How to be a bad data scientist!

So, you want to be a data scientist, or better you think you are now a data scientist and you are ready for your first job… Well make sure you are not one of the stereotypes of “wanna be data scientists” I list below, otherwise you may well go through numerous rejection in interviews. I do not claim it is a complete list of all the stereotypes out there. In fact, if you can think of other stereotypes, please share them in the comments! This is only a few stereotypes of peoples I have met or seen with time, and who sadly seems to repeat over and over again.

I want to be a data scientist [because of the money] where do I start?

This type of person has heard that there is good money to be made in data science and want its share of it… Little this type of person knows that a lot of hard work is involved in learning the knowledge and skills required to perform the job. Little also this type of persons know that data science is a constant work of research. Seldom is a clear path to the solution is in front of you. This is even truer with deep learning where new techniques and ideas pops every day and where you will have to come up with new ideas. If you need to post on a social media the question “where do I start?”, you don’t have what it takes to be one. Get a learn it all attitude, build an innovative spirit and then come back later.

I can do data science, please give me the “clean” data.

If you just came from (god forbid) a single data science course, or hopefully a few ones. And if you performed one or a few Kaggle like competition, you might be under the impression that data comes to you all cleaned up (or mostly ready) and with a couple of statements or commands it will all be well and ready for machine learning. The thing is that those courses and competitions prepare the data for you, so that you can go to the core of the problem faster and learn the subject matter of machine learning. In real life, data comes wild. It comes untamed and you must prepare it yourself. You might have to collect it yourself. A good part of most data scientists job is to play with the data, prepare it, clean it, etc. If you have not done this, figure out a problem of your own and solve it end-to-end and then come back later.

I don’t know any math or I’m bad at it, but people says I can do data science.

No, it is a fallacy. If you don’t have a mathematical mind, one day or the next you will end up in a situation where you just cannot progress anymore. The good thing is that you can learn mathematics. First, get out of the syndrome of: “this is too hard”. Anyway, data science is harder, so better start with something simple as mathematics. Learn some calculus, some statistics, learn to speak and think mathematics and then come back later.

Just give me a “well” defined problem.

Some people just want their little box with well defined interfaces, what comes in, what is expected to go out. Again, a syndrome of someone who just did some well canned coursed in the field… In reality, not only data is messy, but the problem you have to solve are messy, ill defined, muddy, … you have to figure it out. Sometimes you can define and refine it by yourself, sometimes you have to accept the messiness and play around with it. If you cannot be given vague and approximate objectives and refine them through thinking, research and discussions with the stakeholders until you come up with a solution, don’t expect be a data scientist. A big misconception here is that if you have a PhD you are immune to that problem… well not so fast, I have seen PhD struggling with this as much as any others. So, grow a spine, accept the challenge and then come back later.

I’ve learned data science, I have a blog/portfolio/… I can do anything.

Not so fast. This kind of person learned data science and being more marketing oriented and knowing it can help to build a personal brand built his portfolio or wrote blog, articles, etc. but never went to the point of trying it himself in real life. That person thinks he know it all and that he can solve anything. That type of person is probable singlehandedly responsible for the over-hype of what data science and machine learning can achieve and is more of a problem to the profession than of any help. Do some real work, grow some honesty and then come back later.

If you want to be a data scientist, it all boils down to a simple recipe. Learn hard and work hard. You must follow your path and put passion in it. Search to grow knowledge along your interests, learn about it, try things. Continuously learn new things, and not only on connected subjects. Do not limit yourself to courses, find real world examples to practice on, stay honest about what you can do, about what you know and do not know. Be a good human!


Cover image by tookapic at Pixabay.

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