Professional reviewers and academics emphasize the book's blend of theory and "common sense".
: It features a user-friendly version of text mining that does not require an advanced background in natural language processing (NLP). Critical Perspectives and Expert Reviews
: New content covers emerging and niche topics, including the rise of data science, market share estimation , and share of wallet modeling without survey data. Statistical and Machine-Learning Data Mining, T...
: Every chapter from the previous edition was rewritten to incorporate recent methodologies in statistical modeling and big data analytics.
The book focuses heavily on techniques that start where traditional statistical data mining stops, such as the patented . Notable topics include: : Every chapter from the previous edition was
: Reviewers from Technometrics note the book is well-written with numerous worked examples based on real-life datasets.
: The book includes SAS subroutines that can be converted to other programming languages, making it highly applicable for practitioners. : The book includes SAS subroutines that can
: Some critics have noted a limited literature review and a lack of dedicated exercise sections for students. Others suggest that further discussion on high-dimensional data analysis would add value. Core Content & Methodologies