The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R

Colleen M. Farrelly and Yaé Ulrich Gaba  ·  2024

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 ·  TDA   geometry  ·  mathematics   data-science   machine-learning   topology

The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R cover
Cover of The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R

The Shape of Data is a practical guide to geometry-based machine learning and data analysis. Co-authored with Colleen M. Farrelly and published by No Starch Press, the book introduces readers to the powerful geometric and topological tools reshaping modern data science.

What you’ll learn

  • Topological Data Analysis (TDA): Persistent homology, persistence diagrams, Betti numbers, and the Mapper algorithm
  • Metric Geometry: Distance-based methods, embeddings, and curvature features for ML
  • Network Science: Graph representations, community detection, and centrality measures
  • Practical R Code: Hands-on implementations using real-world datasets

Who this book is for

Whether you’re a data scientist looking to expand your toolkit, a mathematician exploring applications, or a student at the intersection of topology and data — this book bridges the gap between abstract mathematical concepts and practical data analysis.

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