Are there correspondence courses or internships in data analysis in Russian?

Question

Python

I'm a professional PHP developer (+7 years of experience), I recently got excited about learning Python with an emphasis on data analysis. Therefore, the question is of interest - are there any Russian companies or universities with the opportunity to remotely train or study in this area?
Everything is complicated by the fact that I live in the provinces, so I need the possibility of a correspondence internship or training. Receiving a scholarship does not play a role for me, only knowledge is needed. On the contrary, I am ready to pay an adequate amount for such an opportunity.
I would like to find a program in Russian, since my English is still rather weak: I could more or less understand the Machine Learning course on Coursera, but there are big problems with communication in English.
I know about the Yandex ShAD, but for admission there you need a very good mathematical background, which I cannot boast of yet. Maybe there is something not with such a high entry threshold?

Answer 1

IMHO, there are several aspects here:
1. As brainick wrote , a mathematical background and English in data science is almost mandatory.
There are several reasons for this: the lack of good literature in Russian (both on theory and programming), the abundance of English terms (lift / top / cross-validation and others), the meaning of which in the translated literature is sometimes explained very vaguely.
2. If we talk about specific literature that is worth reading, I would single out several levels:
1. Business Analytics - Paklin, Oreshkov (the most basic and overview introduction)
2. Statistics / Terver (for me, books by Ayvazyan / Mkhitaryan are good )
3. SQL - without fail. The book "SQL for mere mortals" helped me at one time
4. Learning Python - M. Lutz (the most complete book on the language, everything you need for data science is definitely here)
5. Programming the collective mind (by the way, here in this excellent translation of the book)
1. Mathematical foundations of machine learning and forecasting - Vyugin (the book is difficult, without training in NMU textbooks on the topic of analysis and linear algebra it is better not to approach)
2. Python for Data Analysis (pandas in all its glory, there is nothing to add )
3. Examples and articles on building models in sklearn - on Habré, articles on this topic have often flashed lately, everything is pretty well described there.
1. Hadoop and others ("Hadoop in action", "Programming Pig")
2. Apache Spark - just read the description of the Python API.
There is one more thing - do not get too attached to one language and framework.
One of the annoying problems of python + pandas + sklearn is that this bundle does not scale well - with 2-3-4 GB of data it becomes difficult to fit them in RAM. I know about chunk-reading+partial_fit, but the accuracy of such models leaves much to be desired.
On the other hand, if you process this data in pyspark, then all the convenience of pandas.DataFrame is lost, and so on. The data science industry is rapidly evolving and acquiring new technologies, so you need to keep your finger on the pulse all the time.
UPD: DataFrame appeared in spark 1.3.

Answer 2

Here are lectures from JetBrains
pep8.ru/video/101
in general, here is a selection of video reports about python pep8.ru/video/?page=5

Answer 3

vk.com/wall-54530371_17507
vk.com/wall-54530371_17512

Answer 4

I agree with previous reports that data analysis without mathematics is hardly possible, so what can we start with?
You try in ShAD for this hand is not cut off! ;) There are generally quite nice people there, and even if you don’t enter, then at least you will learn a lot of new things and understand what you are missing (especially if you get to the interview). In addition, it seems that ShAD has a more loyal attitude towards correspondence students.
Just in the spring, a set, of course, you will have to prepare, but there is nothing supernatural there, I know from my own experience (approximately the first couple of courses in mathematics). Many write that they prepared on their own, to be honest, I didn’t prepare at all, but you shouldn’t be equal to this, since I have a mathematical education.
Good luck! ;)