- [JuliaHub](https://juliahub.com/ui/Home) indexes open-source Julia packages and makes the entire ecosystem and documentation searchable from one place.
- [JuliaAcademy](https://juliaacademy.com/courses), which has free short courses in Data Science, Introduction to Julia, DataFrames.jl, Machine Learning, and more.
- [Data Science Tutorials](https://alan-turing-institute.github.io/DataScienceTutorials...) from the Alan Turing Institute.
- [Learn Julia in Y minutes](https://learnxinyminutes.com/docs/julia/), a great quick-start if you are already comfortable with coding.
- [Think Julia](https://benlauwens.github.io/ThinkJulia.jl/latest/book.html), a free e-book (or paid print edition) book which introduces programming from the start and teaches you valuable ways of thinking.
- [Design Patterns and Best Practices](https://www.packtpub.com/application-development/hands-desig...), a book that will help you as you transition from smaller, one-off scripts to designing larger packages and projects.
- Lots more topical books (Statistics, Optimization, etc) if looking for a Julia-oriented subject matter
For me, the most important thing to grasp when coming from another language was that Julia’s multiple dispatch brings with it effectively a whole paradigm of “dispatch-centric programming” that you have to embrace to really get the most out of Julia, including the c-like speed (have to strictly avoid type-instability for that) and the composability that everyone talks about.