Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks

"Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks" is a comprehensive guide aimed at those looking to utilize Mathematica for data science. While Mathematica has been around for over 30 years and is not as popular as Python and R in the data science community, this book provides an in-depth exploration of its capabilities. One reviewer, who had previous experience with Mathematica, found the book to be approachable. They appreciated that all the codes described in the text could be downloaded. However, they did mention that the data files mentioned in the book were incomplete, making it difficult to fully understand that aspect. Another critique was that the layout of the book heavily focused on code, with execution examples often treated as diagrams. This may be suitable if running the sequential code in a Mathematica notebook, but it can be challenging for readers who prefer a more traditional text-based format. Additionally, there were concerns about typographical errors and code mistakes introduced from Chapter 8 onwards. Despite these criticisms, it was noted that the book does an excellent job of organizing the vast documentation available for Mathematica in an accessible manner. However, it was suggested that the editors could have taken more time to further enhance the organization of the content. Overall, "Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks" provides an opportunity to learn about Mathematica's capabilities in data science and machine learning, with a particular emphasis on neural networks.