Julia Cookbook

Over 40 recipes to get you up and running with programming using Julia

Julia Cookbook

Jalem Raj Rohit

Over 40 recipes to get you up and running with programming using Julia
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Book Details

ISBN 139781785882012
Paperback172 pages

Book Description

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation.

Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform.

This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.

Table of Contents

Chapter 1: Extracting and Handling Data
Introduction
Why should we use Julia for data science?
Handling data with CSV files
Handling data with TSV files
Working with databases in Julia
Interacting with the Web
Chapter 2: Metaprogramming
Introduction
Representation of a Julia program
Symbols and expressions
Symbols
Quoting
Interpolation
The Eval function
Macros
Metaprogramming with DataFrames
Chapter 3: Statistics with Julia
Introduction
Basic statistics concepts
Descriptive statistics
Deviation metrics
Sampling
Correlation analysis
Chapter 4: Building Data Science Models
Introduction
Dimensionality reduction
Linear discriminant analysis
Data preprocessing
Linear regression
Classification
Performance evaluation and model selection
Cross validation
Distances
Distributions
Time series analysis
Chapter 5: Working with Visualizations
Introduction
Plotting basic arrays
Plotting dataframes
Plotting functions
Exploratory data analytics through plots
Line plots
Scatter plots
Histograms
Aesthetic customizations
Chapter 6: Parallel Computing
Introduction
Basic concepts of parallel computing
Data movement
Parallel maps and loop operations
Channels

What You Will Learn

  • Extract and handle your data with Julia
  • Uncover the concepts of metaprogramming in Julia
  • Conduct statistical analysis with StatsBase.jl and Distributions.jl
  • Build your data science models
  • Find out how to visualize your data with Gadfly
  • Explore big data concepts in Julia

Authors

Table of Contents

Chapter 1: Extracting and Handling Data
Introduction
Why should we use Julia for data science?
Handling data with CSV files
Handling data with TSV files
Working with databases in Julia
Interacting with the Web
Chapter 2: Metaprogramming
Introduction
Representation of a Julia program
Symbols and expressions
Symbols
Quoting
Interpolation
The Eval function
Macros
Metaprogramming with DataFrames
Chapter 3: Statistics with Julia
Introduction
Basic statistics concepts
Descriptive statistics
Deviation metrics
Sampling
Correlation analysis
Chapter 4: Building Data Science Models
Introduction
Dimensionality reduction
Linear discriminant analysis
Data preprocessing
Linear regression
Classification
Performance evaluation and model selection
Cross validation
Distances
Distributions
Time series analysis
Chapter 5: Working with Visualizations
Introduction
Plotting basic arrays
Plotting dataframes
Plotting functions
Exploratory data analytics through plots
Line plots
Scatter plots
Histograms
Aesthetic customizations
Chapter 6: Parallel Computing
Introduction
Basic concepts of parallel computing
Data movement
Parallel maps and loop operations
Channels

Book Details

ISBN 139781785882012
Paperback172 pages
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