Beyond Model Building: Artificial Intelligence and Interwoven Learning at Krea
By Dr Shyam Kumar Sudhakar, Associate Professor, Biological Sciences & Discipline Coordinator for Data Science, SIAS
The advent of Generative Artificial Intelligence (GenAI) is projected to significantly impact workplace skills and the educational landscape. With GenAI expected to automate numerous job functions, there has been a growing emphasis on critical thinking, technological proficiency, artificial intelligence, and big data skills, along with social and collaborative skills. The Data Science programme at SIAS, Krea University, is well-positioned to meet these emerging demands and equip students with the skills and competencies required to thrive in their careers. The article highlights some of the salient features of the programme and explains how it is uniquely placed to handle the current technological landscape.
The Data Science Programme at Krea
The Data Science programme at Krea University is a unique blend of Mathematics, Statistics, and Computer Science with domain-specific knowledge. The curriculum emphasises problem-solving, equipping students to solve vital problems in multiple domains by combining theoretical knowledge with critical thinking and cutting-edge technology. The coursework starts with foundational programming and then progresses to gaining insights into crucial statistical and mathematical concepts for Data Science. Furthermore, students learn to apply theoretical concepts through technology and advanced modelling for problem-solving in the Sciences and the Humanities. The coursework includes specialised courses like machine learning, deep learning, optimisation for Data Science, and GenAI. Finally, it also aims to impart domain-specific knowledge through interdisciplinary electives such as computational biology, cheminformatics, climate modelling, and the history of digitisation.
Interwoven Learning and Artificial Intelligence
Interwoven Learning is one of the core learning signatures at Krea. Students don’t learn concepts in isolation but through the prism of multiple disciplines, enabling them to connect across fields and approach real-world problems holistically. At Krea, AI is often taught in connection with social sciences and ethics, allowing students to think critically about the curriculum’s concepts. For instance, students learn to identify biases in datasets, understand how these biases affect model outcomes, mitigate them, and incorporate fairness in artificial intelligence models. The coursework gives students a nuanced understanding of AI systems and their complex interplay with technology, society, and institutions. The programme strives to produce students who are both technologically competent and well-rounded in ethical and social contexts, leading to the responsible use of AI technologies.
Today, interwoven skills have gained increased importance because the questions the world seeks to answer often lie at the intersection of multiple disciplines. Modern societal problems, such as poverty, public health issues, or economic challenges, require skills and expertise across domains, including economics, social sciences, public policy, and AI. Furthermore, students trained with the Interwoven Learning model to approach the field from numerous angles. For instance, a solution to a particular research question often involves a complex interplay of skills from multiple domains, advanced computational skills, and AI expertise. Lastly, students with interdisciplinary training, especially those who can connect traditional disciplines with AI, are often more desirable in the job market. As more young people learn to apply their multidisciplinary skills, many of the world’s pressing problems can be tackled through the combination of interdisciplinary science and AI.
Hands-on Training and Domain Knowledge
The programme strives to impart hands-on training through intensive programming exercises in the classroom and cutting-edge capstone research projects. Students are exposed to diverse tools and technologies that are relevant in the current technological arena. To name a few, Python-based libraries such as PyTorch, Scikit-learn, TensorFlow, database systems, and statistical tools and packages are introduced through the coursework and independent study modules.
In addition to hands-on learning and technological proficiency, significant domain-specific knowledge is required to solve problems using AI. Successful model building needs a well-formulated problem statement, effective feature engineering, meaningful interpretation of results, and navigation of the ethical landscape—all of which require considerable knowledge in the domain where the problem is situated. The programme imparts domain knowledge through multidisciplinary electives in fields such as Biological Sciences, Environmental Studies, and Economics. In the final year, students synthesise their theoretical knowledge and practical skills by working on a cutting-edge capstone research project in their chosen domain. Finally, students pursuing double majors in Data Science and other disciplines are well placed to complete research projects aimed at creating social impact through the application of AI.
At the end of the programme, students emerge as well-rounded data scientists equipped with superior technological skills, blended with critical thinking, domain-specific expertise, and a solid grasp of theoretical foundations.
For more information about the Data Science discipline, visit here: https://krea.edu.in/sias/data-science-at-krea/
