Julia for High Performance Data Analysis
Julia is a high-level programming language that combines the speed of HPC languages like Fortran and C with the ease of Python or MATLAB, effectively solving the two-language problem. It is especially well-suited for numerical computing and offers a rich ecosystem of libraries for data analysis, machine learning, and scientific modeling.
Join us for Julia for High Performance Data Analysis, a hands-on workshop where you’ll learn to work with large datasets, optimize code, and apply Julia to real-world problems—all while maintaining clear and efficient workflows.
Practical information:
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- 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.
- Price: Free
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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.
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