How to Self-Study to Be a Data Scientist – Adapted Compilation

My name is Ira, I blog about mathematics, products and ML, do projects in DS / ML, as well as courses on creating ML projects. From my blog audience and friends I often get questions like “Is it worth spending money on this or that expensive course with the codename” Become a DS in two months “or still learn to be a data scientist on your own and for free, and in this case, where to begin?”

Recently, I have collected an answer to this question in detail, in my opinion, to share it with a wide audience. Not all paid courses are bad (although most – yes – due to selection mechanisms, sales and weak programs), but I will write about them separately. It seems to me that you need to take into account more personalized parameters in order to correctly choose a good course for the money.

  1. First, the list includes free online courses and self-study resources, which she passed herself or is advised by the professional community of analysts and data Scientists (often mentioned in ODS.ai), where you do not need to go through some kind of selection or be limited to offline visits.

  2. Second, of course, it is not a complete list of online courseswhich you can find, but it contains the best courses from strong math and Computer Science schools in the world and other common resources among the pros from what I could filter to my taste.

  3. Thirdly, I will start with a short list, with which, it seems to me, it is worth starting learning the subject, and it goes immediately into the next paragraph.

Where do I recommend you start in order to spend time efficiently?

Assuming the aspiring Data Scientist has already completed Harvard-style prep courses CS50 on the basics of programming, pythontutor.ru or course on Stepik from the Institute of Bioinformatics, I will advise a few steps that should become a solid base. Further links are all complete to make it easier to copy:

  • Register in the most popular professional slack community in the CIS Open Data Science ODS.ai, join as many chats as possible, including about mentoring, training and careers and communicate with locals in order to broaden your horizons regarding employers, interview requirements, positions and their differences in different companies, etc., find good mentors, maybe … there are such in the community!

  • Take an introductory course on Math and Python for Data Analysis on Coursera — paid, inexpensive, and good. www.coursera.org/learn/mathematics-and-python

  • Programming – go to leetcode.com all relevant exercises: these are free or inexpensive in the premium version at a price / quality ratio (it includes exercises for interviews in FAANG).

  • Pass the mlcourse.ai Is an open source machine learning course from ODS. The authors were able to develop a machine learning course with a balance between theory and practice, when in a lecture you analyze mathematics in sufficient detail, and then practice first in a notebook, then in Kaggle.

  • To learn how to solve various problems and optimize code – participate in competitions in data analysis and machine learning on the platform kaggle.com

Math for DS / ML

Python & SQL for DS / ML

ML beginner courses

  • No matter how scolded this course is because of the outdated Octave programming language (which is written in Matlab), for my taste this is the simplest and most understandable ML course so far. Machine Learning (Coursera) https://www.coursera.org/learn/machine-learning – Stanford Machine Learning Course by Andrew Ng

  • mlcourse.ai Is an open source machine learning course from ODS. The authors were able to develop a machine learning course with a balance between theory and practice, when in a lecture you analyze mathematics in sufficient detail, and then practice first in a notebook, then in Kaggle.

More advanced ML courses

Deep learning

(It seems to me that you can take one course from the list, and look at the rest at the subject of additions)

Natural Language Processing

Reinforcement Learning & Self-driving cars

Data Engineering & MLOps

  • Free DE course from Dmitry Anoshin, data engineer from Microsoft, ex-Amazon: Getting start with Data Engineering and Analytics https://datalearn.ru/ (course is being prepared in progress)

Competitions

Well, and to learn how to solve various problems and optimize code – participate in Kaggle. https://kaggle.com/

Besides Kaggle, there are several more competitions:

Success in self-preparation!

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