Vijay Pasupuleti, CEO, OdinSchool and GreyCampus

Vijay Pasupulati is an edupreneur and a votary of technology who serves as the CEO of GreyCampus and OdinSchool. Former CEO of Winzest Edutech Private Ltd. and the former Vice President at Goldman Sachs, Pasupulati discovered his love for edupreneurship during the 2008 global economic crisis. GreyCampus, his first brainchild, is a global player in the edtech industry that powers people’s careers in technology and business areas. With over 7 years of experience in the market, his organization has now trained more than 100,000 professionals across 50 countries. An alumnus of New York University Stern School of Business, Vijay Pasupulati holds an MBA in Finance, Information Systems from NYU and BTech from NIT, Warangal.


The field of engineering is never going obsolete. In fact, the world always needs consequential engineering and execution to raise the standards of our living. But what makes thousands of engineers switch to data science every year? Fraught with challenges, the transition from engineering to data science is not a cakewalk. Why is it still worth the effort?

Data science is the hottest job of the century. According to the Economic Times, the Indian Data Science market is expected to have 11.5 million job openings by 2026. Most organizations across industries such as IT, automobile, manufacturing, retail, finance, and pharmaceuticals are making the most of data science to beat the competition in their respective markets. In essence, the data industry has turned into a space where emerging tech professionals can shine the most.

Engineers Leverage Data Already

Irrespective of the kind of engineering one is involved with, all engineers tackle data on a daily basis. Technically, most of the decisions they make are influenced by the same. But not all engineers are skilled enough to wrangle large amounts of raw data for making critical decisions. This is where data science becomes a valuable addition to their skill sets.

Civil engineers, for instance, can use data mining, sensing, and analysis to evaluate the conditions of the infrastructure both above and below the ground. They can maintain water systems and electricity grids better with machine learning and data mining skills.

Chemical engineers are also gravitating towards the usage of data science. All chemical engineers working in a modern-day plant is exposed to a tremendous amount of complex data. This has made data storgae, data visualisation, and data analysis some important in-demand skills in the industry.

The Data Industry’s Hot Pursuit of Engineers

Data science is a blend of mathematics, statistics, machine learning, programming, and good data intuition. Most engineers are already familiar with the mathematical and statistical basics of data science. Some of them are also well-versed in programming.

The present data market is well aware of how engineers can effectively transition into data science. It is on a hot pursuit of engineers due to the following reasons:

  • Engineers can write simple, readable, and maintainable code.
  • Several IT engineers and software engineers are experienced in data cleaning and detection of inconsistencies within data sets.
  • They have a foundational understanding of various data systems.
  • Engineers can leverage their engineering knowledge to maintain the quality of data.
  • They can enhance both productivity and algorithm code quality.

The Transition into Data Science

If you are looking to leave your career in engineering behind and fully transition into a data science professional, here are 3 crucial tips to follow:

  • Join Data Science Bootcamps

If you are keen on perfecting your data manipulation skills through practise, bootcamps are your best bet. These training programs are industry-aligned and prioritize hands-on learning over theoretical knowledge. They make one of the quickest ways to acquire in-demand skills in the data science industry.

  • Understand the Business Domain

Core data science skills alone will not help you overcome the challenges of a data science job. It is important to understand the nature of the business that interests you. Your business acumen will directly influence the decisions you make out of your data.

  • Focus on Professional Networking

Connecting with industry experts and staying in touch with data science professionals is of paramount importance. Not only can you learn from their experience, but they can also turn into your potential employers or referrals.

  • Gather Adequate Hands-on Experience

Data manipulation in a fast-paced data-driven environment is not a cakewalk. To be prepared, take on personal projects and participate in hackathons. Bootcamps also help you engage in portfolio-worthy projects before you break into the field.

Data is rightly called the new oil. But the art of making sense out of data and turning it into business intelligence is no longer endemic to the data industry. With data science skills in their arsenal, not only do engineers learn to make sense of data, but they can also eliminate the wastage of all that valuable business intelligence.

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