Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt

Learning about Julia's type system

Our game works like a charm, but there is one thing we can improve—storing our article info as a Dict. Julia's dictionaries are very flexible and powerful, but they are not a good fit in every case. The Dict is a generic data structure that is optimized for search, delete, and insert operations. None of these are needed here—our articles have a fixed structure and contain data that doesn't change once created. It's a perfect use case for objects and object-oriented programming (OOP). Looks like it's time to learn about types.

Julia's type system is the bread and butter of the language—it is all-pervasive, defining the language's syntax and being the driving force behind Julia's performance and flexibility. Julia's type system is dynamic, meaning that nothing is known about types until runtime, when the actual values manipulated by the program are available. However, we can benefit from the advantages of static typing by using type annotations—indicating...