Julia for Data Analysis
Julia for Data Analysis
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內容簡介
Master core data analysis skills using Julia. Interesting hands-on projects guide you through time series data, predictive models, popularity ranking, and more. In Julia for Data Analysis you will learn how to: Read and write data in various formats
Work with tabular data, including subsetting, grouping, and transforming
Visualize your data
Build predictive models
Create data processing pipelines
Create web services sharing results of data analysis
Write readable and efficient Julia programs Julia was designed for the unique needs of data scientists: it's expressive and easy-to-use whilst also delivering super-fast code execution. Julia for Data Analysis shows you how to take full advantage of this amazing language to read, write, transform, analyze, and visualize data--everything you need for an effective data pipeline. It's written by Bogumil Kaminski, one of the top contributors to Julia, #1 Julia answerer on StackOverflow, and a lead developer of Julia's core data package DataFrames.jl. Its engaging hands-on projects get you into the action quickly. Plus, you'll even be able to turn your new Julia skills to general purpose programming! Foreword by Viral Shah. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
Julia is a great language for data analysis. It's easy to learn, fast, and it works well for everything from one-off calculations to full-on data processing pipelines. Whether you're looking for a better way to crunch everyday business data or you're just starting your data science journey, learning Julia will give you a valuable skill. About the book
Julia for Data Analysis teaches you how to handle core data analysis tasks with the Julia programming language. You'll start by reviewing language fundamentals as you practice techniques for data transformation, visualizations, and more. Then, you'll master essential data analysis skills through engaging examples like examining currency exchange, interpreting time series data, and even exploring chess puzzles. Along the way, you'll learn to easily transfer existing data pipelines to Julia.
What's inside Read and write data in various formats
Work with tabular data, including subsetting, grouping, and transforming
Create data processing pipelines
Create web services sharing results of data analysis
Write readable and efficient Julia programs About the reader
For data scientists familiar with Python or R. No experience with Julia required. About the author
Bogumil Kaminski iis one of the lead developers of DataFrames.jl--the core package for data manipulation in the Julia ecosystem. He has over 20 years of experience delivering data science projects. Table of Contents
1 Introduction
PART 1 ESSENTIAL JULIA SKILLS
2 Getting started with Julia
3 Julia's support for scaling projects
4 Working with collections in Julia
5 Advanced topics on handling collections
6 Working with strings
7 Handling time-series data and missing values
PART 2 TOOLBOX FOR DATA ANALYSIS
8 First steps with data frames
9 Getting data from a data frame
10 Creating data frame objects
11 Converting and grouping data frames
12 Mutating and transforming data frames
13 Advanced transformations of data frames
14 Creating web services for sharing data analysis results
Work with tabular data, including subsetting, grouping, and transforming
Visualize your data
Build predictive models
Create data processing pipelines
Create web services sharing results of data analysis
Write readable and efficient Julia programs Julia was designed for the unique needs of data scientists: it's expressive and easy-to-use whilst also delivering super-fast code execution. Julia for Data Analysis shows you how to take full advantage of this amazing language to read, write, transform, analyze, and visualize data--everything you need for an effective data pipeline. It's written by Bogumil Kaminski, one of the top contributors to Julia, #1 Julia answerer on StackOverflow, and a lead developer of Julia's core data package DataFrames.jl. Its engaging hands-on projects get you into the action quickly. Plus, you'll even be able to turn your new Julia skills to general purpose programming! Foreword by Viral Shah. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
Julia is a great language for data analysis. It's easy to learn, fast, and it works well for everything from one-off calculations to full-on data processing pipelines. Whether you're looking for a better way to crunch everyday business data or you're just starting your data science journey, learning Julia will give you a valuable skill. About the book
Julia for Data Analysis teaches you how to handle core data analysis tasks with the Julia programming language. You'll start by reviewing language fundamentals as you practice techniques for data transformation, visualizations, and more. Then, you'll master essential data analysis skills through engaging examples like examining currency exchange, interpreting time series data, and even exploring chess puzzles. Along the way, you'll learn to easily transfer existing data pipelines to Julia.
What's inside Read and write data in various formats
Work with tabular data, including subsetting, grouping, and transforming
Create data processing pipelines
Create web services sharing results of data analysis
Write readable and efficient Julia programs About the reader
For data scientists familiar with Python or R. No experience with Julia required. About the author
Bogumil Kaminski iis one of the lead developers of DataFrames.jl--the core package for data manipulation in the Julia ecosystem. He has over 20 years of experience delivering data science projects. Table of Contents
1 Introduction
PART 1 ESSENTIAL JULIA SKILLS
2 Getting started with Julia
3 Julia's support for scaling projects
4 Working with collections in Julia
5 Advanced topics on handling collections
6 Working with strings
7 Handling time-series data and missing values
PART 2 TOOLBOX FOR DATA ANALYSIS
8 First steps with data frames
9 Getting data from a data frame
10 Creating data frame objects
11 Converting and grouping data frames
12 Mutating and transforming data frames
13 Advanced transformations of data frames
14 Creating web services for sharing data analysis results
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