Julia for Data Analysis
- Paperback: 472 pages
- Publisher: WOW! eBook (January 10, 2023)
- Language: English
- ISBN-10: 1633439364
- ISBN-13: 978-1633439368
Julia for Data Analysis: 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 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 Bogumił Kamiński, 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!
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.
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 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.