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How to jump into Data Science

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I'm Yury Kashnitsky leading mlcourse.ai - open Machine Learning course by OpenDataScience (ods.ai). In this talk, I'll describe the learning path you need to step in to find your first DS job. Assuming that basic ML is covered (mlcourse.ai, Andrew Ng's course or similar). I'll show your some typical questions that I like to ask at interviews myself.

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How to jump into Data Science

  1. 1. How to jump into Data Science by Yury Kashnitsky (@yorko), leader of mlcourse.ai 1
  2. 2. Intro • BS from MIPT, applied physics • MS from MIPT, applied math • Ph.D. from HSE, applied math • Former Business Analyst, BI dev • Former DS at Mail.ru Group • Leader of mlcourse.ai • DS, NLP practitioner at KPN 2
  3. 3. Who in general are Data Scientists? by Alex Natekin3
  4. 4. Various flavours of DS by Alex Natekin4
  5. 5. Preparation and today’s plan • Python • SQL • Math • Algorithms • DevOps • ML & DL • Pet projects • Competitions • Interviews
 5 Join ods.ai!
  6. 6. Python • Basic level - CodeAcademy, Datacamp, Dataquest, Kaggle Learn • Medium level - EdX course or similar • Advanced level - is it really needed for Junior DS? Anyway, CS Center course (rus.) or similar 
 • Don’t study it just for fun. It’s no fun! • Refresh before an interview • Kaggle Learn or similar will do • The rest you’ll pick up at work 
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  7. 7. Math Treat math as fundamental science (well, it is) - it’s worth your time investment, though hard to describe in what specific way it’s useful Resources: • A single link is Open MIT courseware • Math for ML gives a nice overview • A list with resources collected within ODS (rus.) 
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  8. 8. Algorithms • Two classic courses are those by R. Sedgewick and T. Roughgarden • Leetcode! + Interviewbit • “Cracking the coding interview” • As for interviews, it’s a very controversial topic 
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  9. 9. DevOps • Learn git! TryGit, you need it to collaborate with people • Good to show that you understand how models are deployed. Docker is essential • Background in SWE is often appreciated • Also fine to learn this at work 9
  10. 10. ML & DL • Basic ML is covered in mlcourse.ai, Andrew Ng’s course or Coursera specialisations will suit as well • As for Deep Learning, Stanford’s cs231n (join ods.ai to pass it together from Dec. 2nd) and fast.ai are good options 10
  11. 11. Pet projects • Freedom to choose anything • A way to learn a lot yourself (eg. DevOps) • Something to stand out with, good for your CV Catalyst Albumentations Example: Crypto Fear & Greed index 11
  12. 12. Competitions • Kaggle is a very good platform to learn new stuff, especially in a new field • But be careful with the gamification part • Also good for your CV • But don’t write “I participated in a competition” 12
  13. 13. Interviews • Don’t just sit and study. Practice interviews. Keep getting feedback! • Learn to fail if needed. Still experience • Nervousness is also a factor - be prepared 13
  14. 14. How to jump into Data Science by Yury Kashnitsky (@yorko), leader of mlcourse.ai 14

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