To get the most out of this book

We provide Python scripts and assume some knowledge of basic Python analytics packages (such as NumPy and scikit-learn) and Python syntax. We assume some knowledge of basic analytics tasks such as summary statistics and working with different types of data in Python with either numpy or pandas. Scripts are written for each chapter, with later scripts often depending on earlier scripts in the chapter to build knowledge. Other concepts in Python and in analytics will be introduced conceptually and then with Python code examples.

Software/hardware covered in the book	Operating system requirements
Python 3.12.3	Windows, macOS, or Linux

If you are using the digital version of this book, we advise you to type the code yourself or access the 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.

You are encouraged to try out the code examples in this book on your real-world data science projects. If you want to delve deeper into graph algorithms and network science, we encourage you to look at the latest research papers on network science topics. Google Scholar and arXiv are two good references for network science methods and application papers.