Julia for Data Science

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

Julia for Data Science

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Anshul Joshi

2 customer reviews
Explore the world of data science from scratch with Julia by your side
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Book Details

ISBN 139781785289699
Paperback346 pages

Book 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.

Table of Contents

Chapter 1: The Groundwork – Julia's Environment
Julia is different
Setting up the environment
Using REPL
Using Jupyter Notebook
Package management
Parallel computation using Julia
Julia's key feature – multiple dispatch
Facilitating language interoperability
Summary
References
Chapter 2: Data Munging
What is data munging?
What is a DataFrame?
Summary
References
Chapter 3: Data Exploration
Sampling
Inferring column types
Basic statistical summaries
Scalar statistics
Measures of variation
Scatter matrix and covariance
Computing deviations
Rankings
Counting functions
Histograms
Correlation analysis
Summary
References
Chapter 4: Deep Dive into Inferential Statistics
Installation
Understanding the sampling distribution
Understanding the normal distribution
Type hierarchy in Distributions.jl
Univariate distributions
Truncated distributions
Understanding multivariate distributions
Understanding matrixvariate distributions
Distribution fitting
Confidence interval
Understanding z-score
Understanding the significance of the P-value
Summary
References
Chapter 5: Making Sense of Data Using Visualization
Difference between using and importall
Pyplot for Julia
Unicode plots
Visualizing using Vega
Data visualization using Gadfly
Summary
References
Chapter 6: Supervised Machine Learning
What is machine learning?
Machine learning – the process
Understanding decision trees
Supervised learning using Naïve Bayes
Summary
References
Chapter 7: Unsupervised Machine Learning
Understanding clustering
K-means clustering
Summary
References
Chapter 8: Creating Ensemble Models
What is ensemble learning?
Random forests
Implementation in Julia
Why is ensemble learning superior?
Summary
References
Chapter 9: Time Series
What is forecasting?
What is TimeSeries?
Implementation in Julia
Summary
References
Chapter 10: Collaborative Filtering and Recommendation System
What is a recommendation system?
Association rule mining
Content-based filtering
Collaborative filtering
Building a movie recommender system
Summary
Chapter 11: Introduction to Deep Learning
Revisiting linear algebra
Probability and information theory
Differences between machine learning and deep learning
Implementation in Julia
Summary
References

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

Authors

Table of Contents

Chapter 1: The Groundwork – Julia's Environment
Julia is different
Setting up the environment
Using REPL
Using Jupyter Notebook
Package management
Parallel computation using Julia
Julia's key feature – multiple dispatch
Facilitating language interoperability
Summary
References
Chapter 2: Data Munging
What is data munging?
What is a DataFrame?
Summary
References
Chapter 3: Data Exploration
Sampling
Inferring column types
Basic statistical summaries
Scalar statistics
Measures of variation
Scatter matrix and covariance
Computing deviations
Rankings
Counting functions
Histograms
Correlation analysis
Summary
References
Chapter 4: Deep Dive into Inferential Statistics
Installation
Understanding the sampling distribution
Understanding the normal distribution
Type hierarchy in Distributions.jl
Univariate distributions
Truncated distributions
Understanding multivariate distributions
Understanding matrixvariate distributions
Distribution fitting
Confidence interval
Understanding z-score
Understanding the significance of the P-value
Summary
References
Chapter 5: Making Sense of Data Using Visualization
Difference between using and importall
Pyplot for Julia
Unicode plots
Visualizing using Vega
Data visualization using Gadfly
Summary
References
Chapter 6: Supervised Machine Learning
What is machine learning?
Machine learning – the process
Understanding decision trees
Supervised learning using Naïve Bayes
Summary
References
Chapter 7: Unsupervised Machine Learning
Understanding clustering
K-means clustering
Summary
References
Chapter 8: Creating Ensemble Models
What is ensemble learning?
Random forests
Implementation in Julia
Why is ensemble learning superior?
Summary
References
Chapter 9: Time Series
What is forecasting?
What is TimeSeries?
Implementation in Julia
Summary
References
Chapter 10: Collaborative Filtering and Recommendation System
What is a recommendation system?
Association rule mining
Content-based filtering
Collaborative filtering
Building a movie recommender system
Summary
Chapter 11: Introduction to Deep Learning
Revisiting linear algebra
Probability and information theory
Differences between machine learning and deep learning
Implementation in Julia
Summary
References

Book Details

ISBN 139781785289699
Paperback346 pages
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