Yury Kashnitsky provides advice on how to jump into a career in data science. He recommends developing skills in Python, SQL, math, algorithms, DevOps, machine learning, and deep learning through online courses and competitions on platforms like Kaggle. Key steps include taking online courses to learn Python, math, algorithms, machine learning and deep learning. It is also important to work on personal projects, participate in data science competitions, and practice interviews to prepare for entering the data science field.

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