Statistics for Data Science: Basics and Modelling

This lecture covers the basics of statistical modelling and probability for data science. Topics include statistical modelling frameworks, probability distributions, conditional probability, dependence, and the principles of probability theory. The instructor emphasizes the importance of understanding stochasticity in models and provides examples such as coin flipping and planetary motion laws. The lecture also delves into probability measures, distribution functions, and quantile functions of random variables. Additionally, it explores the concepts of probability density functions, probability mass functions, and the generation of realizations from different distributions.