How Commerce and Humanities Grads Can Conquer AI

It is possible to master not only AI and data science but also programming and application development concepts that bring considerable value to a data science degree from practically any background

Preeti Anand
New Update
Synergizing AI and human expertise


Can a commerce, arts, and science student understand data science, machine learning, and artificial intelligence? Do they have the necessary background knowledge? Can a data science program be created that meets their needs and helps them excel?


The answer is an emphatic yes. It is possible to master not only data science but also programming and application development concepts that bring considerable value to a data science degree from practically any background, provided one is willing to put in the time and effort. All three are enrolled in the BS (Data Science and Applications) curriculum at the Indian Institute of Technology Madras.

Data science and artificial intelligence

Data science and artificial intelligence are built on fundamental mathematics, statistics, and computer concepts. Calculus, probability, linear algebra, discrete mathematics, statistics, estimation, inference, computational thinking, and Python programming are among the foundational courses that must be taught.


Mathematics and computer science

Mathematics and computer science are taught in the 11th and 12th grades curriculum. Others can begin learning these topics at an introductory level even after they have completed their formal schooling. Within the program's first year, these topics can be introduced from elementary concepts and gradually developed to the college level using a well-constructed foundational course set.

For example, the foundation level of IITM's BS (data science and applications) degree covers these topics in eight subjects, requiring only a Class 10 mathematics background. Currently, 22,000+ students in this BS program are learning these foundations primarily through these foundational courses, with more than 31% coming from non-engineering backgrounds.


After mastering the foundations, students can move on to learn the essential abilities required of an application programmer and a data scientist. This necessitates additional knowledge in linear algebra, probabilistic modelling, and optimisation on the data science side.

Application development 

Application development requires knowledge of data structures and algorithms, databases, and sophisticated programming principles. With this knowledge, students can begin constructing applications by taking application development courses using a standard Python framework (for example).


Students can learn more about machine learning (ML) techniques and practice creating models for complicated data sets and corporate data to aid decision-making. At this time, students would be prepared to work on hands-on projects, as they would at the end of the diploma level of IITM's BS (Data Science and Applications) program. After completing diploma-level courses, students can find internships and careers in data science and artificial intelligence businesses.

After gaining hands-on techniques, students will be prepared to tackle advanced AI topics such as deep learning, natural language processing, and big data analytics. The knowledge learned in the fundamental and skills-oriented courses should allow students to handle these advanced topics.

These disciplines can prepare students for higher education while also making them industry-ready. Because data science and AI have several uses, obtaining domain knowledge that can help you use AI tools successfully to solve domain-specific challenges is also beneficial. Shruti, pursuing a BS in Data Science and Applications at IITM and a pharmacy degree, has the necessary understanding to solve complex healthcare problems with AI technologies.


Educational institutions can offer application development, data science, and AI programs open to practically everyone outside of school. According to NIRF, IIT Madras, the country's top-ranked university, has paved the way and demonstrated how such a curriculum may be delivered cheaply through hybrid mode. As all institutions offer more such programs, the landscape of data science and AI learning will become genuinely democratic, with no entry restrictions based on background.