BSc in Computer science (Università degli Studi di Milano)
The course aim at introducing the fundamentals of descriptive statistics, probability and parametric inferential statistics.
Expected results
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.
Language
Lectures are in italian.
Course schedule
Lectures take place in presence, during the spring semester, at the "Città studi" educational sector. The schedule is as follows:
| Day | Hour | Place |
|---|---|---|
| Monday | 13:30 - 16:30 | Aula Magna (Via Celoria 18) |
| Thursday | 13:30 - 16:30 | Aula Magna (Via Celoria 18) |
Any change to the schedule will be announced in class and published in paragraph News of this page.
Office hours
By appointment, room 5015 of the Computer Science Department.
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.
Course material
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.
Syllabus
The course explains the topics listed in the lecture calendar.
Prereqs
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.
Lectures calendar
Exam modalities
Students should sign up for the examination session in order to be admitted at the exam. The assessment consists of a compulsory written examination, lasting two and a half hours, and an oral examination.
The written examination consists of open-ended questions of either a theoretical or an applied nature, with content and difficulty levels consistent with the topics covered during the course. Students may consult a formula sheet and statistical tables provided by the instructor, and may use non-programmable calculators. The consultation of any other materials, including textbooks, personal notes, or electronic devices such as mobile phones, is strictly prohibited. The written examination is graded on a 30-point scale, and the results are communicated by email. Assessment is based on the student's level of mastery of the course material and on the correct use of mathematical notation and formalism. Passing the written examination is a necessary condition for passing the course assessment; failure to pass the written examination therefore results in failure of the examination as a whole.
The oral examination is compulsory for students who obtain a grade below 22/30 or above 26/30 in the written examination. It consists of a discussion of the student's written work and of questions concerning topics covered in the course. The oral examination is assessed on the basis of the student's mastery of the course material, clarity of presentation, appropriateness of language, and correct use of technical terminology. The final grade for the examination, expressed on a 30-point scale, is determined on the basis of the outcomes of both the written and the oral examinations.
Exam sessions
| Session | Date | |
|---|---|---|
| January | 18/01 | |
| February | 03/02 | |
| February | 24/02 | |
| June | 14/06 | |
| July | 05/07 | |
| September | 16/09 |