(Part 1 of 3) - Is Data Science for you?

Every now and then I receive emails/ calls from young minds on whether they should take up Data science, in some cases, they are ready to quit their otherwise good jobs and pursue Data science. After talking to a few, I realised many of them are being intensely swayed by the Big data and Data science hype. Given this, I thought it is worth putting down a note on how to evaluate whether Data science is for you and if yes, how to prepare for it?

This is a three part series, starting with some reality check.

But first, here is a full disclosure: I am not a Big data domain expert, but have been involved with analytical consulting and statistical modeling for over a decade. In the recent few years, I have been gaining hands on experience with Big data and the associated analytical domains. 

Part 1: You will not become a data scientist in 3 months

Over the last couple of years, there has been a monstrous rise in the drumbeat surrounding Big data and Data science. Significant column space has been (and still is being) dedicated to how it will transform the world and how we will need millions of data scientists. There is some truth to this, but a large part is opportunism; in some part due to deliberate misdirection and in some part to due to misconception. Become a data scientist in 2 months with self study ! Do 5 courses and master Deep Learning ! A few decades ago it was - become an ace programmer in 30 days. Firstly, let us start by understanding the word 'Scientist'. What does it conjure? Geeky, dishevelled, intelligent folks, toiling away for years in solitude and solving complicated equations, running complex experiments or working with lab rats in their white coats. Stereotypical for sure. But the point is - it takes dedication, passion, hardwork and more importantly time, to get there. That is the nature of the beast. And it is not just about science, this applies to any field. Becoming anything worthwhile in any field is atleast a decade long commitment if not a life time one. Can you become a good swimmer by reading a book and practicing on the weekends for 6 months, never mind being an elite swimmer? Now one may argue that they don't mean an expert when they refer to the term data scientist, but rather someone who can get to work in this field of big data and data science and get the ball rolling. We need a ton of people to do stuff, however green behind the ears they may be; so we are calling them data scientists. The simple truth is - none of us will add any value, certainly not in the long run and definitely not in a sustainable manner with 6 months of learning. So you have to do significant pre work before getting into it, that way you will maximize your learning curve phase. Now keeping the hype aside, does big data and data science have potential in the future? Of course it does and immense! Now should you consider this as a career path? Of course you should, provided it will resonate with you; because, it will take time.

If you are interested in data science, then what are some of the questions you need to ask yourself before moving ahead? This brings me to the section break and these points to ponder will feature in the next part of the series.

In the meanwhile do read this instructive article by Peter Norvig, Director of Research at Google on 'Teach yourself programming in 10 years' : http://norvig.com/21-days.html.

Disclaimer: The views expressed in this post are my own and do not necessarily reflect the views of my employer.