10 Best Julia Courses for Developers
The Best Julia online courses to use Julia in data science, machine learning, and parallel computing at both beginner and advanced levels.
The Best Julia online courses to learn Julia for data science, machine learning, and parallel computing for beginners
Why learn Julia?
Julia is most recommended for use in data science, machine learning, parallel computing, data visualization due to its high performance. Julia uses LLVM - a target-independent optimizer and code generator, to compile the code and run it on different OS and architectures.
Julia is open source and has a very good developer ecosystem around it. Julia provides built-in libraries and packages that make it faster to work with scientific computing and numerical computing.
Julia is easy to use with various IDEs using built-in packages and plugins:
- Julia vscode plugin - for the VSCode
- Julia package - to run in the Jupyter notebook
- Pluto. ji package - to run in Pluti notebooks
It also solves the two-language problem, i.e, you need not design the prototype of the system first in a static language and then implement it in a dynamic language. You can directly build the system using the dynamic functionalities Julia caters us, including logging, profiling, debugging, asynchronous I/O and a package manager.
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1. Parallel Computing in Julia
This course provides in-depth knowledge to achieve multi-threading and distributed computing using Julia.
This course teaches the fundamental commands in Julia to set up parallel computing using multi-threading. It also covers Julia packages and tools. This course will explain the practical applications of parallelism. The course uses real-world examples to dive into JuliaHub infrastructure, Julia package ecosystem, and techniques to optimize the parallel programs performance.
- Course rating: 4.8 out of 5.0
- Duration: 2.5 Hours
- Course enrollments: 5700+
This course is the best for Julia developers to understand parallel programming and its implementation. The course provides certification on completion through Udemy.
2. Julia Scientific Programming
This course is offered by the University of Cape Town to learn Julia Scientific Programming. This four-module course introduces users to Julia as a first language.
In this course, you will understand the capacities of Julia programming and create programs in Jupyter notebooks. This course teaches you about logical structures and, use of different Julia packages to create predictive models and generate graphs using the data. You will learn to build dataframes, conduct statistical tests, and create data visualizations.
- Course rating: 4.5 out of 5.0
- Duration: 19 Hours
- Course enrollments: 38000+
This course has 15 assignments and a certification on the course completion that make it the most popular Julia course among students.
3. Data Manipulation in Julia
This course covers complex data manipulation using real-world data sets. In this course, you will learn the DataFrames package to transform and group the dataset. You will also use the plot packages to create data visualization. It also covers how to handle columns and how to calculate summary statistics for the group data.
- Course rating: 4.7 out of 5.0
- Duration: 4 Hours
This course is for data scientists with a basic understanding of Julia programming. It's highly recommended to learn Julia fundamentals with Introduction to Julia and intermediate Julia before starting this course. The course provides certification on completion through Datacamp.
4. Julia Programming for Machine Learning
This is the best course to learn the Julia programming fundamentals along with machine learning and data analysis concepts in Julia.
This course starts with Julia syntax, data types, and data structures for beginners. It covers fundamentals of functions, constructors, macros, and methods. In the second part of the course, it teaches data manipulation using DataFrames and TimeSeries, how to solve machine learning problems using in-built packages, and create data visualization using tools in Julia. This course also covers multiple projects to understand the core concepts.
- Course rating: 4.5 out of 5.0
- Duration: 14.5 Hours
This course is the best for beginners to start learning Julia from scratch. The certification will be provided on the course completion.
5. Introduction to Data Visualization with Julia
Learn the basics and advanced techniques of data visualization in Julia with this course. This course teaches about Plots.ji - the most popular Julia package to create histograms, line plots, scatter plots, box plots, and violin plots from the real-world data sets. The course also covers the concept of customization in data visualization using themes, color palettes, multiple plots with grid layouts, and series plots.
- Course rating: 4.7 out of 5.0
- Duration: 4 Hours
This is an intermediate course and requires basic knowledge of Julia. It provides a certificate.
6. Julia 1 Fundamentals
This course explains the fundamental concepts of Julia for beginners. The course starts with an introduction and how to set up Julia, and then goes into the basic concepts, including data types, functions, and operators. Later, it covers how to work with large external data sets such as databases and large JSON files, and CSV files. This course also teaches about how Julia works with external programs and how to run Julia code from Python.
- Course rating: 4.5 out of 5.0
- Duration: 4 Hours
This program is good for gaining Julia knowledge and skills. The course plan includes the certification.
7. Learning Julia
This course begins with the introduction of the Julia programming language, and covers its syntax, basic concepts, features, data types, numbers and strings, data type casting and control, and data structures. The course also explains how Julia combines the performance of C programming with the flexibility of Python, which makes it the best candidate for data science.
- Duration: 2.2 Hours
- Course rating: 3,385 total enrollments
This is the basic Julia course that helps you get familiar with Julia and be ready with the Julia setup in no time.
8. Optimization with Julia
This is the best Julia course to learn how to solve complex problems using Julia. The course explains how to solve and optimize complex computer science problems using various solvers.
It covers the real-world examples and a step-by-step guide on how to optimize them using solvers such as the linear programming using CBC, Gurobi, CPLEX solvers, Mixed-Integer Linear Programming using MILP solvers, Nonlinear Programming using NLP solvers, Mixed-Integer Nonlinear Programming using Bonmin and SCIP solvers in Julia.
This course also explains double summation and multiple constraints problems and how to solve them in Julia.
- Course rating: 4.6 out of 5.0
- Duration: 6 Hours
This course is the best for graduate and PhD students as well as for professionals who need to solve complex problems.
9. Introduction to Julia
This is the best Julia course for beginners to start with. This course starts with Julia basics such as variables, data types, and Julia scripts. It also teaches data structures concepts in Julia and how to use them to processa large set of data faster. You will also learn how to use functions and packages in Julia to reuse the code. The course covers the use of the DataFrames package to process a large set of CSV data in Julia.
- Course rating: 4.9 out of 5.0
- Duration: 4 Hours
This is the Julia certification course highly recommended for beginners who want to learn Julia for the first time.
10. Julia Programming For Data Science & Machine Learning
This course begins with the introduction of Julia and the fundamental concepts. The main focus of this course is how to use Julia in data science.
During this course, you will learn to analyse Data using DataFrames package and draw plots with the plots module in Julia. This course explains how to perform prediction using Linear Regression with the GLM Package and using Multiple Linear Regression on the existing data.
This course covers the high-level concepts such as logistic regression on data with Julia GLM, cluster data using K-Means clustering algorithm,m and reduce dimension of the dataset with PCA ,a multivariateStats package.
- Course rating: 4.4 out of 5.0
- Duration: 4 Hours
- Course enrollments: 2100+
This certification course is best for beginners who want to learn Julia, as well as for developers who need to use Julia for data science.
Wrapping Up
In a nutshell, we can say that Julia is indeed a dynamic, high-performance, efficient, and good-to-go language for the developers of the new age. With several added advantages and difficult-to-ignore features, Julia is a good choice if you are looking forward to dynamic mathematical, scientific, or statistical calculations in data science. In case you want to explore more, you can take the free Julia courses.
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