Data Science : Where Do I Start?

After completing 7 + years as a database administrator on DB2 for z/OS & IMS ( data was never a problem ) and also an executive MBA program from Great Lakes Institute Of Management ( Chennai ) in July 2017 on Business Analytics & Business Intelligence ( which is when data seemed to be a problem ), the question still remained unanswered :

” Where do I start ? “

There is always head load of motivation from my peers ( established data scientists ) to get started and spend more time learning data science, but trust me its very boring to begin with. Why ? Simply because most of us hate statistics and mathematics ( pretty much sums up the core of analytics ). Do you have a choice ? The answer is simply a big NO ( deep technical stream ). But wait, all it takes is one tiny channel which doesn’t scare you. Something that you liked ( be it theoretical ), A book that can kick start your career.

“It’s very important to find influential factors
when you decide to switch technologies”

The executive data science program ( which took about 1 year to complete ) showed us the platform and gave us a launch pad. Including me and most of us out there are fueling up their stations and awaiting a launch date, or worse – spending time trying to find their launch vehicle or even worse – have left the hope of becoming a data science engineer or the fancy term – data scientist. If you have left the hopes : read further, if not – you are welcome to read further anyway.

”  Backward Learning – Start with machine learning / Artificial Intelligence ” 

It may sound weird, but this method actually helped me gain access to the learning channel I was looking for and being stuck to data science topics. At first, I tried going to probabilities, regressions, clusters ( I consider this forward learning ) – they didn’t seem to click, the struggle to kick start seemed immense while reading these dry topics. So try going backwards, the ideal way would be to start off with something fancy ( not too fancy ). Start with ML for example – you will get to know what is supervised and unsupervised learning. Drill down on supervised learning and you will end up with regression eventually. You will feel happy because you would have inadvertently covered if not massive but a decent amount of ground trying to understand what ML feels like.

Having said that, I am not sure if I am fueling up or simply waiting in the launch vehicle, but sure as hell haven’t lost hope. Having said that, there is only one way to find out – by blogging the journey towards becoming a DS and sharing your experiences as a novice data scientist. By the time you are reading this there would probably be millions of blogs, sites and courses out there that are helping people become a data scientist, amidst all of them if you happen to land here – then like me, you had a problem starting your career as a data scientist.

Fascinating career awaits for those who dared to unlearn and step up to the ever growing need of data engineers, the landscape here is rapidly changing at a pace you can’t imagine. If you are reading this, then you are not alone and you still have time. Start now and don’t get left behind in a corner of thoughts of becoming a data scientist, when you can be one instead. If you are with me on this – hit the subscribe button or follow my blog, who knows – we may suggest each other something extraordinary 🙂

If you feel this topic helped you gain some confidence, leave your thoughts on the comment section below and I will make it a point to read every single one of them and respond.

Good Luck !