[Workshop] Julia for High Performance Data Analysis

Overview

Julia is a modern high-level programming language that is fast (on par with traditional HPC languages like Fortran and C) and relatively easy to write like Python or Matlab. It thus solves the two-language problem, i.e. when prototype code in a high-level language needs to be combined with or rewritten in a lower-level language to improve performance. Although Julia is a general-purpose language, many of its features are particularly useful for numerical scientific computation, and a wide range of both domain-specific and general libraries are available for statistics, machine learning, and numerical modeling.

Join us for Julia for High Performance Data Analysis, a hands-on workshop designed to equip you with practical skills for working with large datasets, optimizing code, and leveraging Julia’s rich ecosystem of libraries. You’ll explore real-world applications in data analysis, numerical computation, and machine learning, all while discovering how Julia can streamline your workflow and elevate your performance without sacrificing code readability.

Who is this workshop for?

This workshop is aimed at students, researchers, and developers who:

  • Are already familiar with one or more programming languages such as Julia, Python, R, C/C++, Fortran, or Matlab.
  • Work with large datasets or need to perform computationally intensive modeling and analysis.
  • Want to develop high-performance data science applications while staying within a productive, high-level programming environment.

Prerequisites

  • Experience with one or more programming languages.
  • Familiarity with basic concepts in linear algebra and machine learning.
  • Basic experience working in a terminal is helpful.

Key takeaways

This online workshop will start by briefly covering the basics of Julia’s syntax and features, and then introduce methods and libraries which are useful for writing high-performance code for modern HPC systems. After attending the workshop, you will:

  • Be comfortable with Julia’s syntax, built-in package manager, and development tools.
  • Understand core language features like its type system, multiple dispatch, and composability.
  • Be able to write your own Julia packages from scratch.
  • Know how to perform various linear algebra analysis on datasets.
  • Be productive in analyzing and visualizing large datasets in Julia using dataframes and visualization packages.
  • Be familiar with several Julia libraries for visualization and machine learning.
  • Understand how to analyze large datasets efficiently in Julia using statistical methods.

Tentative Agenda

Time (9:00-12:00) (CET) Contents May 26 Motivation, julia syntax, special Julia features, developing in Julia, package ecosystem May 27 Motivation (julia for data analysis), data formats and dataframes, linear algebra, machine learning (data part) May 28 Machine learning, clustering and classification, deep learning May 29 Non-linear regression, scientific machine learning, conclusions and outlook

Regulations

Due to EuroCC2 regulations, we CAN NOT ACCEPT generic or private email addresses. Please use your official university or company email address for registration.

This training is intended for users established in the European Union or a country associated with Horizon 2020. You can read more about the countries associated with Horizon2020 HERE.

Contact

For questions regarding this workshop or general questions about ENCCS training events, please contact training@enccs.se.