Is it still possible today to become a self-taught data scientist?

If you are an engineer working for a company like Boeing, have processed and leveraged data extensively over years of professional experience, used data science tools and programming languages, and have success stories, you are de facto a data scientist even if you think you are not, in this case an industrial data scientist, as opposed for instance, to a marketing data scientist. And you are hireable as a data scientist.

If you have a PhD in history, has little experience processing serious data and getting valuable insights out of it, and all you do is attending a one-week data camp, you are not going to be hired as a data scientist. You can self-teach many things, for instance I learned Perl, R, and SQL by myself, to the point of being able to use it in a corporate environment. But this is because during my years in college and military service, I learned other programming languages (C, C++) and how to design databases from scratch (it was one project for a class that I attended.) So the learning curve was not steep, and paying $20k to learn new programming languages would have been a waste of money. But I believe the main challenge is getting the experience to process large dirty data sets that have a structure changing over time, work with engineers, understand where the value is, develop/test prototypes and better algorithms, design black-box implementations (that work in production mode with some degree of reliability and added value) which you only learn through extensive experience in the corporate world, by interacting with high-level sales, marketing, engineering, IT and/or other executives or teams, and are able to “talk” and “understand” their “language.” Also, one drawback or being self-taught is that you will have “holes” in your knowledge, that you are not aware of, but they will show up one day, usually at the worst time. You may also not know what you should learn on your own, versus stuff that you can skip (though you could argue that you can ask in a data science forum what is important to learn, and some answers can be found online, for instance here.)

As an illustration, and in a different context, I learned number theory all by myself to the point of becoming an expert. Yet I started with a strong math curriculum in my college years, published my first paper in Journal of Number Theory (all my subsequent papers were about data science.) But my interest is peculiar: you could call me an amateur number theorist, or independent, self-funded researcher. My goal is not to publish in scientific journals, not even trying to be recognized by other number theorists, but to make state-of-the-art, new discoveries (my own) accessible to a large audience. There is no way I would ever be hired as a number theorist in Academia, maybe not even for an organisation such as NSA. I love what I do, but I do it with no desire of getting a paid position out of it (I could win some awards paid in dollars, but that is another story.) In short, self-teaching data science, even to an advanced level, may lead you to the same situation that I am in with number theory: a hobby, a passion that occupies much of my time, but nothing more than that. In itself, this is actually worth a lot more than landing a job, as many employees — including data scientists — seem somewhat depressed, misunderstood, or overworked nowadays. It depends what your goal is.

Source for picture: click here

To learn real data science, the best way is on the job though. Many curricula are still outdated today, so you could learn more than a college student, all by yourself, but employers won't hire you as a data scientist unless you have the right experience and a related degree, not necessarily data science, but operations research, statistics, data analysis, software engineering, BI or MBA with a strong analytics background.

For related articles from the same author, click here or visit www.VincentGranville.com. Follow me on on LinkedIn.

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Comment by Harshendu Desai on July 13, 2018 at 9:00am

The problem  with  industries is too much  fishing  in  interview process which  restrict the candidate to do  the actual  job.  Everyone wants everything.  If you look at job  requirement ,  most of the requirement bullets never require while doing  actual  job.  This creates the fluffs of resume to  show off the right words which  match the 1st  cut  of interview. And then , if you are lucky , you  got hired through  communication skill set. Bottom  line no one wants to  train  the resources like training  the machine learning / statistical  models.  

Comment by Kamil Mazurek on July 4, 2018 at 1:14am

How would you define data science exactly since there are many resources that have a different definition?

What if you're a software engineer studying math through textbooks that are used in universities, ask people for help once you're stuck (to find potential gaps in your knowledge?), maybe compete and end up to be in the 1-5%, develop tools that save/make money for businesses (such as SaaS), develop your own tools for betting/trading in order to make money for you. 

What must a data science have done in the past to be considered one? Process big data (assuming you're dealing with many million data entries) and make a valuable prediction/analysis. What if you've done only the latter? 

Comment by Vincent Granville on July 3, 2018 at 5:58pm

Hi Robert, I could not agree more with you. Very well said!

Comment by Robert de Graaf on July 3, 2018 at 3:01pm

As someone who occasionally hires data scientists, I note that as Data Science degrees become more common, it will become progressively more difficult to get hired without one, simply because as hiring managers get to know the curricula of particular programs, and become familiar with the quality of people from those programs, they are going to be more likely to hire from those programs simply because they know what they are getting (assuming the quality isn't atrocious). Which is not to say it won't be possible to be hired without such a degree, but it will take more effort as time goes on. A possible conclusion from this observation is that not doing a degree may not necessarily save you time and money.

Robert de Graaf

The Lazy Data Scientist

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