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


Julia's intuitive syntax makes for a lean learning curve. The optional typing and the wealth of shorthand constructors result in readable, noise-free code, while the large collection of third-party packages makes accessing, manipulating, visualizing, plotting, and saving data a breeze.

Just by learning Julia's basic data structures and a few related functions, coupled with its powerful data manipulation toolset, we were able to implement an efficient data analysis workflow and extract valuable insight from the Iris flowers dataset. That was all we needed in order to perform efficient exploratory data analysis with Julia.

In the next chapter, we'll continue our journey by learning how to build a web crawler. Web mining, the process of extracting information from the web, is an important part of data mining and a key component of data acquisition in general. Julia is a great choice when building web mining software, given not only its built-in performance and its rapid prototyping features...