Both Python and R can be invoked within Julia by using either the PyCall or RCall packages. A quick intro to Julia’s PyCall and RCall packages. Using Python and R with Julia

Taking a complete look at creating complex constructors with Julia. There are two different types of constructors in the Julia programming language: Inner Constructors and Outer Constructors. Both of these constructors have their own specific purpose and are both presented to return the same type ultimately. However, we can use outer constructors to support arithmetic and type-processing for different types with dispatch.

Learn how to turn Julia into Python! Learn how to use the object-oriented programming paradigm in the Julia programming language. Why would you want to make Julia an object-oriented programming language? On top of just working with Julia, you could also recreate your favorite Python packages in the language using this method! While Julia is traditionally seen as a more functional programming language, using some of Julia’s cool tricks — like outer constructors and incredibly dynamic typing syntax, we can actually make the programming language become object-oriented quite quickly and very effectively!

In this talk I present the fundamental design concepts behind DataFrames.jl to help potential users get started with using it. I show how an exemplary pandas data processing pipeline can be transferred to DataFrames.jl. Finally I discuss how DataFrames.jl integrates with the whole data science ecosystem available in the Julia language.

The first step in any data analysis is to get the data. There’s arguably no easier way than to load a CSV file into a data frame. In this tutorial, we will explore how to do that in Julia Programming Language. In Julia are separated into two modules — CSV.jl and DataFrames.jl and there is more than one way how to combine them. In this guideline we will see: 3 ways how to load CSV to the data frame, non-UTF-8 encodings, explain the most common parameters of the CSV method, how to install Julia and run it in Jupyter Notebook

We are going to explore some of most currently used programming languages in ambit of Data Science & Cloud Development: Julia; Go; Python; Scala; Javascript

We'll look at the advantages that Julia has over Python: Pace; Friendly Syntax; Memory management; Parallelism; Julia’s Machine Learning Libraries. Julia was designed for scientific and numerical computation. Thus it’s no surprise that Julia has many features advantageous in this case.

I’ll show you the steps to add Julia to Jupyter Notebook from scratch. Julia is a high-level, high-performance dynamic language. Julia has been gaining popularity in recent times as it can also be used for Data Science & Machine Learning.

A gentle introductory guide on getting started with Data Analysis in Julia. Getting started with Julia is pretty straightforward, especially when you are familiar with Python.

In this tutorial we will be seeing how to install Pluto Notebooks and how to use PlutoUI widget in Julia Programming Language.Written Tutorial & Codehttps://...

In this tutorial, we will explore JuMP a Julia library useful for solving optimization problems such as linear programming,etc

I will be showing you how you can deploy projects in the Julia Programming Language on Heroku, a cloud application platform. How you can publish Julia web apps on Heroku!

Top 9 Machine Learning Framework in Julia: Flux, Mocha.jl, Knet, Scikitlearn.jl, Tensorflow.jl, MXNet.jl, MLBase.jl, Merlin, Strada. Julia is developing the number of Machine learning packages and Frameworks Rapidly. Like Python Julia is also a high-level programming language and we need to write less amount of code in Julia which is the same case as of Python.

A quick lesson on how modules and imports work in the Julia language. In order to add a package in Julia, we will use Pkg. You can do this by either importing Pkg into Julia with using, or just using the Pkg REPL that can be accessed by pressing ] in the Julia REPL.

Learn how to build a vanilla neural network model WITHOUT any Machine Learning package but instead implement these mathematical concepts with Julia. Learn how to build an Artificial Neural Network from scratch in Julia

Julia Visualization Libraries: Which Is Best? An overview of the visualization libraries commonly used in the Julia language

How To Use Syntactical Expressions And Dispatch In Julia. The bread and butter of Julia: Syntactical expressions and multiple dispatch

JuliaHub: The Greatest Approach To Putting Together Packages And Documentation. How JuliaHub automates all of the things programmers hate to do.

Getting Familiar With Loops In Julia. An introduction to using loops in tandem with conditionals in the Julia programming language.

Today we are going to take a look at actually writing functions to deal with our types, as well as a broad overview of how conditionals work inside the Julia language.