Data Science

This book supports the PHDSI course on foundational data science: probability, statistics, linear models, and hands-on algorithm labs. Content is drawn from the original course text and supplemented with interactive notebooks.

Table of contents¶

• Data Science
• Probability
  • Sets, the sample space, and probability
  • Random variables
  • Probability distributions: templates
• Statistics
  • Law of large numbers, Method of Moments, and the Central limit theorem
  • Likelihood theory
  • Single covariate linear regression and conditional expected value and variance
  • Matrix calculus
  • Logistic regression
• Algorithms lab
  • Laboratory 01
  • Laboratory 02
  • Laboratory 03
  • Laboratory 04
  • Laboratory 05
• Multivariate methods
  • Principal components analysis