Curriculum for an abbreviated Data Science "masters"

data-science-curriculum.md

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