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 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’s written by Bogumił Kamiński, 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.
about the technology
Julia is a huge step forward for data science and scientific computing. It’s 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’s also an awesome general purpose programming language.
about the book
Julia for Data Analysis
teaches you how to read and write data in different formats, transform data, analyze data, and create insightful visualizations using the Julia language. The book guides you through the most important features of Julia using plenty of hands-on examples, including analyzing currency exchange, time series data, ranking the popularity of chess puzzles, and more. You’ll explore every step of the data science process, from acquiring and cleaning data right through to analysis. When you’re finished, you’ll be a confident Julia programmer ready to apply what you know.
about the reader
For data scientists familiar with Python or R. No experience with Julia required.
about the author
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.