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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.
  1. 1

    Julia basics

    Free

    Take 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.

    Play Chapter Now
    Getting started with Julia
    50 xp
    Using the console
    50 xp
    Julia as a calculator
    100 xp
    Printing
    100 xp
    Variables
    50 xp
    Valid variable names
    100 xp
    Assigning variables
    100 xp
    Calculating with variables
    100 xp
    Basic data types
    50 xp
    Recognizing data types
    100 xp
    Finding the data type
    100 xp
    Converting types
    100 xp
  2. 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.

    Play Chapter Now
    Introduction to strings
    50 xp
    Indexing strings
    100 xp
    String interpolation
    100 xp
    Slicing strings
    100 xp
    Introduction to arrays
    50 xp
    Creating arrays
    100 xp
    Indexing arrays
    100 xp
    Slicing arrays
    100 xp
    Working with arrays
    50 xp
    Modifying arrays
    100 xp
    Fibonacci sequence
    100 xp
    Appending arrays
    100 xp
    Operating on arrays
    50 xp
    Finding the array length
    50 xp
    Array operating fluency
    100 xp
    Operating on body temperatures
    100 xp
  3. 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.

    Play Chapter Now
    Conditionals
    50 xp
    Comparisons
    100 xp
    Check input data type
    100 xp
    If-else practice
    100 xp
    Conditioning on body temperature
    100 xp
    Basic Functions
    50 xp
    Writing a function for strings
    100 xp
    Writing a function with multiple arguments
    100 xp
    Absolute value
    100 xp
    Mutating functions and multiple dispatch
    50 xp
    Modifying arrays
    100 xp
    Everyone wins
    100 xp
    Multiple dispatch
    100 xp
    Using packages
    50 xp
    Importing packages
    100 xp
    Using the Statistics package
    100 xp
  4. 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.

    Play Chapter Now
    DataFrames
    50 xp
    Loading and examining data
    100 xp
    Creating a DataFrame
    100 xp
    DataFrame properties
    100 xp
    Sorting and slicing data
    50 xp
    Indexing DataFrames
    100 xp
    Slicing DataFrames
    100 xp
    Sorting patients
    100 xp
    Descriptive statistics
    50 xp
    Literary analysis
    100 xp
    Describing patient data
    100 xp
    Standardize heart rate
    100 xp
    Filtering
    50 xp
    Constructing filters
    50 xp
    Filtered body temp
    100 xp
    Classic books
    100 xp
    Final thoughts
    50 xp

In the following tracks

Julia Fundamentals

Datasets

BooksPatients

Collaborators

Collaborator's avatar
Izzy Weber
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Arne Warnke
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Katerina Zahradova
James Fulton HeadshotJames Fulton

Climate 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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