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

Summary


In this chapter, we learned what data munging is and why it is necessary for data science. Julia provides functionalities to facilitate data munging with the DataFrames.jl package, with features such as these:

  • NA: A missing value in Julia is represented by a specific data type, NA.

  • DataArray: DataArray provided in the DataFrames.jl provides features such as allowing us to store some missing values in an array.

  • DataFrame: DataFrame is 2-D data structure like spreadsheets. It is very similar to R or pandas's dataframes, and provides many functionalities to represent and analyze data. DataFrames has many features well suited for data analysis and statistical modeling.

  • A dataset can have different types of data in different columns.

  • Records have a relation with other records in the same row of different columns of the same length.

  • Columns can be labeled. Labeling helps us to easily become familiar with the data and access it without the need to remember their numerical indices.

We learned...