Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2019/2020

Learning outcomes

The course covers the fundamental aspects of probability theory and the principles of statistical inference. Upon successful completion of this Course, students are able to perform a rigorous data analysis: i) manipulate and summarize data; ii) visualize and understand relationships inside data; iii) apply the appropriate tools of probability theory and inferential statistics to extract useful information, test hypotheses and make predictions.

Course contents

-- Probability Theory

  1. Fundamentals of probability
  2. Random variables and probability distributions
  3. Functions of a random variable
  4. Bivariate random variables
  5. Convergence of random variables and limit theorems

-- Statistics

  1. Fundamentals of statistics
  2. Point estimation
  3. Interval estimation
  4. Hypothesis testing

-- Modern and reproducible data analysis with R and knitr

  1. Introduction to R
  2. Introduction to knitr



-- Main textbook

-- Probability and mathematical statistics

-- R and knitr

Teaching methods

  • Lectures.
  • Classes.
  • Computer science lab sessions.

Assessment methods

A two-hour written examination composed of

  • Theoretical questions.
  • Exercises.

Teaching tools

The following material will be provided:

  • Slides of the lectures.
  • Solved exercises.
  • Mock exam.

Office hours

See the website of Simone Giannerini