In this chapter, we present several recipes with hints and tips on organizing workflows with Julia.
We start with
Revise.jl, an essential package in any larger Julia project, which automatically reloads function definitions in packages that have changed. Next, we explain how to benchmark and profile your code. For some scenarios, Julia alone might not be enough to achieve your data science/analytical goals efficiently and hence a need arises to use other programming languages. We show how to configure and mix Julia code with code and libraries from the two most popular data science languages: Python and GNU R. Finally, we present a standard template for a Julia project.