Not Everyone Can Become A Data Scientist, Says Prof. Charanpreet Singh Of Praxis Business School

Once considered a niche domain, Data Science has now emerged as a well established and essential functional area that helps businesses operate effectively and competitively. The demand for trained data science professionals has increased tremendously. Analytics and data science are a regular part of boardroom discussions across sectors and industries.  Naturally, there is a huge surge in the excitement about getting into the data science field. Professionals from different backgrounds and with varied skill-sets are attempting to upskill themselves through formal training programs in full-time, hybrid and online modes to become ‘data scientists’.  https://www.youtube.com/watch?v=euQgH1qSBEA However, there's a lot of conflicting narrative out there which is causing a good deal of confusion. On the one end, we hear claims that ‘anyone can become a data scientist’ and then there  industry experts and employers who debate vehemently that data science, like any other science, is for a select few.  To get a clarity around these issues, we decided to speak to Prof. Charanpreet Singh from Praxis Business School. Praxis Business School has been the pioneers in Analytics education in India and has the longest history of selecting, teaching and placing students in this domain.

1) Analytics India Magazine: Since inception, Praxis has followed a clear and strict selection process — is that a message that Data Science is not for everyone?

Charanpreet Singh: We have always had a selection process for admitting students in our program. I feel this is the responsible thing to do, because we have a full-time program and people leave existing jobs to pursue it. This kind of makes us feel responsible for their lives. As educators, we have to  be sure that the candidate has, in our opinion, a reasonably good chance of success in the field.  It is a fact that data science is a red hot career option with high demand, salary and respect, so everyone wants a piece of this action. We believe that Data Science is a complex domain and demands an adequate investment of time and effort to strengthen the knowledge and skill levels from which one can chart career growth. And so, for the other question that if anyone can be a data scientist, my answer is NO.  Here is a list of people in our experience who would find it hard to be data scientist: 

• People who aren’t comfortable with numbers, do not enjoy the math of things. People who dreaded higher math and avoided it all through their prior education but now suddenly want to embrace it and absorb it
• People who think coding is for geeks and they are either averse to it or think they are well above it and would now wish to ‘manage’ things rather than ‘do’ things!
• People who do not have a penchant for analyzing things or what is popularly called a ‘problem solving attitude’. If you are not obsessed with solving a problem, irrespective of how tough and frustrating it is, and how long it takes, you may be chasing a wrong career. 
• The ‘quick-fix-guys’ who believe be it the course, or the subsequent work  there is always a way around hard work

2) AIM: Praxis boasts of a large and very successful alumni base in analytics. In your experience, what kind of students actually make it big? What are the necessary traits for success in data science? CS: We have been in this for 8+ years now and our alumni has done exceedingly well - in most cases way ahead of our expectations. Our understanding of what works and what does not has also evolved over the years.  Academic background certainly helps — but it is not the decisive factor. In order to make good use of this program, one has to have a fundamental comfort with numbers and technology — irrespective of the academic and/ or professional background one hails from. Secondly, one has to have the courage, curiosity and commitment to learn new things — sounds trivial but the program is complex and rigorous and demands a sustained effort. One needs to look beyond curriculum-driven learning and participate on platforms like Kaggle, AIM and other global hackathons. All this means a lot of hard work. This commitment to learning needs to stay post the program and across ones career to keep pace with the rapidly evolving domain.  And finally, a trait that is crucial for success is that one must enjoy the whole process of problem solving — all the hours of finding and cleaning data, trying out different techniques and then articulating the solution. So it’s more about attitude and compatibility with the domain than just about a specific educational qualification.  3) AIM: It seems like educational background or a particular type of experience isn’t a decider for you. What message do you have for someone aspiring to enter this domain?

CS: For someone wanting to enter the domain, I would urge them to do a lot of work home before deciding. This can shape up to be the most crucial career decision one has made.  Research the domain, understand what kind of work is done in data science, how does a typical day look like in a data scientist’s life - establish that you would like to experience that life. Look beyond articles that talk about demand, and try and understand why is there such a demand. One would always be successful in a field one is good at — do a lot of retrospection and make sure that you have the capability to learn and to perform in this domain.  Finally, research the quality of the programs you are applying to, and engage with the institute alumni before you decide.  At Praxis we have had a diverse set of students — different educational backgrounds, varying years of experience across multiple sectors and functions; and these students have been able to do well during the program and carve out successful careers in data science.  To illustrate the point, here is a small sample of students from Praxis Business School with a summary of their career progression: