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

Exploratory data analysis with Julia

Now that you have a good understanding of Julia's basics, we can apply this knowledge to our first project. We'll start by applying exploratory data analysis (EDA) to the Iris flower dataset.

If you already have experience with data analysis, you might've used the Iris dataset before. If so, that's great! You'll be familiar with the data and the way things are done in your (previous) language of choice, and can now focus on the Julia way.

On the contrary, if this is the first time you've  heard about the Iris flower dataset, no need to worry. This dataset is considered the Hello World of data science—and we'll take a good look at it using Julia's powerful toolbox. Enjoy!

The Iris flower dataset

Also called Fisher's Iris dataset, it was first introduced in 1936 by British statistician and biologist Ronald Fisher. The dataset consists of 50 samples from each of three species of Iris flower (Iris setosa, Iris virginica, and Iris versicolor). It is sometimes...