approaches, we are already witnessing direct placements from these institutions into vacancies put out in the job market.
We may have heard less of Big Data and Analytics jobs in the last 6 months or so. There has been a shift in focus to actionable insights and not merely aggregation of Big Data. Today, automated Machine Learning models to control Big Data analytics is more important. Jobs in AI and related technologies continue to drive the goods that Big Data brought to us in the last decade.
