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Dimensionality reduction in data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dimensionality reduction in data scienceedited by Max Garzon ... [et al.].
other author:	Garzon, Max.
Published:	Cham :Springer International Publishing :2022.
Description:	xi, 265 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Big data.
Online resource:	https://doi.org/10.1007/978-3-031-05371-9
ISBN:	9783031053719$q(electronic bk.)

Dimensionality reduction in data science
Dimensionality reduction in data science[electronic resource] /edited by Max Garzon ... [et al.]. - Cham :Springer International Publishing :2022. - xi, 265 p. :ill., digital ;24 cm.

1. What is Data Science (DS)? -- 2. Solutions to Data Science Problems -- 3. What is Dimensionality Reduction (DR)? -- 4. Conventional Statistical Approaches -- 5. Geometric Approaches -- 6. Information-theoretic Approaches -- 7. Molecular Computing Approaches -- 8. Statistical Learning Approaches -- 9. Machine Learning Approaches -- 10. Metaheuristics of DR Methods -- 11. Appendices.

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.

ISBN: 9783031053719$q(electronic bk.)

Standard No.: 10.1007/978-3-031-05371-9doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.D343 / D55 2022

Dewey Class. No.: 005.7

Dimensionality reduction in data science
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505 0 $a 1. What is Data Science (DS)? -- 2. Solutions to Data Science Problems -- 3. What is Dimensionality Reduction (DR)? -- 4. Conventional Statistical Approaches -- 5. Geometric Approaches -- 6. Information-theoretic Approaches -- 7. Molecular Computing Approaches -- 8. Statistical Learning Approaches -- 9. Machine Learning Approaches -- 10. Metaheuristics of DR Methods -- 11. Appendices.
520 $a 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.
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