| #2 |  Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making
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| #3 |  Strong Towns
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| #4 |  Escaping the Housing Trap: The Strong Towns Response to the Housing Crisis
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| #5 |  Probability Theory: The Logic of Science
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| #6 |  Interpretable Machine Learning Interpretable Machine Learning: A Guide For Making Black Box Models Explainable
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| #7 |  Modeling Mindsets: The Many Cultures Of Learning From Data
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| #8 |  Supervised Machine Learning for Science: How to stop worrying and love your black box
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| #9 |  An Introduction to Optimization on Smooth Manifolds
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| #10 |  Geometric Deep Learning Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
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| #11 |  Statistical Rethinking: A Bayesian Course with Examples in R and Stan
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| #12 |  Forming Humanity: Redeeming the German Bildung Tradition
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| #13 |  Structure and Interpretation of Computer Programs
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| #14 | Category Theory for Programmers Category Theory for Programmers
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| #15 |  Regression and Other Stories
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| #16 |  Bayesian Data Analysis
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| #17 |  Causal Inference and American Political Development: New Frontiers |
| #18 |  Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science
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| #19 |  Public Policy Analysis Public Policy Analysis: An Integrated Approach
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| #20 |  Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist
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| #21 |  Critical Data Studies: An A to Z Guide to Concepts and Methods
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| #22 |  Bullshit Jobs: A Theory
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| #23 |  Hegel’s Moral Corporation
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