The course aim at introducing the fundamentals of descriptive statistics, probability and parametric inferential statistics.
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.
Office hours on April, 2nd
The office hours of April, 2nd are canceled.
Access to recorded lectures
Access to the recorded lectures is allowed only to students who have signed up in the course moodle page. Should this not be possible, students are asked to contact the teacher.
Office hours change for the course «Statistics and data analytics»
The 12/03 office hours of the course «Statistics and data analytics» will start at 18:30.
Semester for the Statistics and data analytics course
The class of Statistics and data analytics will be delivered in the spring semester.
Lectures are in italian.
Lectures take place (until further notice) using a hybrid modality: in presence at the educational sector of "Città studi", and online through authentication to a zoom link published on the moodle page of the course. The provisional schedule is as follows:
|Tuesday||11:30 - 13:30||303|
|Thursday||14:30 - 17:30||Beta|
Any change to the schedule will be announced in class and published in paragraph News of this page. The recording of lectures, marked with (R) in the schedule, is available until the end of the course, through access to the corresponding moodle page.
By appointment (via e-mail).
It is possible contact the teacher by e-mail, taking care to read in advance the guide prepared by Prof. Sebastiano Vigna and clearly specifying in the message the course name and the academic year. In particular, students are encouraged to always use their academic address (i.e. based on the domain
studenti.unimi.it) signing with name and student ID number and recalling that the response time may vary depending on the teacher commitments.
Specific, group-based office hours for the course will be organized on a weekly basis, each Friday at 17:30 using the same zoom link of lectures.
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, 2014, ISBN 978-0-12-394811-3 (marked by RPS in the lectures calendar).
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.
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.