Tip: Python Intermediate Developer?

Question

Python

Actually, I would like to hear thoughts / advice / ideas about how / where / how to move forward:
1. I started learning to program (from scratch) about 2 (with breaks) years ago. At first I mastered Swift, but at that time it was too tough for me. This year, in March, I sat down specifically for Python and Computer Science in general. I won’t say that I became a programmer, because. I admit that I'm a shitcoder, but galloping around Europe, I went through the entire university program, including mathematics (matan/discrete/linal), algorithms, software operation, basic programming (incl. I do not know how).
2. Now: I have re-read and re-watched all the videos and books on Python.
Made about 10 Jose Portilla courses on Udemy:
1. Python Bootcamp
2. Django (+HTML/CSS/Bootstrap/jQuery/JS - basics)
3. Flask
4. SQL Bootcamp
5. Dash (Python framework)
6. Data Structures + Algorithms
7. Python for Finance
8. Python for Data Science (with elements of NumPy, Pandas, MatplotLib, Seaborn, Scikit-Learn and
examples of Linear / Logical Regression, K Nearest Neighbor, Decision Trees, SVM, NLP,
recommender system, a little bit of Tensor Flow and how to install AWS)
9. Spark
10. course on Tableau
11. Hiryanov's lectures with MIPT
Skimmed through several other major books Dive into Python, Automate Everything, Algorithms, McKinney "Python for Data Analysis" etc for the intermediate (and other topics like patterns, OOP, algorithms, cyphers :)).
Plus fully mastered Learn Python the Hard Way and Learn Programming with Python by Irv Kalb (2 best resources IMHO for beginners)
Made a portfolio on Github with 10 projects:
1. Social Network on Django/HTML/CSS
2. Stock Ticker on Dash.Plotly (see here https://dash.plot.ly/gallery)
2. Bank Account
3. Blackjack
4. Calculator Guess Number
5. MCQ game with IO capabilities
6. Tank Game
7. Find zodiac sign
8. diff. small tasks for Panda's (and others by Henley "Learn Data Science with Python"
9. a couple of simple exercise games for mastering OOP ,
etc.
As a result:
1. The final goal is to get / go to Data Science in the commercial field
(especially now I work as an accountant , i.e. I have domain knowledge, and
even more so (still), I recently went to a lecture by a professor of mathematics, who has now become a Data Scientist, but in 2 hours of the lecture she never said the word business, nor anything practical, but only chattering about how you can work with R).
2. But: a) realistically, I don’t have either a Master’s or even a CS education, which is an essential condition for candidates for these positions b) or even more or less professional programming skills to at least go to interviews there
3) Therefore: I have ideas go to work in the design studio junir'om, cat. are developing software for clients, but more towards the front-end. IMHO, some serious companies will not take me to a programmer position at the moment.
But, on the one hand, if you go into layout and other web, i.e. this is even further from Data Science, although you can learn a lot directly from people, a cat. they do it every day and professionally.
On the other hand, studying alone to study in the evenings is already tired and there is no more strength, because. while studying all this, I myself invented more than one bicycle and now I understand that everything could be mastered both faster and more systematically even if I had taken simple courses, but at a university or some kind of academic level. Plus, working with a mentor is sometimes a problem, a cat. it may take me an hour to solve, it will take a professional 3 minutes to explain.
Those. again, I also want to finish with the main work, because. and I sat on it unrealistically, and the company does not promote me, I reached both the professional and moral ceiling in it, we must move at least much further.
Therefore: I welcome any tips, previous experience, thoughts about how best to move on or thoughts about the above.
Thanks in advance for all comments!

Answer 1

You are strange people... I
started, I advanced, I didn't reach the end yet, I decided to turn in a completely different direction. It’s not a pity to throw away 2 years of your life ...
In essence, there are two pieces of advice:
1) start working as a python developer not in Data Science yet. Get specialized development experience and then, even without a specialized education, it will be easier to move to the right position.
2) Lack of formal education hinders? Get in absentia, remotely.
In general: in American "Do OR die", in our opinion "Die BUT do". If you feel that the area is interesting - finish it to the end. Well, change your job, it kills your it and self-confidence, and this prevents you from moving on.

Answer 2

I'm in exactly the same situation, exactly the same. If you spent this time learning Java, you would already be working as a Java developer, earning money, there would be prospects and growth. Feel sorry for the wasted time? I partially yes.
Top universities (Moscow State University, Higher School of Economics, Moscow Institute of Physics and Technology) will not accept you without a strictly mathematical education. Very few people can finish the evening of the Mekhmat of Moscow State University, study for 4 years, it costs a million. Few people go from full-time to data science for many reasons, including not taking it. Python in data science is generally just a tool, you don’t need to code there as a programmer, there is mathematics and analyst thinking. Look at what materials are being prepared at the ShAD and what they are doing there (there are also few people pulling, it’s difficult), this is about data science. With your background, you will not be hired anymore, the competition is big, there are many graduate students among them.
And you understood this and decided to go into the web, because you know the python, and there it is like a dog's fifth leg, PHP with JS is king and god, and this work is bad, if frankly and unpromising.
So :)) Ie. No chance. Some who are now there, literally from elementary grades participated in mathematical Olympiads (parents dragged), and nerds (in a good way) are all there. So this topic is certainly better than the enterprise and site-clearing.

Answer 3

In data science, you will most likely be flunked at an interview because they will ask what rank you have on kaggle.com and what problems you solved with the kegle.
On the web, they will ask you to show a ready-made online store or some other more or less finished project by you, even if it is not in production. In fact, such a project can be written on the knee in 2 weeks.
I would advise either programming 1c because you are an accountant, or a tester, i.e. QA in python.

Answer 4

Rule of good manners: Punch one sphere, then you will get to success.

Answer 5

Solve problems in new areas with new tools.

Answer 6

But, on the one hand, if you go into layout and other web, i.e. it's even further away from data science

layout yes, but backend is quite relevant for Data Science. Working in this direction, you will develop your skills in writing code, working with tools. You will develop as an engineer. And in DS you can taxi later.