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A Review of Veridical Data Science by Bin Yu and Rebecca L. Barter

Full article forthcoming.
Published onApr 30, 2024
A Review of Veridical Data Science by Bin Yu and Rebecca L. Barter
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You're viewing an older Release (#1) of this Pub.

  • This Release (#1) was created on Apr 30, 2024 ()
  • The latest Release (#2) was created on Jun 10, 2024 ().

Editor-in-Chief’s Note: In this inaugural book review for Harvard Data Science Review, Yuval Benjamini and Yoav Benjamini provide a succinct summary and insightful reflection on Veridical Data Science by Bin Yu and Rebecca Barter (2024). The core premise of Veridical Data Science (VDS) is that data science results and findings must be demonstrably trustworthy to offer viable solutions to real-world problems. The book is founded on the PCS principles—predictability, computability, and stability—articulated by Bin Yu and her team in recent years. While predictability and computability are frequently emphasized in data science practice and theory, the book uniquely stresses the importance of stability as an integral and routine aspect of data science practice. The Benjamini duo discuss the potential uses and prospective readers of the book, concluding that its pedagogical excellence, diverse examples, and projects make Veridical Data Science a suitable textbook for students of all levels, in addition to being a valuable resource for data scientists in general. They also suggest content for a possible second volume, such as general design principles for stability that go beyond traditional robust designs.


Full article forthcoming.


©2024 Yuval Benjamini and Yoav Benjamini. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.

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