Introduction to Julia
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
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Course Description
Get Started with Julia Programming
Julia is a new and exciting programming language designed from its foundations to be the ideal language for scientific computing, machine learning, and data mining. This course will give you a running start in your journey with Julia.You’ll start by covering the very basics of Julia, so you can follow along if you have never programmed before. However, by the end of the course, you'll be analyzing DataFrames of patient data and book ratings. If you already know another programming language, we'll help you transfer this knowledge to Julia.
Learn the Basics of Julia Programming
Designed to encompass the speed of C, the general programming ability of Python, and the statistical prowess of R, Julia is simple to learn and very powerful.As you work through this course, you'll learn about the special features of the Julia programming language, including how Julia handles data types.
Get a Thorough Introduction to Julia
You'll learn how to store and process data in arrays, write reusable code in functions, and use other popular packages to borrow other Julia developers' code. You'll learn about Julia's headline feature of multiple dispatch and make use of this feature yourself.By the end of this course, you’ll be familiar with coding in Julia, understanding the basics, including data types and structures, the functions and packages, and how to use DataFrames to work with tabular data.
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Julia Fundamentals
Go To Track- 1
Julia basics
FreeTake your first steps towards coding with Julia and learn what makes this language unique. Learn about variables and data types, and perform simple calculations using Julia scripts and the interactive console.
- 2
Data structures
Learn how to process text data using strings and how to use arrays to process large amounts of data quickly and efficiently. Julia's inbuilt arrays are both powerful and easy to use.
Introduction to strings50 xpIndexing strings100 xpString interpolation100 xpSlicing strings100 xpIntroduction to arrays50 xpCreating arrays100 xpIndexing arrays100 xpSlicing arrays100 xpWorking with arrays50 xpModifying arrays100 xpFibonacci sequence100 xpAppending arrays100 xpOperating on arrays50 xpFinding the array length50 xpArray operating fluency100 xpOperating on body temperatures100 xp - 3
Functions and packages
Learn to write code that can make its own decisions using conditional expressions and write functions so you can reuse your code. Plus, learn Julia's powerful function features like broadcasting and multiple dispatch.
Conditionals50 xpComparisons100 xpCheck input data type100 xpIf-else practice100 xpConditioning on body temperature100 xpBasic Functions50 xpWriting a function for strings100 xpWriting a function with multiple arguments100 xpAbsolute value100 xpMutating functions and multiple dispatch50 xpModifying arrays100 xpEveryone wins100 xpMultiple dispatch100 xpUsing packages50 xpImporting packages100 xpUsing the Statistics package100 xp - 4
DataFrames
The DataFrames package is the definitive way to work with tabular data in Julia. You'll use this package to load CSV files and analyze and process this data to get the insights you need.
DataFrames50 xpLoading and examining data100 xpCreating a DataFrame100 xpDataFrame properties100 xpSorting and slicing data50 xpIndexing DataFrames100 xpSlicing DataFrames100 xpSorting patients100 xpDescriptive statistics50 xpLiterary analysis100 xpDescribing patient data100 xpStandardize heart rate100 xpFiltering50 xpConstructing filters50 xpFiltered body temp100 xpClassic books100 xpFinal thoughts50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Julia Fundamentals
Go To TrackJames Fulton
See MoreClimate Informatics Researcher
James is a PhD researcher at the University of Edinburgh, where he tutors computing, machine learning, data analysis, and statistical physics. His research involves using and developing machine learning algorithms to extract space-time patterns from climate records and climate models. He has held visiting researcher roles, working on planet-scale data analysis and modeling, at the University of Oxford and Queen's University Belfast and has a masters in physics where he specialized in quantum simulation. In a previous life, he was employed as a data scientist in the insurance sector. When not several indents deep in Python, he performs improvised comedy.
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