The best books of 2024

This list is part of the best books of 2024.

Join 1,889 readers and share your 3 favorite reads of the year.

My favorite read in 2024

Book cover of Basketball Data Science: With Applications in R

Christophe Ley ❤️ loved this book because...

It is THE book for basketball analytics, very inspiring and agreeable to read.

  • Loved Most

    🥇 Teach 🥈 Thoughts
  • Writing style

    👍 Liked it
  • Pace

    🐕 Good, steady pace

By Paola Zuccolotto, Marica Manisera,

Why should I read it?

1 author picked Basketball Data Science as one of their favorite books, and they share why you should read it.

What is this book about?

Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player's shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable…


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My 2nd favorite read in 2024

Book cover of Computer Age Statistical Inference, Algorithms, Evidence, and Data Science

Christophe Ley 👍 liked this book because...

This is a book for (aspiring and established) data scientists, and it has been the best thematic book I held in hands for years! Huge congrats to the authors for putting the material together in such an agreeable and intriguing-to-read form!

  • Loved Most

    🥇 Teach 🥈 Writing
  • Writing style

    👍 Liked it
  • Pace

    🐕 Good, steady pace

By Bradley Efron, Trevor Hastie,

Why should I read it?

3 authors picked Computer Age Statistical Inference, Algorithms, Evidence, and Data Science as one of their favorite books, and they share why you should read it.

What is this book about?

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic…


My 3rd favorite read in 2024

Book cover of Interpretable Machine Learning: A Guide For Making Black Box Models Explainable

Christophe Ley ❤️ loved this book because...

It describes a very hot topic, interpretable machine learning, in a very enjoyable way. It served me as a great basis for a part of a course I am teaching.

  • Loved Most

    🥇 Teach 🥈 Thoughts
  • Writing style

    ❤️ Loved it
  • Pace

    🐕 Good, steady pace

By Christoph Molnar,

Why should I read it?

1 author picked Interpretable Machine Learning as one of their favorite books, and they share why you should read it.

What is this book about?

Interpretable Machine Learning is a comprehensive guide to making machine learning models interpretable

"Pretty convinced this is the best book out there on the subject"
– Brian Lewis, Data Scientist at Cornerstone Research

Summary

This book covers a range of interpretability methods, from inherently interpretable models to methods that can make any model interpretable, such as SHAP, LIME and permutation feature importance. It also includes interpretation methods specific to deep neural networks, and discusses why interpretability is important in machine learning. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are…


Don‘t forget about my book 😀

Modern Directional Statistics

By Christophe Ley, Thomas Verdebout,

Book cover of Modern Directional Statistics

What is my book about?

The field of directional statistics has received a lot of attention over the past two decades, due to new demands from domains such as life sciences (related to the Nobel Prize in Chemistry 2024) or machine learning, to the availability of massive data sets requiring adapted statistical techniques, and to technological advances. This book covers important progresses in this burgeoning field of data science.

Book cover of Basketball Data Science: With Applications in R
Book cover of Computer Age Statistical Inference, Algorithms, Evidence, and Data Science
Book cover of Interpretable Machine Learning: A Guide For Making Black Box Models Explainable

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