Learning data science can be frustrating because it is multi-disciplinary. There is much context switching, tools switching, etc. This is further exacerbated if you have taken boot camps that hope to turn you into a data scientist within X weeks (X <= 12). Given the time constraint, they teach you a mish-mash of EVERYTHING (By the way, you really do not become a data scientist after the boot camp) Not easy!
Here are a learning strategy I have for you. Remember them when it comes to learning Data Science.
Divide & Conquer
Multi-disciplinary means each discipline has to interact with each other. Such a process of connecting the dots can be frustrating. Don't get overwhelmed. Start small, understand as much as possible then move on to the next piece.
Concepts First, Tools Later
Focus on the concepts. It will help with learning the tools later. Concepts will guide your questions on tools, making your learning more efficient. I am not a big fan of going for the tools first and the concept later because tools come and go. Focus on the concept and you then not tied to a specific tool, in case the tool becomes obsolete.
Strong Foundation Helps!
For each of the disciplines, focus on getting a good foundation first. Once you are confident of it, go to the next level but be willing to re-visit the foundation. You'll strengthen the foundation that way. As you start working on your individual projects, you will definitely come across situation where you are unsure, and that is the time to re-visit your fundamentals and foundation. If you are interested to find out more what are the fundamental knowledge you should have as a data scientist, check out the following posts.
1 - Mathematics & Statistics
3 - Domain Expertise
These are the learning tips I have for now. If you have some tips to share, feel free to reach out to me on my LinkedIn or Twitter! If you find my content to be useful, consider signing up for my newsletter to keep in touch! :)