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

Sampling


In the previous example, we spoke about calculating the mean height of 1,000 people out of the 10 million people living in New Delhi. While gathering the data of these 10 million people, let's say we started from a particular age or community, or in any sequential manner. Now, if we take 1,000 people who are consecutive in the dataset, there is a high probability that they would have similarities among them. This similarity would not give us the actual highlight of the dataset that we are trying to achieve. So, taking a small chunk of consecutive data points from the dataset wouldn't give us the insight that we want to gain. To overcome this, we use sampling.

Sampling is a technique to randomly select data from the given dataset such that they are not related to each other, and therefore we can generalize the results that we generate on this selected data over the complete dataset. Sampling is done over a population.

Population

A population in statistics refers to the set of all the...