Vijaybhoomi University

Future workforce should be data savvy: Venkatesh Sunkad, Dean, INSOFE School of Data Science, Vijaybhoomi University

IT companies today are looking at hiring a workforce that is future-ready. Engineers passing out of universities are being expected to be well-versed in technologies like artificial intelligence, machine learning and data science. Prof Venkatesh Sunkad, Dean, INSOFE School of Data Science, Vijaybhoomi University talks about what students need to do in order to be part of a future-ready workforce.

DQ: Data engineering jobs seem to be on the rise in India. What has led to this development?

Venkatesh Sunkad: The global pandemic has led to a realignment of priorities and outlook for most of the companies globally. One of the major impacts of this pandemic has been on Supply chain management and prediction of demand on various systems. Companies want to make sure that they are ready to meet these challenges and they want to use AI/Machine learning to solve these problems. Additionally, many companies have been impacted on the revenue side and they want to optimize the assets the companies have and machine learning is best suited to solve these complex problems and hence there is an uptick in the demand for data scientists and data Engineers to bring the solutions to scale and deploy them in real-world systems.

According to the recent report by NASSCOM the demand for digital /Data engineering jobs are nearly 800K and is projected to increase to 2.2 to 2.4 million well-paying jobs in 2024. According to me these estimates are on the lower side. The post-pandemic world will accelerate the digitization process of various companies and the demand will be much higher. In addition, if you look at various companies of how they are using AI, for example, one of the largest tech companies worked on a project called “OXYGEN” in which they started with an assumption do teams really need managers then they concluded that it was critical to have leaders for each team for various reasons such team morale, team building and cohesiveness within the team. More and more companies are now using AI not just for operational and strategic areas but also for people and compensation decisions

DQ: Are there enough skilled individuals as far as data engineering jobs are concerned?

Venkatesh Sunkad: More than to say are they enough skilled individuals as far as data engineering jobs are concerned it is very important to understand whether there are enough individuals who have the real-world experience and know how to deal with data engineering tasks

The recent hiring trend is a forward indicator of the future workforce. The latest survey by the leading professional and social connect company “LinkedIn” has determined the top two highest paid jobs are “AI Engineer” and “Data Engineer. A couple of years ago it was a “software programmer”. However that does not mean everybody should be an AI Engineer or a Data Scientist, but more and more decisions by organizations are going to be based on data evidence. So the future workforce should be “DATA SAVVY” meaning that everybody should be comfortable with digital technology and should be able to translate whatever the results the AI models give out into real business tangible actions and to productionize these AI solutions. This requires a special skill set which the industry is looking more and more since this is what the future of AI looks like

DQ: What must students do in order to be eligible for these jobs?

Venkatesh Sunkad: One of the main things the students must do is to enroll in programs both at the Undergraduate level and Masters Level that teaches the students to be Industry Ready. Now let us take Vijaybhoomi University. I am the Dean for “INSOFE School of Data Science” which is one of the centres of excellence in digital and disruptive technologies within Vijaybhoomi University. We have dedicated degree programs in both Bachelor’s (Bachelor of Science in Data Science, BE in AI) and Master’s (Master of Science in Data Science and MTech in AI) levels at Vijaybhoomi University. These programs are in collaboration with the International School of Engineering (INSOFE) and Vijaybhoomi University. The most important aspect of all these programs is that nearly 50% of the time is spent on “hands-on training” with real world problems which according to me at an undergraduate level would be the first in India. This would make the students “industry ready” starting from how to analyse data, build Models and finally productionize the solution.

DQ: Are educational institutions in India at par with universities abroad when education in emerging technologies are concerned?

Venkatesh Sunkad: An excellent question, it depends on the institutions. Nowadays most educational institutions offer some kind of data science or Machine learning courses. However, if you look at India particularly there may be a handful of institutions that can create the environment and teaching that would really cater to the needs of the industry. In this regard, the Government of India is also providing opportunities such as where anybody can participate in the INDIA AI journey.  We need to have more public and Government partnerships to solve the real difficult problems of tomorrow. Additionally, most institutions offer theoretical courses in data science and the need of the hour is for hands-on training with real-life problems facing the industry today.

Additionally another very important aspect of upskilling, students must be trained to understand the fundamentals of the technology and should be comfortable dealing with digital technology which not only involves AI and ML but other disruptive technologies like Block-Chain, IoT, Robotics, etc. This is what in the social world we call these students “digital natives”. Once the students are comfortable with fundamentals then they can adapt to the ever-changing world. Also a liberal professional education is very critical for a successful career in data science because problems are no more one-dimensional but multidimensional which needs 360-degree thinking. One of the examples I always give is the autonomous or self-driving cars. When a kid suddenly comes in front of the car, today if a human is driving they make a decision to avoid the kid which may result in the car hitting another vehicle or a structure. However, if it is a “self-driving car “should the car protect its passengers or the kid in front of it. These are called “moral algorithms” that an AI engineer should design and knowledge of humanities, law and other aspects play a very critical role here.

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