The data science path. How would be more correct?

Question

IT education

Good afternoon!
Briefly about my background: I
have experience writing a front, experience as an IT recruiter.
I studied engineering at a technical university, but a year later I realized that this specialty itself was not for me.
Objective assessment of shortcomings:
I know and understand mathematics well, BUT sometimes I noticed inattention in my calculations. Those. the main problem is seasonal inattention.
At the moment, after talking with a number of Data Science specialists, I became interested in the direction. I want to try to develop in it. From which I had questions:
1) Will the lack of specialized education interfere?
2) Will the features of my impulsive thinking sometimes interfere?
3) Where do you actually start? Are there any recommendations for books/courses. English at a good level, but it will be difficult to perceive English-technical speech.
Many thanks in advance for your replies!

Answer 1

1. "experience writing front, experience it recruiter." - what is the relationship in Data Science. From the word "absolutely".
2. "I studied engineering at a technical university, but a year later I realized that this specialty itself was not for me." - i.e. dropped out? By the way, if there is no penchant for engineering specialties, how can you be sure that there is a penchant for "Data Science"? I note that "I would like to become" and "I have a penchant for" are very different things.
3. "I know and understand mathematics well" - where does this confidence come from? From school? Or from the university where you dropped out? What courses of mathematics did you "hear out" after all, what are the successes?
4. "carelessness in calculations.- Generally speaking, DS is not calculations. Rather, the calculation today is performed by well-known implementations of methods in different packages. DS is, first of all, the ability to understand the methods, understand their essence, limitations, features, etc., the ability to map data from the subject area to abstract mathematical methods and then interpret the results in terms of the subject area.
5. "I want to try to develop in it." - if you want - develop. What is the problem? Get the blessing of the community? What for? If a person really wants something, then he takes it and does it. Otherwise, it looks like a search for an excuse for why "I want to, but I won't do it."
6. "Will the lack of profile education interfere?"- interfere. Well ? If you want to develop in this direction, then you still have to get a "profile education". In any way - at the university, at the courses, on your own. But you have to get. And how without it? No offense, but DS is not recruiting. It's much more serious.
7. "Will the features of my impulsive thinking sometimes interfere?" - I know specialists in DS with a very different type of thinking. Once again I have to repeat - do not look for excuses "why not to do this."
8. "English at a good level, but it will be hard to understand English-technical speech"- at the first - and possibly at all subsequent - stages you will not have to speak English, that's for sure. Perceive - well, unless you decide to study through the registration for English-language training courses. Here, as you wish - the Internet is full of materials - courses, books, blogs, forums - and in Russian, but at the university you hardly have to study in English. But reading and understanding written English is still a prerequisite for entering this specialty. Otherwise, your prospects in it will not be more interesting than prospects in recruiting.
9. " Where do I start? Are there any recommendations for books/courses."- this one - in bulk, even on this forum. So much so that you don’t even want to repeat the same thing 254 times. So look, you will find. I hope you know how to use Google. Or at least just go through" Similar questions" below. But taking this opportunity, I want to emphasize once again that the main skill of a Data Science specialist is the ability and skills of independent search, analysis and interpretation of information. Fortunately, its sources are now available to anyone who has access to the Internet.
Good luck.

Answer 2

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Answer 3

How are you doing in Data Science? Now I'm starting to look towards Data Science after an invitation to a vacancy. I started with simple books and introductions to Data Science (like the book "Theoretical minimum for Big Data. Everything you need to know about big data"), now I'm just looking at examples of implementations of various algorithms, etc. things. Fortunately, there are already a lot of books and articles! Topic is hot right now