Julia for Data Science in Healthcare

Julia is a programming language designed for data science to make numerical computing easier. It’s said to be faster and more efficient than Python, which many healthcare iT brands already use for data science. This blog post will explore Julia’s potential for use in healthcare data science projects and see how it compares to Python.

What the heck is Julia, and what are its benefits for data science in healthcare analytics?

Julia is a high-level, high-performance dynamic programming language for technical computing with a syntax familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Julia’s Base library provides basic data structures and algorithms, linear algebra, numerical integration, random number generation, and string processing. In addition, the Standard Library provides tools for network and web programming, databases and persistence, metaprogramming, numerical algorithms, unit testing, and more. Third-party packages extend Julia’s capabilities even further.