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.

Meet the Instructors

User picture
Evgeniya Vorontsova
Associate Professor, PhD, Far Eastern Federal University.
Topics: Mathematical Programming, Machine Learning, Python, C++ Programming, Graph Theory, Education https://www.researchgate.net/profile/Evgeniya_Vorontsova/

Course content

Theory lessons "Data Science. Introduction to Julia"
Practical lessons "Data Science. Introduction to Julia"
Новый модуль

Learners' reviews

Quite an interesting course for the initial study of Julia. If you previously studied Octave, Python, C, there will be no problems at all
It is not clear why the course on the new language the team Stepik made adaptive, also mixed with Python and R (within the framework of the big course Data Science). In my opinion, this will only scare away potential users of the language (nothing is clear, but they are already asking for some tasks to solve). For a normal introduction, I advise you to follow youtube on the request of julia language. A very interesting alternative to python, especially if the speed numpy is already missing for tasks / not vectorized / tortured with cython.

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