General Information
SSD: ING-INF/05
CFU: 8
Professor: Flavio Giobergia
Teaching Assistants: Alkis Koudounas, Eleonora Poeta, Lorenzo Vaiani
Exams
Exam rules
The exam rules for the A.Y. 2024/25 will be made available here — stay tuned!
Written Exams
In this section, you will find the results of the written tests — good luck!
- Winter Session # 1
- Winter Session # 2
- Summer Session
- Fall Session
Projects
| Exam Session | Assignment | Results | Example Report * |
| Winter | |||
| Summer | |||
| Fall |
* Occasionally, we may ask students to publish here their reports in case of very good productions. They will serve as a reference for their colleagues.
Teaching Material
Data science
This section will contain the slides of the data science course.
- Course introduction (slides)
- Introduction to data science (slides)
- Data preprocessing (slides)
- Association rules (slides)
- Data exploration, feature engineering and data visualization (slides)
- Classification fundamentals (slides)
- Clustering fundamentals (slides)
- Regression analysis (slides)
- Time series analysis (slides)
Python
This section will contain the slides of the data science course.
- Introduction to Python (slides)
- Python programming (slides)
- Structuring Python projects (slides)
- NumPy (slides)
- Pandas (slides)
- Scikit-learn (Classification) (slides)
- Scikit-learn (Regression) (slides)
- Scikit-learn (Preprocessing) (slides)
- Matplotlib (slides)
Other material
- GitHub repository with exercises
- Scientific writing – how to write your report (slides)
Laboratory Material
This section will contain all the material for carrying out laboratories. No laboratory will be evaluated and assigned a mark, so no laboratory will give additional points to the final exam.
Introduction to laboratories – pdf
- Lab 1 – Python basics
- Lab 2 – Data Preparation
- Lab 3 – Frequent Itemsets, Association Rules
- Lab 4 – KNN Implementation
- Lab 5 – Pandas
- Lab 6 – Tree-based models
- Lab 7 – Classification *
* During this laboratory, we will set up Data Science Lab Environment, the online evaluation platform we will use during the leaderboard part of the project.
Team organization
Students will be divided into two teams, Team 1 and Team 2. Team 1 will attend the laboratories on Monday from 10:00 to 13:00 in LAIB3B. Team 2, instead, will attend on Tuesday from 8:30 to 11:30 in LAIB2B.
You can use the following rule:
- last name from “A” to “K” => Monday, 10:00 to 13:00, LAIB3B (Team 1)
- last name from “L” to “Z” => Tuesday, 08:30 to 11:30, LAIB2B (Team 2)