By the end of the course, students will be able to understand basic principles of probability theory, including discrete and continuous probability distributions, expected values, and variance and apply them to model simple probabilistic problems.
They will also be able to apply basic statistical concepts, such as measures of central tendency and variability, hypothesis testing, confidence intervals and linear regression.
Additionally, the course aims to develop students' ability to critically evaluate statistical claims and interpret statistical results in real-world contexts.
Students will learn how to explore the statistical properties of real datasets and implement in Python the methods introduced during the course.