Statistics and data analytics2023/24

Students will be able to carry out basic explorative analyses and inferences on datasets, they will know the main probability distributions and will be able to understand statistical analyses conducted by others; moreover, they will know simple methods for the problem of binary classification, and will be able to evaluate their performances. The students will also acquire the fundamental competences for studying more sophisticated techniques for data analysis and data modeling.

The parts on descriptive statistics are taken from the first three chapters of the textbook Ross, Introductory statistics, Academic press, 2010, ISBN 9780123743886 (marked by PS in the lectures calendar). These chapters (from the italian edition) can be downloaded through authentication using the previously described credentials.

The remaining part course is mainly based on the following textbook: Ross, S., Introduction to Probability and Statistics for Engineers and Scientists, Academic press, 2020, ISBN 9780128243466 (marked by RPS in the lectures calendar).

Some lecture notes for topics not covered by the textbooks are available through a github repository, as well as in a form directly executable in a Web browser.

The book Wess McKinney, Python for Data Analysis, third edition, O'Reilly 2022, which is also available online, can be used for strenghtening the knowledge of Python.

The course explains the topics listed in the lecture calendar.

Students shall have passed the exam of «Calculus»; besides that, the course requires knowledge of the main topics of computer programming, and having passed the exam of «Discrete mathematics» is strongly suggested.

Students should sign up for the examination session in order to be admitted at the exam.

The exam consists of a written and an oral test, both relating to the topics covered in the course. The written test takes place in a computer-based room and it lasts two hours and a half. It is based on open-ended questions and on the analysis of a dataset through the adequate application of the statistical techniques described during the classes. The evaluation, with a mark of pass/fail, takes into account the level of mastery of the topics and the correct use of mathematical formalism.

The oral test, which is accessed after passing the written test, is based on the discussion of the written test answers and on questions concerning topics covered in the course. Its evaluation, expressed on a scale between 0 and 30, takes into account the level of mastery of the topics, the clarity, the language skills, and the correct use of technical jargon.