Dimensionality Reduction in Data Science

The book "Dimensionality Reduction in Data Science" offers a comprehensive and practical examination of data science from the perspective of dimensionality reduction. It addresses various techniques for solving problems associated with real-world datasets. The authors, who are experts in the fields of statistics, computer science, and mathematics, explain modern findings and solutions that are relevant for professionals in various scientific disciplines, such as biology, cybersecurity, chemistry, and sports science. The book places particular emphasis on problem definition, data cleaning, feature selection and extraction, as well as statistical, geometric, information-theoretic, and machine learning methods for reducing the dimensions of large datasets. Additionally, quantitative and qualitative evaluation methods are presented to implement and validate solutions in the real world. It is aimed at professionals with a bachelor's degree in a scientific field, particularly in quantitative disciplines, and provides motivating examples to illustrate the methods discussed.