Master the intricacies of data science with "Dimensionality Reduction in Data Science," a comprehensive guide for practitioners. This hardcover book delves into practical, state-of-the-art techniques for reducing dimensionality, enabling you to derive more valuable insights from large datasets across various scientific domains. Learn to tackle real-world problems with hands-on methods from statistics, computer science, and mathematics. The book covers essential steps from problem definition and data cleansing to feature selection and advanced reduction techniques. It provides quantitative and qualitative assessment methods for validating solutions in practice. Targeted at professionals with a quantitative science background, this first edition offers clear explanations, motivating examples, and in-depth analysis to compare and contrast different approaches. Unlock the potential of your data and solve complex problems more efficiently. Key topics include: * Data Science fundamentals through dimensionality reduction * Practical, hands-on techniques for real-world data * Methods from statistics, computer science, and mathematics * Data cleansing, feature selection, and extraction * Statistical, geometric, information-theoretic, biomolecular, and machine learning approaches * Comparative assessment of solutions Summary: Dimensionality Reduction in Data Science by Max Garzon, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana, Lih-Yuan Deng. Published by Springer Cham, this English hardcover book is the first edition, containing 265 pages and 62 b/w illustrations. ISBN: 9783031053702.