Julia Scientific Programming
Provider: University of Cape Town
This course guides you from Julia’s basics to its application in scientific research, making it ideal if you aim to master this fast and efficient programming language.
Starting with an introduction to Julia version 1.0, the course highlights why Julia is a go-to for scientific computing, setting a solid foundation.
You’ll quickly move on to practical skills, such as navigating the Julia REPL (Read, Evaluate, and Print Loop), handling arithmetical and logical expressions, and understanding Julia’s type system.
A significant focus is on both built-in and user-defined functions, crucial for any programmer.
The course ensures you’re equipped with the latest tools, guiding you through setting up IJulia for Jupyter Notebooks, a favorite in the data science community.
Despite Juno’s discontinuation, it introduces alternatives like the Julia VS Code Extension, keeping your learning environment modern and efficient.
As the course progresses, it dives into real-world applications, using the 2014 Ebola Epidemic as a case study.
You’ll learn to manage data, from loading to creating .csv files, and employ Julia for data analysis, including for loops and plotting with the Plots package.
This approach not only solidifies your Julia skills but also demonstrates its power in addressing complex scientific questions.
The curriculum then advances to scientific modeling, specifically through SIR Models of Disease Dynamics, offering a deep dive into epidemiology modeling with Julia.
You’ll gain hands-on experience in fitting models to data, a key skill in scientific research.
The course also covers essential statistics, from creating simulated data and working with data frames to visualizing and analyzing data, preparing you for a wide range of scientific computing challenges.
With a mix of peer-graded assignments and ungraded labs, the course emphasizes practical application, ensuring you’re not just learning but applying Julia in meaningful ways.
By the end, you’ll not only have a robust foundation in Julia programming but also understand its significant potential in scientific computing.