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When we look at the most innovative countries which are at the forefront of technology, the USA, Canada, the UK, China, Australia, and New Zealand automatically come to mind. Maybe there are other countries like France, Germany, and Switzerland that have a sound curriculum in the new age STEM subjects (Science, Technology, Engineering and Math, a close surrogate for Data Science and AI programs) which are leading edge.
What are these countries doing differently vis-à-vis India in course pedagogy and learning outcomes? Well, for that, one has to look only as far as International Baccalaureate (IB) schools versus our more conventional schools following the Central (ICSE/CBSE) or State Board curriculum- the approach is completely different encouraging thinking based on Method of Enquiry, Projects on diverse topics covering environmental issues like global warming, conservation of water, creating garbage-free sustainable cities to live in, etc.
Also, they offer a fundamental immersion into various “hard to understand” subjects like Math though with continuous evaluation and learning by doing, while the conventional schools remain centers of learning by rote, infrequent updation of the course curriculum, underinvested laboratories and lack of playgrounds.
A pertinent question to ask is how does the same apply to Engineering? Well, almost the same conclusion can be reached for how engineering courses are taught in the country. In several colleges – exams are theory-based, labs are an eye-wash, students, in general, have the inability to code or inculcate hands-on skills or solve real-world problems through low priority projects (low weightage to the final grade awarded hence the low priority status). Some professors are career academics who are insulated from industry and with little focus on updating curriculum every year.
The success of an Engineering degree, especially from the colleges that were set up in the 1950s and 1960s was based on the vision of Prime Minister Nehru who wanted Indian engineering schools to be among the best in the world. He had also enlisted some of the leading higher education institutions of the West to develop them. Help from different countries also meant a diversified engineering and technical education system would result but such collaboration from the US, USSR, UK, and Germany made graduates acceptable into a graduate (technical) program that created the brain drain of the 1970s and 1980s.
From the mid-1990s, engineering degrees of any discipline became the passport into Information Technology firms like Wipro, Infosys, and TCS – they offered a gateway into international projects and the classical H1B Visa into the USA. This transformed towards domestic-based careers with lucrative stock options and dot com era in the late 1990s and early 2000.
This has caused most parents to believe an engineering degree is the only one worth having and thousands of children are nudged towards engineering careers even if their interests lie elsewhere. What that meant was intelligent students who could do science or liberal arts were being almost forced to take up engineering of any stream and that implied commoditization and lack of interest in the curriculum and done only for the engineering “degree” as a sure-shot path to a good job.
The current trend of startups disrupting established corporates with the adoption of innovative technologies and business models have brought to the fore the need for the following skillsets: Broad-minded holistic thinking, deeper understanding and interest in innovative technologies, and insights into future trends.. This brings to the forefront a change in the kind of education with a focus on Data Science / Math skillsets and understanding of AI and ML.
Software engineers “create the products that create the data” while data scientists analyze the data from first principles. Software engineers work on front-end/back-end development, build web and mobile apps, develop operating systems and design software to be used by organizations. Data scientists, on the other hand, focus on building predictive models and developing machine learning capabilities to analyze the data captured by that software.
Data scientists specialize in finding methods for solving business problems that require statistical analysis. They take the data that is created by the organization’s systems and create actionable insights and recommendations for the purposes of optimization in forms like risk mitigation and demand analysis to determine if:
- There is a correlation between customer geography and sales quantity one month and, in the next month, and
- To determine the effect of customer demographics on purchasing propensity by day of week and time of day.
Clearly, these jobs are here to stay while software jobs will remain stagnant or gradually decrease through automation and code generation using AI/ML tools. All this is driving the approach and pedagogy of new universities and Liberal Arts Universities with a broad array of subjects.
Education should be more broad-based with core Level 0 and 1 that can be taken from a bucket of design, business, law, and data science courses. Core and Elective Data Science and AI courses should be taught by a healthy mix of scholarly Academics and Industry Practitioners who are currently immersed in doing leading-edge work themselves in the field of Data Science.
The focus should be on electives with “micro-specialisation” and Capstone Projects which build “Masters’ level” skills for Bachelor-level students. An example is the famous “Build a Robot” project at Stanford University where bachelor’s students get $100 to buy parts and build a working robot using various components during the course.
Additionally, the course work in Data Science and AI should have a mix of theory and hands-on practice. Overall the coursework should be designed to make the student, industry, or research-ready, whichever path they want to take in their professional journey.