Univext is not a marketplace. We hire only the best Julia teachers to ensure that you get the best learning experience. We believe education is important and we control the quality of our
teachers by going through a rigorous hiring process. Unlike other platforms, we don't want you to waste your time and money looking for a good teacher. We went through the process for you.
Whether you're a beginner or an expert, we have the right teacher for you. Our teachers are experienced and will help you achieve your goals. They will help you in Julia and will
make sure you become a master in no time.
Learn Julia at your pace.
Whether you're a busy professional looking to enhance your skills, a student seeking additional academic support, or someone simply looking to pursue a personal interest, you have the
freedom to book classes when you solely want to.
Choose the modules you are interested in to become an expert in Julia. On Univext, focus
on what you want to learn and pay only for the classes you need. Check the curriculums below to see the skills you can learn.
What is Julia:
Julia is a programming language created by MIT researchers in 2009 and which will arrive to the general public in 2012. Currently, the Julia language is the most popular programming
language next to the famous Python language. It is a high-level, high-performance, dynamic programming language designed for scientific computing. Having wanted an open-source language while being liberal, the designers of Julia combined all the characteristics of the most popular languages in order to obtain a language considered to be more efficient. Thus Julia would allow the reunification of all other languages into one. In addition, many institutions use it such as universities and research institutes, multiple platforms including Netflix and Capital One.
What you will learn and cover during your lessons:
Introduction to Julia
This module covers the basic syntax of Julia programming language, including variables, data types, and control structures.
In this module, you will learn how to work with advanced data structures in Julia, including arrays, tuples, and dictionaries. You will also learn about the performance characteristics of these data structures and how to choose the right one for your use case.
This module covers how to leverage Julia's built-in support for parallel and distributed computing to scale up your code and run it on multiple processors and machines. You will learn how to work with remote references, message passing, and other key concepts of distributed computing.
In this module, you will learn how to debug and profile your Julia code to identify and fix performance issues. You will learn about tools such as the Julia debugger, profiling tools, and code coverage analysis.
This module covers how to work with databases in Julia, including connecting to databases, executing SQL queries, and working with data frames. You will learn about popular packages such as SQLite.jl, MySQL.jl, and DataFrames.jl.
In this module, you will learn how to develop and publish Julia packages, including creating modules, writing tests, and creating documentation. You will also learn about the Julia package manager and how to manage package dependencies.
This module covers how to perform numerical optimization in Julia, including using packages such as Optim.jl, NLopt.jl, and JuMP.jl. You will learn about various optimization algorithms, including gradient-based methods, nonlinear programming, and global optimization.
In this module, you will learn how to use Julia to write parallel programs that can take advantage of multicore processors and distributed computing architectures, including working with the Distributed and SharedArrays packages.
This module covers how to create visualizations in Julia using popular packages such as Plots.jl and Gadfly.jl, including working with different types of charts and customizing visualizations to meet your needs.
In this module, you will learn how to use Julia to develop machine learning models, including working with popular packages such as Flux.jl and MLJ.jl, and how to apply machine learning techniques to real-world data.
This module covers how to use Julia to perform numerical analysis tasks, including working with linear algebra, optimization, and differential equations, using packages such as LinearAlgebra.jl and Optim.jl.
In this module, you will learn how to use Julia to perform data analysis tasks, including working with data frames, data manipulation, and statistical modeling using popular packages such as DataFrames.jl and StatsModels.jl.
This module covers how to use Julia for high-performance computing tasks, including working with distributed computing, GPU programming, and memory management using packages such as CUDA.jl and DistributedArrays.jl.
This module covers advanced Julia types and multiple dispatch, including working with abstract types, parametric types, and composite types. You will also learn how multiple dispatch works in Julia and how it enables powerful generic programming techniques.
In this module, you will learn how to use Julia for machine learning, including working with the Flux.jl package for neural networks, using the MLJ.jl package for model selection, and creating machine learning pipelines.
This module covers how to build and distribute packages in Julia, including working with the Pkg.jl package manager, creating documentation with Documenter.jl, and publishing packages on the General registry.
This module covers how to write parallel and distributed programs in Julia using the built-in features for distributed computing and message passing, including working with parallel loops, distributed arrays, and remote function calls.
This module covers how to use Julia for machine learning, including working with popular machine learning frameworks and libraries such as Flux.jl, MLJ.jl, and TensorFlow.jl, and implementing advanced machine learning models such as deep neural networks.
In this module, you will learn how to use Julia for data science, including working with data frames, data visualization libraries such as Plots.jl and VegaLite.jl, and advanced statistical analysis techniques.
Numerical Methods and Scientific Computing in Julia
This module covers how to use Julia for numerical methods and scientific computing, including working with linear algebra libraries such as BLAS and LAPACK, and implementing advanced numerical methods such as finite element methods.
In this module, you will learn how to use Julia for web development, including working with web frameworks such as Genie.jl and HTTP.jl, and implementing web applications with advanced features such as real-time communication and server-side rendering.