Instructors:

• Francesca Naretto
• KDDLab, Università di Pisa
• francesca [dot] naretto [at] unipi [dot] it
• Riccardo Guidotti
• KDDLab, Università di Pisa
• riccardo [dot] guidotti [at] unipi [dot] it

Classes

Office hours - Ricevimento: Riccardo Guidotti: Online using Teams or in my Office (Appointment by email). Francesca Naretto: Online using Teams or in my Office (Appointment by email).

A Teams Channel will be used ONLY to post news, Q&A, and other stuff related to the course. The lectures will be only in presence and will NOT be live-streamed.

• Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Addison Wesley, ISBN 0-321-32136-7, 2006
  • http://www-users.cs.umn.edu/~kumar/dmbook/index.php
  • Chapters 4,6 and 8 are also available at the publisher's Web site.
• Jake VanderPlas. Python Data Science Handbook: Essential Tools for Working with Data. 1st Edition.
• For Python Notions: Very basic notions on Python

• The slides used in the course will be inserted in the calendar after each class. Most of them are part of the slides provided by the textbook's authors Slides per "Introduction to Data Mining".

For those of you that opt for the written exam, these are some exercises similar to the practical part of the exam (written exam is composed of practical and theoretical questions).

• Exercises on Clustering: ex._clustering.pdf
• Excercise for DT learning simulation: dt-learning-simulation.pdf learnedtree.pdf
• Some text of past exams of a similar course: 2017-1-19.pdf, 2017-9-6.pdf, 2016-05-30-dm1-seconda.pdf, dm2_exam.2017.06.13_solutions.pdf, dm2_exam.2017.07.04_solutions.pdf, dm2_mid-term_exam.2017.06.06_solutions.pdf, dm2_exam.2015.04.13.results.pdf, dm2_exam.2016.04.4_sol.pdf, dm2_exam.2016.04.5_sol.pdf, dm2_exam.2016.06.20_sol.pdf, dm2_exam.2016.07.08_sol.pdf
• Some very old exercises (part of them with solutions) are available here, most of them in Italian, not all of them on topics covered in this year program: Verifica 2006, Verifica 2005 (con soluzioni), Verifica 2004, Verifica 5 giugno 2007, Verifica 26 giugno 2007, Verifica 24 luglio 2007 (e Soluzioni), Verifica 18 luglio 2008 - parte 1, Verifica 18 luglio 2008 - parte 2, Exam with solution 2010-06-01,Exam with solution 2010-06-22, Exam with solution 2010-09-09, Exam with solution 2010-07-13

For following the practical lessons of the course, as well as for the project, you need the following softwares:

• Python - Anaconda (at least 3.7 version!!!): Anaconda is the leading open data science platform powered by Python. Download page (the following libraries are already included)
• Scikit-learn: python library with tools for data mining and data analysis Documentation page
• Pandas: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Documentation page

The exam consists of: a group project (in teams of two or three) and an oral exam that includes a discussion of the project and an assessment of the theoretical knowledge acquired, for those who complete the project during the course and meet all intermediate and final deadlines set by the instructors.

Alternatively, students who do not complete or submit the project within the established deadlines will be required to take a written exam and an oral exam covering all course topics.

PROJECT

A project consists in data analyses based on the use of data mining tools. The project has to be performed by a team of 2 max 3 students. It has to be performed by using Python. The guidelines require to address specific tasks. Results must be reported in a unique paper. The total length of this paper must be max 25 pages of text including figures. The students must deliver both: paper (single column) and well commented Python Notebooks.

Deadline. January 5th, 2026. Delivery instructions. The final deadline of the project is 5th January 2026 at 23:59. This deadline is STRICT. No extension is possible because then the winter session of exams starts. Groups that will not deliver the project by 5th January will need to do the written exam during the exam sessions. Each group must deliver by email to anna.monreale@unipi.it, francesca.naretto@unipi.it a zipped folder named DM_GroupID.zip and containing 4 folders and 1 pdf file: a folder named DM_GroupID_TASK_DU, containing source code of data understanding; a folder named DM_GroupID_TASK_CLU, containing source code of data clustering; a folder named DM_GroupID_TASK_CLA, containing source code of classification; a folder named DM_GroupID_TASK_TS, containing source code of time series analysis; a pdf file with maximum 25 pages including figures discussing the results of the tasks. The name of this file must be: DM_Report_GroupID.pdf. The file must contain the list of authors (i.e., members of the group). The subject of the email must be “DADHProject25_GroupID”

Data Analytics for Digital Health (DAD) - 9 CFU A.Y. 2024/2025