Readings in Applied Data Science
A curated reading list and syllabus for a Stanford discussion class on applied data science topics.
What is Readings in Applied Data Science?
Stats 337 is a curated collection of readings and a syllabus for a university-level discussion class on applied data science. It addresses practical topics often overlooked in traditional curricula, such as data organization, software engineering practices, ethics, reproducibility, and career development in data science. The project serves as an open educational resource for anyone interested in the real-world challenges of data work.
University students, educators, and data science practitioners seeking a structured, discussion-oriented exploration of applied data science topics beyond standard textbooks.
It provides a unique, community-informed curriculum that blends academic papers with industry blog posts, focusing on the practical skills and ethical considerations essential for modern data science work.
Overview
Readings in applied data science
Use Cases
Best For
- University instructors designing a data science discussion course
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.