Dimensionality Reduction in Data Science

Name: Dimensionality Reduction in Data Science
Brand: Springer International Publishing AG
SKU: 9783031053702
Price: 9900 KES
Availability: InStock
ISBN: 9783031053702

2022 ed.

Book Details

Format Hardback

ISBN-10 3031053702

ISBN-13 9783031053702

Edition 2022 ed.

Publisher Springer International Publishing AG

Imprint Springer International Publishing AG

Country of Manufacture GB

Country of Publication GB

Publication Date Jul 29th, 2022

Print length 265 Pages

Weight 572 grams

Dimensions 16.40 x 24.20 x 2.20 cms

Product Classification: Biomedical engineering Biomedical engineering / Medical engineering Data mining Expert systems / knowledge-based systems Machine learning

KSh 9,900.00

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This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.

The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.

This book focuses on data science and problem definition, data cleansing, feature selection and extraction, statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.

This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.

Get Dimensionality Reduction in Data Science by Ching-Chi Yang at the best price and quality guaranteed only at Werezi Africa's largest book ecommerce store. The book was published by Springer International Publishing AG and it has 265 pages.