#### Overview of this book

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Julia Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Extracting and Handling Data
Metaprogramming
Statistics with Julia
Building Data Science Models
Working with Visualizations
Parallel Computing

## Scatter plots

Scatter plots are the most basic plots in exploratory analytics. They help the analyst get a rough idea of the data distribution and the relationship between the corresponding columns, which in turn helps identify some prominent patterns in the data.

We will use the `Gadfly` library, which we used in the preceding recipes. So, to install the library, you can follow the installation steps mentioned in the previous recipes.
1. Let's start off with plotting a simple scatter plot of iris features: the length and the width. This will help us identify the relationship between the two features of the flower. This can be done using a line plot similar to the one in the preceding recipe, but including the aesthetic `Geom.point` instead of `Geom.line` in the `plot()` function. This can be done as follows:
```plot(dataset("datasets", "iris"), x = "SepalLength", y = "SepalWidth", Geom.point)