To get the most out of this book
It is assumed that you have a basic understanding of writing code in at least one of C++, Python, or Go to benefit from and use the code snippets. You should know how to compile and run code in the desired language. Some familiarity with basic concepts of data analysis will help you get the most out of the scenarios and use cases explained in this book. Beyond this, concepts such as tabular data and installing software on your machine are assumed to be understood rather than explained.
|
Software/hardware covered in the book |
Operating system requirements |
|
An internet-connected computer |
|
|
Git |
Windows, macOS, or Linux |
|
C++ compiler capable of C++17 or higher |
Windows, macOS, or Linux |
|
Python 3.8 or higher |
Windows, macOS, or Linux |
|
conda/mamba (optional) |
Windows, macOS, or Linux |
|
vcpkg (optional) |
Windows |
|
MSYS2 (optional) |
Windows |
|
CMake 3.16 or higher |
Windows, macOS, or Linux |
|
make or ninja |
macOS or Linux |
|
Docker |
Windows, macOS, or Linux |
|
Go 1.21 or higher |
Windows, macOS, or Linux |
The sample data is in the book’s GitHub repository. You’ll need to use Git Large File Storage (LFS) or a browser to download the large data files. There are also a couple of larger sample data files in publicly accessible AWS S3 buckets. The book will provide a link to download the files when necessary. Code examples are provided in C++, Python, and Go.
If you are using the digital version of this book, we advise you to the complete code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.
Take your time, enjoy, and experiment in all kinds of ways, and please, have fun with the exercises!
