Honestly, I don’t know. It’s probably a decade too early for me to become a Data ‘ . Scientist’ I am writing this blog because people too often ask “How to become data scientist in a month?” Here lies the real problem. Two actually: ‘Research’ isn’t fairly understood term People love shortcuts too much Half of you wouldn’t even have done a single project in Data Science, . yet How do I know? Simple, I was one of you. Damn, I’ve been in lectures without even making an effort to know what that lecture is about! PS : Not the smartest idea. First Issue Let’s clarify the following terms: : A person who uses existing knowledge and/or to build something Engineer tools : A person who extends the knowledge about understanding of something Researcher : A person who has contributed towards the research Scientist significantly Data Scientist is ridiculously thrown around. Ask a physicist what it would take him to be called scientist ( ). partial insanity is just the beginning Data Science, currently, is majorly a research oriented field, yes. Doesn’t mean anyone working in it becomes a researcher, let alone a scientist. Second Issue You see, . You can just be there are no shortcuts for gaining knowledge smart about the learning techniques. No tutorials can jump-start your career from novice to pro ( if there is then damn! I wasted life ) :/ You become a pro by mastering: Execution Dedication Persistence Claims like “Master Neural Networks in One Minute!” will give you an abstract idea about the working of it. So you’ll be lost when you are tackling a real life not-so-mainstream problem as default settings won’t work. Like me, when I used to read the abstract of a research paper and assume I’ve understood all the concepts. PS : Yeah, the old me wasn’t that bright. So how to go about it? Irrespective how experienced you are, these will apply to you: Ask the stupidest questions Just because nobody thought of it, doesn’t mean it is wrong Leave the guessing work to Learner, you code and evaluate Keep your mind open to All Fields (Bio, Info Theory, Physics etc.) Visualise Everything! It’s the map to the Treasure Chest Never be scared to improvise What if you already are a pro? Well if you already have experience in this field then you very well know how a single idea can change the whole landscape. This just highlights the fact that Data Science has more potential than one could fathom. Take example of Reinforcement . Most of the people I know think of RL as a game-play learning. They barely try to scratch their heads to apply this in text or vision paradigms. Learning RL is Goal-Oriented learning. Something like a customer support chatbot is perfect playground for RL is field of text. Where the goal could be anything from answering their queries to persuading them to buy a policy. So where does this leave us? The harder you work the sweeter the fruit and longer you keep licking your fingers (you creep!) This also increases one’s chances to ‘beat the odds’ or ‘defy the norms' (with motivational tunes in background). As Freddie Mercury once said “do what you want to do …. just don’t make it boring”. And All the Best! :D
