For a difficult technical subject like data science any student will need real interest at the foundation level to follow.
Data science is the intersection of computer science (computer engineering, statistics, efficient management (e.g. marketing, finance, supply chain, etc.) and business domains (e.g. BFSI, FMCG, DeepTech, etc.). Skills are not practical. Before that, it is advisable to have a plan based on one’s own merits and similarities.
Data science as a functional field is governed by qualified professionals in computer science and / or statistics-mathematics. The core of data science is mathematics primarily, and therefore without a flair or interest in mathematics, one cannot expect to do well in this field. It is noteworthy that the basis of computer science and statistics is also mathematics.
It has been experienced in India (and elsewhere) that due to stress (often from family / peer groups), students pursue their undergraduate studies in non-STEM (Science Technology Engineering Mathematics) such as Commerce or Business Administration etc. , Since securing a position as a graduate student in a decent STEM program requires going through difficult entrance exams after high school, a student may find himself overly anxious and under-prepared. There may be various other reasons for such an early career decision.
Against this background, I will mention two specific cases of transition from a non-STEM background to data science from my own experience:
(a) An undergraduate business student is successfully moving into data science and is finally enrolling in MS Computer Science Engineering, one of the top 20 universities in New York, USA.
(b) An undergraduate in hotel management is one of our undergraduate courses in data science and has made it one of the top 3 analytics companies in India and is currently being hired as a software engineer for a Boston-based fintech company.
In both cases, students wanted to get a job right after graduation and were advised by their parents / colleagues / counselors to follow the above programs, where their interest was in computer science. Although data science prepares students for entry-level positions in the IT / Analytics department of a company that relies heavily on statistics at both the computer science and academic levels (for both UG and PG), it still accommodates such cases as entry requirements are not yet available. So well defined for an engineering master. In both cases, the students had skills in coding, math and so when they had the opportunity to prove themselves, they improved.
Therefore, yes, it is possible for a non-STEM student to pursue data science at the undergraduate or graduate level, they have a real interest in this subject.
There are several companies, private entrepreneurial education initiatives that often advise students that one does not have to code, do not have to go through complex algebra and calculus, do not need to know the basics of the database (relational or columns): these are not true. There is no serious data scientist in the world who does not know coding and statistics: the two basic competencies of data science. If one looks back 15-20 years in India and elsewhere, several universities started offering undergraduate courses in computer science, because as a newcomer with a CS degree, one can easily aspire to get a position. In the case of India, where family resources are expanded to provide college education for one member, getting a job after the program becomes a priority. Thousands of students graduated with computer science degrees, but with one basic error – most could not write a decent line of code. As a result, outside of ITS companies being mass-recruited, they can rarely find jobs in companies where they probably aspired to work.
A difficult technical subject like data science would require a genuine interest in computer science at a basic level and there is no escape from this harsh reality. It doesn’t matter if the student has undergone a non-STEM subject or not, but since the demand-supply space is closing in the next few years, one has to seriously consider one’s own mojo before signing up. With a course that could possibly give a lift to a career; But without a real interest in the field, no one will be able to keep the job for long and probably go to relevant fields like marketing analytics, MIS reporting etc. where you don’t need to know the list and tipples To crunch data. This, unfortunately, is not data science.