- [JuliaLang.org](https://julialang.org/), the home site with the downloads to get started, and links to learning resources.

- [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

The official manual is very good, and reads more or less like a textbook: https://docs.julialang.org/en/v1/

Yeah, this is probably actually the closest equivalent to the Rust Book, but I’ll also second the suggestion from the other comment of “Think Julia” (beginner) and “Design Patterns and Best Practices with Julia” (intermediate /advanced).

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.