Data Science Curriculum
Goals
- Broad not narrow focus (i.e. more than deep learning)
- Emphasis on fundamentals: theory over technology
- Present a clear, optimized path: separate essential areas of study from practical resources
- Assume strong software engineering foundation
Foundations
- Concepts of Modern Mathematics by Ian Stewart
- Calculus by Spivak
- Essence of Linear Algebra by 3B1B
- Introduction to Statistical Inference by Jack C. Kiefer
- Probability and Random Processes by Geoffrey R. Grimmett, David R. Stirzaker
- Data Analysis: A Bayesian Tutorial by Devinderjit Sivia, John Skilling
Core
Introduction
- Data Science from Scratch: First Principles with Python by Joel Grus
- An Introduction to Statistical Learning by Gareth James, Trevor Hastie, Robert Tibshirani, Daniela Witten
- Google: Machine Learning Crash Course
- Coursera: Andrew Ng's Machine Learning
Data Exploration
- Kaggle: Courses in Machine Learning
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney
- Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas
- Kaggle: Data cleaning challenge
- Coursera: Applied Data Science with Python Specialization
Theory
- The Elements of Statistical Learning
- Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David
- Coursera: Probabilistic Graphical Models Specialization
Ethics and Bias in Data Science (podcasts & audiobooks)
- Data Skeptic by Kyle Polich
- Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
- Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil
TODO
- Add Data Engineering curriculum/resources