Multivariate Statistics and Machine Learning in R for Beginners: With Applications in Biology and Medicine - Couverture rigide

Tilevik, Andreas

Couverture rigide

ISBN 10 : 3032018501 ISBN 13 : 9783032018502

Editeur : Springer Nature Switzerland AG, 2026

Synopsis

This book is more than just a book – it is a full course designed as an interactive guide for beginners in multivariate analysis. Combining theoretical videos with practical examples in R, it offers readers a unique blend of theory, practice, and application in biology and medicine. In an era where data-driven insights shape every field, mastering multivariate statistics and machine learning techniques has never been more essential. Each chapter links directly to videos, which explain the theoretical foundations of the statistical or machine learning methods in a basic way. Following each video, readers will find R code that replicates the analyses presented in the videos, empowering them to see real-world applications in action. Many exercises are included, allowing the readers to test their understanding of each concept through hands-on practice. The book covers a comprehensive range of essential topics in multivariate statistics and machine learning, including fundamentals of matrix operations, multivariate plotting, and correlation, as well as methods for multivariate data analysis such as multivariate analysis of variance (MANOVA), principal component analysis (PCA), clustering, decision trees, discriminant analysis, random forest, partial least squares (PLS), canonical correlation analysis (CCA) and survival analysis. It also includes two case studies that reproduce the multivariate analyses in two scientific papers related to drug discovery and biomarker identification. By integrating videos with practical coding examples, this text makes complex topics accessible for beginners. The interactive learning approach ensures that readers not only grasp the statistical theories and machine learning concepts but also gain the confidence to apply them effectively in real-world scenarios.

A propos du livre

À propos de l'auteur

Andreas Tilevik is an associate professor in Systems Biology at the University of Skövde, Sweden. He has more than 15 years of teaching and research experience in data analysis and machine learning. Andreas holds a PhD from the University of New South Wales, Australia, in computational biology and has a bachelor's degree in statistics from Karlstad University, Sweden. He is also the creator of the YouTube channel TileStats, which includes more than 100 videos in statistics and machine learning.

À propos de la quatrième de couverture

This book is more than just a book – it is a full course designed as an interactive guide for beginners in multivariate analysis. Combining theoretical videos with practical examples in R, it offers readers a unique blend of theory, practice, and application in biology and medicine. In an era where data-driven insights shape every field, mastering multivariate statistics and machine learning techniques has never been more essential.
Each chapter links directly to videos, which explain the theoretical foundations of the statistical or machine learning methods in a basic way. Following each video, readers will find R code that replicates the analyses presented in the videos, empowering them to see real-world applications in action. Many exercises are included, allowing the readers to test their understanding of each concept through hands-on practice. 
The book covers a comprehensive range of essential topics in multivariate statistics and machine learning, including fundamentals of matrix operations, multivariate plotting, and correlation, as well as methods for multivariate data analysis such as multivariate analysis of variance (MANOVA), principal component analysis (PCA), clustering, decision trees, discriminant analysis, random forest, partial least squares (PLS), canonical correlation analysis (CCA) and survival analysis. It also includes two case studies that reproduce the multivariate analyses in two scientific papers related to drug discovery and biomarker identification.
By integrating videos with practical coding examples, this text makes complex topics accessible for beginners. The interactive learning approach ensures that readers not only grasp the statistical theories and machine learning concepts but also gain the confidence to apply them effectively in real-world scenarios.

Détails de l'édition

• Éditeur: Springer Nature Switzerland AG
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 3032018501
• ISBN 13: 9783032018502
• Reliure: Relié
• Nombre de pages: 416
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne