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 course offers two alternative examination paths: a project path, for students attending the course, and a written-exam path, for students who do not follow the lessons.

Students following the course as attending students are expected to participate in the project activities throughout the semester. The project must be carried out in groups of 2 or 3 students and implemented in Python.

During the lectures, the topics and methods needed to carry out the project will be presented. The project must be developed throughout the first semester, following the progress of the course. Intermediate checkpoints will be scheduled during the semester. At these checkpoints, each group will be asked to present and discuss the work completed and the results obtained so far. Participation during the lessons, as well as in the intermediate checkpoints and compliance with the corresponding deadlines are part of the project-based examination path.

The final evaluation consists of:

• Group project: 70% of the final grade
• Oral exam: 30% of the final grade

The project requires the application of the data mining methods presented during the course to the assigned data and research question. The results must be documented in a final report and accompanied by well-commented and executable Python notebooks.

The oral exam will include questions covering the entire course programme. Questions may address both theoretical concepts and practical aspects of the methods presented during the course. The oral exam will also include a discussion of the project and of the methodological choices made by the group.

The final project deadline is January 6th, 2027.

Students who choose this examination path must complete and submit the project by this deadline. Students who do not submit the project by the deadline will no longer be eligible for the project-based examination path and will be required to take the written and oral exams described in Option 2.

This examination path applies to students who do not follow the project-based activities during the course and to students who do not complete or submit the project by the established deadline.

The final evaluation consists of:

• Written exam: 70% of the final grade
• Oral exam: 30% of the final grade

The written exam will cover the entire course programme and may include both theoretical questions and practical exercises related to the data mining methods presented during the course.

The oral exam will also cover the entire course programme and may include both theoretical and practical questions.

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