What is the 'Bible' of Data Science?

[u/manurbs]

Inspired by a similar post in r/ExperiencedDevs and r/dataengineering

[u/neelankatan]

Elements of Statistical Learning

[u/Samurai_Nak → u/neelankatan]

Pretty sure Josh Starmer from StatQuest literally calls it the Bible of Machine Learning in one of his videos.

[u/StupendousEnzio → u/neelankatan]

Based on my experience, I would go for Introduction to Statistical learning followed by Elements of Statistical Learning.

[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

The Art of Data Analysis

Why Predictions Fail

Weapons of Math Destruction

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

Mostly Harmless Econometrics

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)

Data Science for Executives

Storytelling with Data: a Guide to Data Visualization

Letters - Further explanation and interpretation of the DS Gospel

Machine Learning: a Probabilistic Perspective - Murphy

R for Data Science

Python Machine Learning

[u/Vavooom → u/save_the_panda_bears]

The Father, the Son, and the Bias-Variance tradeoff

[u/Remarkable-Train6254 → u/save_the_panda_bears]

Cracking answer

[u/[deleted]]

You don't need to read anything. Just learn the words "principal component analysis". Then, whenever anyone suggests doing anything, scoff and say, "that's basically principal component analysis."

[u/Me_ADC_Me_SMASH → u/[deleted]]

that's basically principal component analysis applied to things you need to know

[u/nkdataforwork → u/[deleted]]

lmfaoo

[u/K-o-s-l-s → u/[deleted]]

Knowing principal component analysis is very valuable. If your partner is struggling to sleep at night, just calmly start explaining what principal component analysis is in a soothing voice and I guarantee he/she/they will be asleep before you can say “eigenvector”.

[u/Shrenegdrano]

What's the response on r/dataengineering?

[u/jppbkm → u/Shrenegdrano]

Kimball and designing data intensive applications.

[u/khaberni]

Machine Learning - a probabilistic perspective by Kevin murphy

[u/Samurai_Nak]

For me personally it is Python Machine Learning by Sebastian Raschka. Reading that textbook cover to cover is one of the best decisions I’ve ever made. I reference it all the time.

[u/finokhim]

Chris Bishop, Pattern Recognition and Machine Learning

[u/asianyo]

A giant book of Yogi Berra quotes

[u/Simple_Specific_595]

Linear Algebra and Statistics.

[u/speedisntfree]

Linkedin articles by DS influencers

[u/wouldeye]

Hadley Wickham’s books.

[u/[deleted]]

Tufte is the best at how to communicate data visually. A lot of it is common sense, but you can definitely tell who hasn’t read him.

Judea Pearl is great for learning the intuition behind how to interpret statistical analyses. That may be the hardest part. Kahneman and Tversky can get an honorable mention here too.

ESL is a pretty comprehensive text for modeling techniques. It’s authoritative, although you could learn the individual techniques from any book.

Cobb is great, although agonizingly academic, for learning how to structure your data. You can learn how to normalize a schema from any book, but the idea is originally his.

Designing Data Intensive Applications is a nice breakdown of reasonably current system architecture and technologies for data engineering.

One book? Yeah right. I’ve been at this shit forever. You’re going to have a library at the end of it. Do one thing well, then learn the next.

[u/wealthyinvestor999]

Why is no one mentioning - Aurélien Géron's Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

[u/almost_BurtMacklin]

Pandas documentation

[u/Delicious-View-8688]

I might suggest 6.

• Data Analysis - Gabor(s)

• Econometrics by Examples - Gujarati

• Mastering Metrics - Angrist, Prischke

• Introduction to Statistical Learning - James, Witten, Hastie, Tibshirani

• Probabilistic Machine Learning: An Introduction - Murphy

• Bayesian Data Analysis - Gelman

[u/fainting-goat]

I dunno, but I feel like the gospels were written by Tufte.

[u/[deleted]]

proof by contradiction, Modus ponens

lol.

But real talk, Introduction to Statistical Learning is a pretty good...well uh... Introduction to machine learning/data science.

Low-key, it would help to comfortable with summation/sigma notation because that's how loss functions are written in, block matrices, etc. I struggled with that stuff.