Julia for Data Science

Anshul Joshi

• Publisher: Packt Publishing
• Year: 2016
• ISBN:
  9781785289699
• Edition: 1
• Topic: Computer Science
• Subtopic: Programming Algorithms
• Index: Computer Science

Explore the world of data science from scratch with Julia by your side

About This Book

• An in-depth exploration of Julia's growing ecosystem of packages
• Work with the most powerful open-source libraries for deep learning, data wrangling, and data visualization
• Learn about deep learning using Mocha.jl and give speed and high performance to data analysis on large data sets

Who This Book Is For

This book is aimed at data analysts and aspiring data scientists who have a basic knowledge of Julia or are completely new to it. The book also appeals to those competent in R and Python and wish to adopt Julia to improve their skills set in Data Science. It would be beneficial if the readers have a good background in statistics and computational mathematics.

What You Will Learn

• Apply statistical models in Julia for data-driven decisions
• Understanding the process of data munging and data preparation using Julia
• Explore techniques to visualize data using Julia and D3 based packages
• Using Julia to create self-learning systems using cutting edge machine learning algorithms
• Create supervised and unsupervised machine learning systems using Julia. Also, explore ensemble models
• Build a recommendation engine in Julia
• Dive into Julia's deep learning framework and build a system using Mocha.jl

In Detail

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.

Style and approach

This practical and easy-to-follow yet comprehensive guide will get you learning about Julia with respect to data science. Each topic is explained thoroughly and placed in context. For the more inquisitive, we dive deeper into the language and its use case. This is the one true guide to working with Julia in data science.

Julia for Data Science

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Julia for Data Science

Copyright © 2016 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: September 2016

Production reference: 1260916

Published by Packt Publishing Ltd.

Livery Place

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Birmingham

B3 2PB, UK.

ISBN 978-1-78528-969-9

www.packtpub.com

Credits

Author Anshul Joshi	Copy Editor Safis Editing
Reviewer Sébastien Celles	Project Coordinator Nidhi Joshi
Commissioning Editor Akram Hussain	Proofreader Safis Editing
Acquisition Editor Sonali Vernekar	Indexer Mariammal Chettiyar
Content Development Editor Aishwarya Pandere	Graphics Disha Haria
Technical Editor Vivek Arora	Production Coordinator Arvindkumar Gupta

About the Author

Anshul Joshi is a data science professional with more than 2 years of experience primarily in data munging, recommendation systems, predictive modeling, and distributed computing. He is a deep learning and AI enthusiast. Most of the time, he can be caught exploring GitHub or trying anything new on which he can get his hands on. He blogs on anshuljoshi.xyz.

I'd like to thank my parents, who have been really supportive throughout, my professors, who helped me during my days at university and got me where I am, and my friends, who were very understanding. A big thanks to the Julia community. These people are amazing and are the rockstars of our generation.

I would also like to thank Packt Publishing and the editors for helping me throughout. A special thanks to Sébastien Celles; his expertise and reviews really helped me improve the book.

About the Reviewer

Sébastien Celles is a professor of applied physics at Poitiers Institute of Technology (Université de Poitiers—IUT de Poitiers—thermal science department). He teaches physics and computer sciences (data processing).

He has used Python for numerical simulations, data plotting, data predicting, and various other tasks since the early 2000s. He is a member of PyData and was granted commit rights to the pandas DataReader project. He is also involved in several open source projects about the scientific Python ecosystem.

He is also author of some Python packages available on PyPi:

• openweathermap_requests: A package to fetch data from http://openweathermap.org/ using requests and requests-cache and get pandas DataFrames with weather history
• pandas_degreedays: A package to calculate degree days (a measure of heating or cooling) from a pandas time series of temperature
• pandas_confusion: A package to manage confusion matrices, plot them, binarize them, calculate overall statistics, and class statistics

He made some contributions (unit testing, continuous integration, Python 3 port…) too:

• python-constraint: A Constraint Solving Problem (CSP) resolver for Python

He was a technical reviewer of Mastering Python for Data Science explores the world of data science through Python and learn how to make sense of data. Samir Madhavan. Birmingham, UK, Packt Publishing, August 2015.

Two years ago, he started to learn Julia, with which he has performed various tasks about data mining, machine learning, forecasting, and so he's a user of (and sometimes a contributor too) some Julia packages (DataStructures.jl, CSV.jl, DataFrames.jl, TimeSeries.jl, NDSparseData.jl, JuliaTS.jl, MLBase.jl, Mocha.jl, and so on)

He is also author of some Julia packages:

• Pushover.jl: A package to send notifications using the Pushover Notification Service
• BulkSMS.jl: A Julia package to send SMS (Short Message Service) using BulkSMS API
• DataReaders.jl: A package to get remote data via Requests.jl and get DataFrames thanks to DataFrames.jl
• RequestsCache.jl: A transparent persistent cache using the Requests.jl library to perform requests and using JLD.jl library as a storage backend
• PubSub.jl: A very basic implementation of the publish-subscribe pattern
• SignalSlot.jl: A very basic implementation of the signal-slot pattern
• TALib.jl: A Julia wrapper for TA-Lib (Technical Analysis Library)

He has a keen interest in open data and he is a contributor of some projects of the Open Knowledge Foundation (especially around the DataPackage format).

You can find more information about him at http://www.celles.net/wiki/Contact.

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