According to AICTE, more than 60 percent of the 8 lakh engineers graduating from technical institutions across the country every year remain unemployed. As if this number isn't sad by itself, only 3 percent of engineers passing out have suitable skills to be employed in software or product market and only 7 percent can handle core engineering tasks. This has been published in a survey conducted by an employability assessment firm, Aspiring Minds.

So, where is the gap? What are the challenges being faced by graduates and the colleges producing these graduates, in making them employable?

The existing training models follow "One-size-fits-all" methodology, while the need of the hour is for personalization- identifying students as unique individuals and customizing training programs based on their skill-gaps and learnability. The prevalent model is syllabus-based rather than gap-based thus the real problem is not addressed. Moreover, the present training set-up is non-scalable-plagued by dearth of quality trainers' en-masse'. Add to this, the widening gap between the fast-paced industry requirements and the skills being imparted to the students. The worst of all is, skill-development training is seen as a short-term last-minute solution where-as it requires long-term systematic solution.

Is it all that discouraging? The answer is No! There are a few visionary Universities and Colleges, which are embracing innovative and systematic training programs to up-skill their students - integrated into their pedagogy - to help retain the essence of traditional-learning with futuristic and impactful learning models- especially the ones driven by Data Science and Technology.

Data science, in layman's term, is an advanced form of machine learning technique that uses certain algorithms and scientific techniques to extract useful information from raw data with an objective to forecast the future.


Needless to say, 'Data science' is the latest buzzword in today's technology space. Data science, in layman's term, is an advanced form of machine learning technique that uses certain algorithms and scientific techniques to extract useful information from raw data with an objective to forecast the future. How is it relevant in Education space and how can it help improve a student's employability?

Well, data science can not only graph the obvious academic data of a student, but can also record the student's cognitive, analytical and behavioral data, dynamically and it can then provide scientific meaning to this raw data in identifying a student's blocks/challenges in his/ her learning curve. It can also help in understanding a student's pace-of-learning; in deciphering a student's moods in learning; in maybe, predicting a student's performance in future. When this understanding is augmented with content modeling- using the right content capsules- a teacher or a trainer (which could also be a machine too), can fix those gaps in the skill-development journey of a student, customized to a student's learnability, instead of providing "One-size-fits-all" solution. Now, add Technology to this, you are suddenly looking at scalable personalized training models, that could change the learning outcomes of students.

Let me walk you through an example, to elaborate on this model:

Let's suppose that a student's Reading Comprehension (RC) skills are suffering in various recruitment or competitive exams. The general assumption in traditional training models is that this student has poor reading habits- which might or might not be true for this student. On the other hand, if we could read into a student's knowledge graph and use that data to decode that, the issue is not with a student's reading ability, but the problem is with a student's understanding of a particular variety of question in mapping the logic asked to what he read and understood, then the training solution for this particular student should be focused on building that logic-gap, rather than on his reading ability (which was our original assumption).

Similarly, for another student data reveals that her failure is more because of the examination pressure than her reading or logic inability, the training solution for her should be focused on building the right behavioral skills. Once, these skill-gaps are identified in a student's learning journey, start filling them with the right content capsules- broken down into small granules (this is what content modeling is). When this data is modeled into defined set of algorithms we are looking at scalable customization- to help students address and improve their skill-gaps!

Wow! Doesn't this sound exciting! In fact, this is what India is trying to achieve as a milestone in the education sector. Though not as widespread as in other countries, India is seeing a rapid growth in the education sector in terms of delivery of personalized education experience and data-driven training solutions. Anyhow, I strongly believe that Data sciences, Technology, and Content Modeling are the future of Education space in India and that they can help bridge the employability gap when put to the right use!

Sumanth Palepu

Completed his Bachelors in Computer Science from Gayatri Vidya Parishad College of Engineering and MBA in Marketing and Strategy from IIM, Kozhikode, Sumanth Palepu previously worked with Sony Entertainment Television, Mahindra Satyam & Computer Science Corporation (CSC). Currently, he holds the position of Chief Marketing Officer and Director at Conduira Online