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

A quick look at our data

In this chapter, we will use some real-life data provided by Eurostat, the official EU office for statistics. Eurostat has a wealth of databases available on its website. For our learning project, we'll take a look at the unemployment numbers—with the EU's economy growing after a long recession, these stats should be quite interesting. Various EU employment and unemployment figures can be downloaded from We'll be using the Unemployment by sex and age – monthly average dataset.

You don't need to download this because a better structured dataset is provided in this chapter's support files. However, if you're curious and want to take a look, you can get the raw data from under the Employment and unemployment (Labour force survey) category | LFS main indicators subcategory | Unemployment - LFS adjusted series folder.

I've also customized the data by using thousand persons for the unit of measure (the default is percentage...