Julia for Data Analysis

Auteur

Bogumi Kamiski is 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 for corporate customers. He has been teaching data science at the undergraduate and graduate levels for two decades.

Texte du rabat

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 you'll master essential data analysis skills through engaging examples. Along the way, you'll learn to easily transfer existing data pipelines to Julia.

Résumé

Master core data analysis skills using Julia.

Julia for Data Analysis is a fascinating, hands-on projects guide you through time series data, predictive models, popularity ranking, and more.

With this book, you will learn how to:

• Read and write data in various formats
• Work with tabular data, including subsetting, grouping, and transforming
• Visualise your data using plots
• Perform statistical analysis
• Build predictive models
• Create complex data processing pipelines
  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 is written by Bogumi Kamiski, a top contributor to Julia, #1 Julia answerer on StackOverflow, and a lead developer of Julia's core data package DataFrames.jl.

You will learn how to write production-quality code in Julia, and utilize Julia's core features for data gathering, visualisation, and working with data frames. Plus, the engaging hands-on projects get you into the action quickly.

About the technology

Julia is a huge step forward for data science and scientific computing. It is a powerful high-performance programming language with many developer-friendly features like garbage collection, dynamic typing, just-in-time compilation, and a flexible approach to concurrent, parallel, and distributed computing. Although Julia's strong numerical programming features make it a favorite of data scientists, it is also an awesome general purpose programming language.

About the reader

For data scientists familiar with Python or R. No experience with Julia required.