Julia for Data Science
- 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
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.
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
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.
Table of Contents
- The Groundwork – Julia's Environment
- Data Munging
- Data Exploration
- Deep Dive into Inferential Statistics
- Making Sense of Data Using Visualization
- Supervised Machine Learning
- Unsupervised Machine Learning
- Creating Ensemble Models
- Time Series
- Collaborative Filtering and Recommendation System
- Introduction to Deep Learning