An Introduction to the Julia Programming Language
| Julia_001 | |
| 30.04.2024 | |
| 2.5 Std. | |
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| ETH Zürich, Zentrum | |
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| 32 | |
| 60 | |
| Kostenlos | |
| HG D 7.1, Rämistrasse 101, Zürich | |
| For students | |
| English | |
| Julia offers a fresh approach to scientific computing, high-performance computing and data crunching. Recently designed from the ground up Julia avoids many of the weak points of older, widely used programming languages in science such as Python, Matlab, and R. Julia is an interactive scripting language, yet it executes at speeds of C(++) and Fortran. Its qualities make it an appealing tool for the scientist and student. Julia has been gaining traction in many fields of science over the last years in applications ranging from high performance simulations, data processing, statistics, machine learning, differentiable programming to general modelling. The Julia package ecosystem necessary for such applications has substantially matured, which makes it readily usable for research and studies. | |
| Participants will be able to write Julia programs, to install and use packages, and to find help and documentation. | |
The course will use both teaching and hands-on exercises. Topics are: • Learn about the Julia language and what sets it apart from others • Write simple Julia code to get you started with scientific programming (arrays, loops, input/output, etc.) • Hands-on exercise on installing Julia packages and management of package environments (similar, e.g., to virtual-environments in Python) • Code a small project, such as a simple 1D model or a data processing pipeline, with a particular focus to achieve performance on par with C or Fortran. | |
| • Basic programming knowledge in another language • Working Julia installation on your laptop (see below) | |
• Please install Julia beforehand. Recommended via https://github.com/JuliaLang/juliaup (contact us if you struggle) • Course material will be provided via a GitHub repository |