Data Science. Introduction to Julia

This practical course will help you to begin studying Julia, a modern dynamic programming language, appropriate for scientific and numerical computing.

About this course


Welcome to our course dedicated to the Julia programming language!

We believe that there are many good reasons to study Julia, especially for data scientists. The Julia programming language is a flexible dynamic language, appropriate for scientific and numerical computing, with performance comparable to traditional statically-typed languages.
Nowadays the popularity of Julia is rapidly increasing in the field of data science, high-performance computing and scientific computing.

Julia is a very young programming language. The work on the Julia project began at the Massachusetts Institute of Technology (MIT) in 2009. It became open source (MIT licensed) in 2012.
Anyone competent in MATLAB can easily learn Julia because it's syntax is very similar to MATLAB. But it is not a MATLAB clone. Some advantages of Julia over comparable systems include:

  1. User-defined types are as fast and compact as built-ins
  2. No need to vectorize code for performance
  3. Designed for parallelism
  4. Powerful type system
  5. Elegant and extensible conversions and promotions for numeric and other types
  6. Efficient support for Unicode
  7. Call C functions directly (no wrappers or special APIs needed)
  8. Powerful shell-like capabilities for managing other processes

Upd (30.07.2018):  the course is not adaptive now, but linear.

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