Original comment by /u/save_the_panda_bears
The Bible is technically a series of books that form a cohesive narrative. In that sense, here is my Bible of Data Science roughly divided into a classical stats OT and a more modern ML NT:
The Law - The mathematical foundations
Statistical Inference - Casella & Berger
History - Foundational works that provide additional context for more advanced concepts
Convex Optimization - Boyd & Vandenberghe
Probability Theory: The Logic of Science - Jaynes
Clean Code - Martin
Poetry - Prose type works
Major Prophets - Seminal works on major topics
Applied Regression Analysis - Draper & Smith
The Data Warehouse Toolkit - Kimball
Bayesian Data Analysis - Gelman
Forecasting: Principles and Practices - Hyndman & Athanasopoulos
Minor Prophets - Important works, but not quite at the level of the DS Major Prophets
Causal Inference for the Brave and True
Trustworthy Online Controlled Experiments
The Gospels - The fulfillment of the DS Law
Introduction to Statistical Learning
The Elements of Statistical Learning
Deep Learning - Goodfellow
History Pt. 2 - Data science goes to the Gentiles (non-DS/execs)
Storytelling with Data: a Guide to Data Visualization
Letters - Further explanation and interpretation of the DS Gospel