Take your Julia skills to the next level with our intermediate Julia course. Learn about loops, advanced data structures, timing, and more.Start Course for Free
4 Hours15 Videos54 Exercises
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Julia is a relatively new programming language built with speed and performance in mind, and it can do this while maintaining a similar syntax to other programming languages such as Python or Ruby. This course follows the Introduction to Julia course, introducing topics such as looping and timing so that you can take advantage of Julia's speed and performance.
Build on Your Julia FoundationsBuilding on the core concepts of the introductory course, you will be one step closer to mastering Julia. You will first learn about different loops, one of the most common tools in Julia, and all programming languages.
Cover Advanced Julia Data StructuresYou'll also cover advanced data structures, including dictionaries, tuples, and structs. In addition, you will learn how to define your own Julia functions for code re-usability and how to time your code to be as efficient as possible. At the end of this course, you'll be able to work with more complex DataFrame operations to inspect and clean a global video game sales dataset broken down by region. You will also be able to leverage your Python and R knowledge in Julia as we introduce the PyCall and RCall packages, allowing you to use Python and R functions in Julia. By the time you finish, you'll have built a solid intermediate Julia programming foundation which you can continue to develop through further courses.
Loops and RangesFree
Loops are one of the core concepts that underpin Julia. In this chapter, you'll learn about for loops and while loops, and how to use them to iterate over data structures that you are already familiar with. You will also cover ranges, a useful tool for generating sequences of data.
This chapter focuses on expanding your knowledge of the data structures available in Julia. Learn how to use tuples, dictionaries, multi-dimensional arrays, and structures to store and traverse data quickly and efficiently.Tuples50 xpWhen should you use a tuple?100 xpCreate, index, and slice a tuple100 xpCreate a NamedTuple for a person100 xpDictionaries50 xpCreate untyped dict, iterate100 xpCreate typed dict, iterate100 xpModify keys/values in dict, use get()100 xpMulti-dimensional arrays50 xpCreate 1D and 2D arrays100 xpIndex and slice a 2D array100 xpArray merging100 xpStructs50 xpDefine a structure100 xpMutable and typed structs100 xp
Advanced Functions in Julia
In this chapter, you’ll extend your understanding of functions, exploring positional, keyword, and default function arguments. You will also cover code execution timing, getting a strong understanding of how to measure the time your code takes to run. This chapter will round off with a capstone on writing your own functions to solve real-world problems.Execution time and measurement50 xpBuilt-in time macro50 xpTiming a function100 xpPositional and Default Arguments, Type Declarations50 xpPositional arguments recap100 xpDefault arguments100 xpType declarations100 xpKeyword Arguments50 xpKeyword arguments100 xpVariable number of arguments100 xpWriting Your Own Function50 xpWriting your own functions100 xpWriting your own functions - structs100 xp
Dataframe Operations and Python/R Packages in Julia
This final chapter will introduce anonymous functions and will recap one of the powerful features of Julia; multiple dispatch. You will learn how to use functions from Python and R packages within Julia and discover how to clean and modify data within dataframes.Anonymous Functions and Multiple Dispatch50 xpMultiple dispatch100 xpAnonymous functions100 xpFiltering DataFrames100 xpImporting Functions from Python and R50 xpImporting Python and R100 xpPython functions in Julia100 xpR functions in Julia100 xpCleaning Data50 xpRenaming columns100 xpMissing data100 xpAdvanced missing data100 xpCongratulations!50 xp
PrerequisitesIntroduction to Julia
Anthony MarkhamSee More
Anthony is a Quant Developer in Algorithmic Trading and Risk Management in Sydney, Australia. With a background in Aerospace and Software Engineering, Anthony has experience in Data Science, facial recognition research, tertiary education, and Quantitative Finance, developing mostly in Python, Julia, and C++. When not working, Anthony enjoys working on personal projects, flying aircraft, and playing sport.
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