"Scientific computing" is such a broad term that it's not terribly useful. For numeric computing, Fortran, C, and C++ will likely remain at the top for years t

"Scientific computing" is such a broad term that it's not terribly useful. For numeric computing, Fortran, C, and C++ will likely remain at the top for years to come. For statistical and exploratory data analysis, R has long been king (and closed-source tools before R), but Python is rapidly coming out on top here. Clojure could challenge here, but it's far from having the popularity of R or even Python right now. For machine learning, Matlab along with its open-source analog Octave, have long been de rigueur, but Python is rapidly gaining ground here, too. I think here is where Julia is hoping to gain ground, at least initially. So it's a bit odd to lump different areas of scientific computing together, but even odder to neglect the one language that has a chance of topping more than one of these areas. And I say that as someone who is moving from Python to Go and Rust for a lot of my software (but still stay with Python for data exploration). Nonetheless, not a bad introduction to the languages in question.