Book Image

Julia for Data Science

By : Anshul Joshi
2 (1)
Book Image

Julia for Data Science

2 (1)
By: Anshul Joshi

Overview of this book

Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.
Table of Contents (17 chapters)
Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Understanding the normal distribution


The normal distribution is the core of inferential statistics. It is like a bell curve (also called a Gaussian curve). Most of the complex processes can be defined by the normal distribution.

Let's see what a normal distribution looks like. First, we will import the necessary packages. We are including RDatasets now, but will be using it later:

We first set the seed and then explore the normal function:

As per the warning given, we can also use fieldnames instead of names. It is recommended to use fieldnames only from the newer versions of Julia.

Here, we can see that the Normal function is in the Distributions package and has the features Univariate and Continuous. The constructor of the normal() function accepts two parameters:

  • Mean (μ)

  • Standard deviation (σ)

Let's instantiate a normal distribution. We will keep the mean (μ) as 1.0 and the standard deviation (σ) as 3.0:

We can check the mean and standard deviation that we have kept:

Using this normal...