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.

## Information

Date | Info |
---|---|

01/04/2020 |
Lab lectures for the Statistics and data analytics course The optional lab for the Statistics and data analysis course will start on Friday, April 3rd, via distance learning. Students can download the dataset which will be analyzed during the first lecture, as well as its description. |

17/03/2020 |
Movement of video lectures In a few days, the recordings of the lectures will be moved to the University's OneDrive space. Students are therefore invited to check that their academic account linked to Office365 is activated. |

12/03/2020 |
Remote office hours organization Starting today, office hours will take place remotely. On each Thursday, students can connect from 17:00 to the meeting «ricevimento-malchiodi» organized on meet.jit.si , writing their name and surname in the chat, and waiting to be called. The channel is open to all participants, so the need for private office hours must be reported, always in the chat, when connecting. |

06/03/2020 |
Organization of distance learning Until further notice, the lectures of «Statistics and data analysis» and «Algorithms for massive datasets» will take place via distance learning. On the days when a course is scheduled, a video recording of the lesson will be made available on the corresponding Web page. Students can send questions to the teacher via email on any clarifications: the following day a documentcontaining the answer to the questions of general interest will be published. |

06/03/2020 |
Recording of the lecture «Introduction to statistics and centrality indices» for the Statistics and data analysis course The recording of the lecture «Introduction to statistics. Centrality indices.» for the Statistics and data analysis course is available. |

05/03/2020 |
Restricted access to lecture recordings Access to confidential content has changed. The «Course material» section in the pages of the involved courses describes the new method. |

04/03/2020 |
Recording of the lecture «Python» for the Statistics and data analysis course The recording of the lecture «Python» for the Statistics and data analysis course is available. |

23/02/2020 |
Cancellation of teaching activities All teaching activities are canceled until 29/2. |

13/02/2020 |
Office hours on February, 20th The office hours of February, 20th are canceled. |

05/02/2020 |
Timetable change for the Statistics and data analysis course Tuesday lectures of Statistics and data analysis will start at 11:30. |

## Language

Lectures are in italian.

## Course schedule

Lectures take place at the educational sector of Città studi, according to the following provisional schedule:

Day | Hour | Place |
---|---|---|

Tuesday | 12:30 - 14:30 11:30 - 13:30 | G21 |

Thursday | 13:30 - 16:30 | 405 |

Any change to the schedule will be announced in class and published in paragraph News of this page. There will be additional, non-compulsory, lab lectures each Friday, starting mid-october. The starting date will be communicated later on.

## Office hours

Thursday, at 17:00.
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 recording of some lectures, marked with (R) in the course schedule, is available. Authentication is done using the Office365 academic account.

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).

Some lecutre 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.

## 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

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.

## Exam sessions

Session | Date | |
---|---|---|

June | 17/06/2020 | |

July | 07/07/2020 | |

September | 08/09/2020 | |

September | 25/09/2020 | |

January | 14/01/2021 | |

February | TBA |