Introduction to Data Science

This lecture covers the organization of the course, evaluation methods, and the topics to be discussed throughout the semester. It includes an introduction to data learning, digital tools for linear algebra, principal component analysis, basic data analysis methods, and linear regression. The course also delves into uncertainty quantification in physical measurements, statistical estimation and inference, stochasticity in physical systems, random walks, Brownian motion, and probability distribution sampling. Students will acquire Python programming skills, learn error estimation in physics experiments, analyze data using linear algebra and multivariable calculus, and understand stochasticity in physics for statistical physics preparation and machine learning.