This introductory course teaches the basics of probability theory and statistics. It aims to develop a good intuition of random events and variables, common distributions and their properties, estimators and statistical tests. The emphasis is made on the tools widely applied in data science, such as maximum likelihood estimation and Bayesian inference.
Besides the classical paper-and-pencil problems, there will be assignments in the Python ecosystem. After completing the course, the students should be able to propose probabilistic models to describe randomness in life, and statistical methods to estimate their parameters. The students would also be ready to apply the learned methods the subsequent courses on machine learning.