A year of working as data scientist

I want to give a brief overview of working as a data scientist for one year. I wrote several posts about my progress and now it is time to look back and see what was accomplished (though all of this is only a beginning). Maybe my story will help to motivate some people planning to work as DS.

• Started working in a bank on April, 17 last year;
• Completed machine learning specialization by Yandex and MIPT on Coursera;
• Completed most of cs231n course;
• Build a first pet-project: online recognition of handwritten digits. This project was well received and people still use it - the app was used at least 2000 times in the current month;
• Realized that working in the bank isn't interesting and rewarding, also SAS isn't fun at all. I changed my job and started working in a startup;
• Completed second session of ml_open course (here is a link for english version) and finished at the 5th place;
• During 2 months of working in startup I did only one task, and there were no definite plans for future. Also several crazy thing happened and I decided that I deserve something more. So I changed my job again;
• Completed two courses (kaggle and nlp) in Advanced Machine Learning specialization on Coursera;
• As a part of final task of nlp course I made a telegram chat-bot @amlnlpbot;
• Started taking part in official and unofficial meetings of data scientists;
• Continued working on my portfolio: https://erlemar.github.io/ ;
• Realized that there was only little professional development for me in the current company and decided to change my job once more;
• Tried to take part in machine learning competitions but without success. Currently I take part in a competition on Kaggle, where prizes are given for kernels with the most votes, here is the link;
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Thoughts:

• A year ago I couldn't imagine how amazing, motivating and useful would be ods.ai community :) It is possible that without it I would still stagnate in a bank;
• It is very difficult to find balance between practice and theory. I know that my theoretical knowledge isn't enough, especially regarding statistics and maths, but I have yet to have problems with these spheres at job, so currently I practice morel
• Sometimes I feel that most companies (excluding big or/and advanced ones) hire DS just to have them, while having little relevant tasks or not understanding their possibilities;
• Realized it is necessary to take part in competitions, even if it is only for experience;
• I suppose it is worth investing more time into learning DL. Of course there are many interesting tasks without it, but most of them are related to marketing;
• It is important to improve programming skills;

Plans:

• Pay a lot of attention to DL. Complete fast.ai course or some parts of it, then try to implement popular papers;
• Take part in competitions on Kaggle. Earn at least a silver medal;
• Maybe try learning R to understand why so many people praise it for data processing and visualizations as well as Shiny;
• Improve programming skills. Maybe learn Java/Scala for writing production solutions;
• Create 1-2 more pet-projects;