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Key benefits

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

Description

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.

Who is this book 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

What do you get with a Packt Subscription?

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50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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RareComplexCollectionOfMatter Aug 17, 2020

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Deepankar A. Jan 25, 2017

The way the book is written is really amazing with various practical examples. This actually gives a good insights of how to use Julia with Data Science. The language is easy by comparing the complexity the book is dealing with so it is easy for starters to start with. It is a must read if one is adopting Julia as a language for Data Science.

Amazon Customer Dec 27, 2016

I would definitely recommend this book. I have worked on many projects in the past and have used Python,R and Scala, this book has added an entire new area for me to work on. It is well structured and was easy to go through, a good job by the author.

Amazon Customer Dec 22, 2016

Learning a new language is always a difficult task, but this book has covered all the crucial topics and thus makes learning very smooth and easy.

Rahul Dec 17, 2017

As someone who knew nothing about data science and machine learning models, this book proves to be a great asset for people looking out for serious and immersive content over the topic. The author explains each and every topic in detail and uses Julia code, which btw is something that every modern data scientist should be looking out for. Overall, this is a great book and I would highly recommend it.

Julia for Data Science: high-performance computing simplified

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