Claimed as the "sexiest job of the 21st Century" here I'll discuss the reasons for my proclamation as a Data Scientist, beyond the hype.
Data Science, you know that thing that everyone is talking about nowadays, and that almost everyone wants to do right now. It's not particularly easy to define Data Science as a whole, or subject, I've done it in other articles:
But here I want to talk about why I proclaimed my self "Data Scientist".
As you may know, maybe you follow me here or LinkedIn or somewhere else, I call myself a Data Scientist. But Why? How did I become a Data Scientist? Why after studying Physics and Computer Engineering now I'm suddenly a part of this field?
These are questions that many of you may be asking about other people or yourself. Why are you a Data Scientist?
The thing here is that I did not study this in a "formal school", so why should I have the right to call myself a Data Scientist?
If you think about it, we are used to following the path that if you graduate from a specific field like Mathematics, then you have the right to call yourself a Mathematician, but what happens when a new field is developing, and you are a part of that growth, and also there was no specific Bachelor or Graduate degree for becoming a part of this field?
Well, then you have to proclaim yourself part of the field. And this is what I did.
This may sound weird for a lot of people, but a self-proclamation is not that crazy. I'm going to give you some examples before explaining my story.
Physics in the 1600Ã?Â¢??1800
I'm not going to bore you with a thousand words on Physics or the history of Science, I'm just going to tell you a little story. One or two paragraphs, I promise.
After the important distinction between Knowledge and Truth by Descartes, we can mark the beginning of science as we know it. But the scientists in the old era did not call themselves "scientists" back them. If you see the Principia by Newton:
He mentions that he talks about Natural Philosophy. Not Physics. Actually, that was the name of Physics back them. And while the concept of science was a thing in Newton's time, he considers himself more a philosopher than a scientist. The philosopher of nature.
So, Newton, one of the most important physicists of history, did not study Physics in a formal school, he actually studied math, philosophy and more. But we call him physicists too. I don't believe he proclaimed himself a physicist, we did that for him, but in the end is what we think he is.
One of the things I don't talk a lot about is my passion for Lacanian Psychoanalysis. I'm not going to give you a full picture of what it is right now, just an example related to the point I'm trying to make.
Lacan in his Proposition of 9 October 1967 on the Psychoanalyst of the School and in some of his essays, liberated radically the analytical practice of the corset of the institutional authorization and it was the execution of the death to the "hierarchy" of the didactic and to the "guarantee" of the genealogy of the divans.
The amplitude of its effects became evident when the same "Lacanian" came to defend themselves from their own institutional orthodoxies, considering it necessary to restrict this approach in this way.
In this Auters Ecrits he said:
"Ce a quoi il a a veiller, c'est qu'Ã?? s'autoriser de lui-mÃ??Ã?Âªme il n'y ait que de l'analyste"
"Only the analyst, that is, not anybody, is authorized only by himself"
This may be a little radical, but he is talking about that the only way to authorize someone to be an analyst is that the authorization comes from himself. This has a lot of implications, but the main point here is that if you are able to follow a pah and acquire enough knowledge you can proclaim yourself an analyst.
Back to Data Science
So as you can see it's not so insane to proclaim yourself a part of a study field. But we should be careful.
I don't mean that if you read a medicine book you can just proclaim yourself a Medical Doctor, or if you know how to calculate the derivative of a function you can call yourself a Mathematician.
We need to possess some knowledge and capabilities to be able to proclaim ourselves something. Normally we do this by taking a 4Ã?Â¢??5 years education of a field, with tests and a systematic knowledge path, but they're ways to be part of some fields without going to a college.
And that's what I did. I studied, a lot, about data science (actually I did not know what was Data Science when I started learning all these things that took me to the field), machine learning, deep learning, programming, and more. From courses, self-study, projects and then I learned a lot from my data-related jobs.
After getting all that self-education and knowledge then I proclaimed my self a Data Scientist. But why? Why not say I'm doing data stuff?
I think if we want to make this a serious field we need to have some standards, and a way to call ourselves. After defining data science in other articles, we should define "Data Scientist", and I'll do that right now:
A Data Scientist is a person (or system?) in charge of analyzing business/organizations problems and give a structured solution starting by converting this problem into a valid and complete question, then using the scientific method, programming and computational tools develop codes that prepare, clean and analyze data to create models and answer the initial question.
If you have any comments on my definition please let me know. I say system there too because we don't know what will happen in the future, maybe we get automated, I don't know.
Also, I'm talking about the scientific method because I think we should think of Data Science as a modern Science. I'm more than happy to discuss this with you, just reach me. You can listen and read more about that here:
Data science is awesome, and if you want to be a part of it you have to work hard and consistently. There's a lot of stuff to learn, I'm learning every day and I think I'll never stop learning.
If you want to call yourself a data scientist, do it with responsibility.
Data Science requires a level of expertise and knowledge on several areas that I've highlighted before.
You'll need to know math, statistics, statistical learning, machine learning, now maybe deep learning too, programming (in several languages), have some insights about the business you're working for, be able to follow rigorous methods for extracting, analyzing and exploring data, create learning algorithms and models, be able to explain your results to different audiences and more.
It sounds hard, but it can be done. Be sure you want that kind of responsibility before entering this area.
What you'll do day by day will have a tremendous impact on your organization, so you better do it well. You have to be prepared to fail and fail again. To have an open mind, to be able to criticize yourself and your ideas.
I'm not going to lie, if you can do this and more, become a Data Scientist, it's a lot of fun.
Thanks for reading this. I hope you found something interesting here :)
The article was originally published here