Be Resourceful — One Of The Most Important Skills To Succeed In Data Science

However, something is missing — YES, soft skills — and resourcefulness is one of the pieces to be successful as a data scientist after studying, understanding and talking with various data scientists.In this post, you’ll see why being resourceful is extremely crucial to learn and pursue data science path and I’ll also share my experiences to hopefully show how you can use this forgotten skill in your pursuit of success in data science, and most importantly, in life.So let’s get started!1..Being Resourceful is a MindsetResourcefulness is a mindset..Period.This is especially relevant when the goals — or the problems — you have set are difficult to achieve or you cannot envision a clear path to get to where you desire to go.And this is perfectly fine..Many of us (including me) very often don’t have a clear path or approach to solve our problems to achieve our goals.It’s okay to have uncertainties that lie ahead..But it’s not okay to stay stagnant, not going anywhere but to wait for some miracles to happen..Because chances are, they won’t happen..Sad to tell you the truth but reality is always harsh.????Resourcefulness is to be proactiveParticularly for aspiring data scientists to pursue a career path in data science, there are tons of resources out there waiting for being discovered..Yet, most of them tend to be a passive observer rather than being proactive to find and create their own resources.When we talk about resources here, we mean books, online courses, open source projects (Kaggle etc.), hackathons and competitions, and most importantly, networks.With a resourceful mindset we’re driven to find a way..There is no waiting..We’ll always try to find a way, always.When I first started out to learn and pursue my career in data science, I was like other aspiring data scientists baffled by tons of resources out there, each of which claimed to be the best among all..Overwhelmed..I read as many book and articles as I could, asked for advice from as many data scientists as possible, took as many online courses (shitty and good) as I could, and made tons of mistakes as far as I could remember and learn from them.The learning path was uncertain, but I was proactive.. More details

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