This course aims to teach learners how to use the Julia programming language for data analysis and statistical computing. By the end of the course, students will be able to perform data manipulations, statistical calculations, and create visualizations using Julia. The course covers topics such as working with JuliaDB for data operations, utilizing Julia's statistical packages, conducting statistical tests, and setting up Julia environments. The teaching method includes tutorials, practical examples, and hands-on exercises. This course is intended for individuals interested in data analysis, statistical computing, and programming using Julia.
Overview
Syllabus
Julia Language Database JuliaDB.
Selections in JuliaDB.
Reductions in JuliaDB.
Grouping in JuliaDB to calculate descriptive statistics.
Joining two tables in JuliaDB.
Importing a csv file in JuliaDB.
Gradient descent tutorial using Julia code.
Julia Statistics Package.
Julia Statistics Package.
Julia for medical statistics 2020 edition.
Julia plots using VegaLite.
Julia environments.
How to use Pluto notebooks and set Julia environments.
Complex numbers introduction using Julia.
Introduction to Euclidean vector spaces with Julia.
Tutorial on matrices with help from Julia.
Solving linear systems using elementary row operations with Julia.
Ordinary least squares tutorial using Julia.
Gram Schmidt process for QR decomposition using Julia.
Bootstrap resampling for confidence intervals and sampling distrubutions.
Estimating a p value for the difference in means between two groups.
F ratio sampling distribution under the null hypothesis using Julia.
Install Julia with Jupyter and Pluto notebooks and VS Code.
Taught by
Dr Juan Klopper
