Название: Julia for Data Analysis (Final Release)Автор: Bogumil Kaminski
Издательство: Manning Publications
Год: 2023
Страниц: 474
Язык: английский
Формат: pdf (true)
Размер: 13.4 MB
Master core data analysis skills using Julia. Interesting hands-on projects guide you through time series data, predictive models, popularity ranking, and more.
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 teaches you how to perform core Data Science tasks with this amazing language. It’s written by Bogumil Kaminski, a top contributor to Julia, #1 Julia answerer on StackOverflow, and a lead developer of Julia’s core data package DataFrames.jl. You’ll learn how to write production-quality code in Julia, and utilize Julia’s core features for data gathering, visualization, and working with data frames. Plus, the engaging hands-on projects get you into the action quickly.
The book is divided into two parts. The first part introduces the basic concepts of the Julia language, introducing the type system, multiple dispatch, data structures, etc. The second part then builds on these concepts and presents data analysis—reading data, selecting, creating a DataFrame, split-apply-combine, sorting, joining, and reshaping—and finally finishes with a complete application. There is also a discussion of the Arrow data exchange format that allows Julia programs to co-exist with data analysis tools in R, Python, and Spark, to mention a few. The code patterns in all the chapters teach the reader good practices that result in high-performance data analysis.
This book does not require prior knowledge of advanced Machine Learning (ML) algorithms. This knowledge is not necessary for understanding the fundamentals of data analysis in Julia, and I do not discuss such models in this book. I do assume that you have knowledge of basic data science tools and techniques such as generalized linear regression or LOESS regression. Similarly, from a data engineering perspective, I cover the most common operations, including fetching data from the web, writing a web service, working with compressed files, and using basic data storage formats. I left out functionalities that require either additional complex configuration that is not Julia related or specialist software engineering knowledge.
Appendix C reviews the Julia packages that provide advanced functionalities in the data engineering and data science domains. Using the knowledge you glean from this book, you should be able to confidently learn to use these packages on your own.
Who should read this book:
This book is for data scientists or data engineers who would like to learn how Julia can be used for data analysis. I assume that you have some experience in doing data analysis using a programming language such as R, Python, or MATLAB.
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