Library

A collection of books that I have read or want to read that are in some way related to data science, software engineering, machine learning, and so on with a few books sprinkled in that have been tangentially beneficial to my journey in the aforementioned fields.

Books are organized into the following categories:

* denotes I have read the book in its entirety.

~ denotes I have read parts of the book.

^ denotes I am currently reading the book.

Data Engineering

R

• Reis, J., & Housley, M. (2022). Fundamentals of Data Engineering: Plan and Build Robust Data Systems. O'Reilly Media.

Data Science

G

• ~ Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media.

P

• * Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly Media.

Machine Learning Engineering

H

• Hall, P., Curtis, J., & Pandey, P. (2023). Machine Learning for High-Risk Applications. Compliments of Dataiku. O'Reilly Media.
• Huyen, C. (2022). Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications. O'Reilly Media.

Research

M

• * Mineault, T. (2021). Good Research Code Handbook. Zenodo.

• ~ Mulaney, T., & Rea, C. (2022). Where Research Begins: Choosing a Research Project that Matters to You (and the World). University of Chicago Press.

Statistics

A

• Albert, J. (2017). Teaching Statistics Using Baseball. American Mathematical Society.
• Albert, J. (2022). A Course in Exploratory Data Analysis.
• * Larson, R., & Farber, E. (1999). Elementary statistics: Picturing the world. Pearson Education.
  • Physical copy only; no PDF to link.