How to jump into Data Science

1. How to jump into Data Science by Yury Kashnitsky (@yorko), leader of mlcourse.ai 1

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. Who in general are Data Scientists? by Alex Natekin3

4. Various ﬂavours of DS by Alex Natekin4

5. Preparation and today’s plan • Python • SQL • Math • Algorithms • DevOps • ML & DL • Pet projects • Competitions • Interviews  5 Join ods.ai!

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   6

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.)   7

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   8

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. 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. 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. 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. 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. How to jump into Data Science by Yury Kashnitsky (@yorko), leader of mlcourse.ai 14