Source: Deep Learning on Medium
This blog will feature different types of posts and I will try to distinguish between the types at the beginning of the title (e.g. “JOURNAL |”) so, as you might have guessed, this is the first entry of my Journal. The reason I decided to write on this blog is that I’m going through a really big shift in my career, specifically, after officially being a Digital Marketing Specialist for the last five years and learning to program behind the scenes for three years, I finally decided to become a Data Scientist. Now, the problem is, I’m a strong opponent (an enemy, a villain, an antagonist) of formal education. I have never earned a degree, dropped out of my first (and last) college where I’ve been learning Law. The bottom line of my opinion about formal education is: not for me, not for anyone who really wants to learn, just an overpriced and overrated thing that humanity simply got used to. I have already read a substantial number of articles on having/pursuing a career in Data Science and M.S. and Ph.D. always pop out of everywhere. In this blog, I will be documenting what I do and how I do it to become a Data Scientist with nothing more than a GED (not including MOOCs and other non-academic stuff) and let’s see how things work out. Hopefully, in the meantime, some of you will notice this mess and give some meaningful pieces of advice.
I became a digital marketer (of some sort) in 2013, never earned a single certificate or anything, just applied for jobs, talked them into hiring me, finally got somewhere. I started learning Python programming in 2015 via SoloLearn. It was fun. I have always been a victim of a well-known stereotype that programming is too hard and only for chosen ones. This platform showed me it isn’t and my many thanks to it for that. In a year or so, I became quite proficient in the basic scope of Python. Meaning, I could deal with built-in features very well, automating tasks, doing some Django/Flask web development, etc. My biggest discovery was, if you learn from tutorials, you will always have to learn from tutorials, so I learned how to read the documentation and that will be my primary advice for anyone who is looking to learn: Master reading the documentation (which is hard as hell, but worth it) and you will learn anything in this field.
In 2016 I started learning Data Science, Machine Learning and Deep Learning with books and scientific articles I downloaded for free (yes, sorry, per capita income in my country is 6 times lower than in the USA and intellectual property means nothing to me) and this has been going on for more than two years now. Right now I am a digital marketer in a gambling company, which is, with a little of my help, one of the biggest today in Georgia (this one, not that one, damn, I hate doing this) and I’m becoming a Data Scientist for two reasons: it pays well, I think I’m smart enough to handle it. And I’m going to do it in two ways: earn some MOOC’s certificates, build an interesting portfolio. And, also, I’m considering two types of job options: being a freelancer, being a remote contractor (difficulty working in the office, waking up early, etc.).
I am already taking courses in Datacamp.com and Dataquest.io (since December 3, this year) in which I’m looking to complete all available career tracks and paths, which include Data Engineer, Data Scientist, Data Analyst, Programmer, etc. all in different languages and technologies. I’m still reading the books, doing some math on the Brilliant app for Android, learning statistics, probability, calculus, linear algebra, all the math I missed out on for years and there’s nothing left other than to wish myself some luck.
Whatever happens, I will leave a story behind. It will be either about what to do, or what not to do, both of which will be helpful to others.