Thursday, April 19, 2018

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: ;
  • 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;


  • A year ago I couldn't imagine how amazing, motivating and useful would be 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;
  • Pay a lot of attention to DL. Complete 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;

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