Instructor:

• Anna Monreale
  • KDDLab, Università di Pisa
  • anna [dot] monreale [at] unipi [dot] it
• Mattia Setzu
  • KDDLab, Università di Pisa
  • mattia [dot] setzu [at] unipi [dot] it

Teaching Assistant:

• * Lorenzo Mannocci
  • University of Pisa
  • lorenzo [dot] mannocci [at] phd [dot] unipi [dot] it

• [14.09.2024] The lectures will start on 19th September 2024

• Fundamental concepts of data knowledge and discovery.
• Data understanding
• Data preparation
• Clustering
• Classification
• Pattern Mining and Association Rules
• Outlier Detection
• Time Series Analysis
• Sequential Pattern Mining
• Ethical Issues

Classes

Office hours - Ricevimento: Anna Monreale: TBD

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, but recordings of the lecture or of the previous years will be made available here for non-attending students.

• 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.
• Laura Igual et al. Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications. 1st ed. 2017 Edition.
• 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".

• 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

TBD

Data Mining (309AA) - 9 CFU A.Y. 2023/2024

DM-INF 2022-2023

Data Mining (309AA) - 9 CFU A.Y. 2021/2022

Data Mining (309AA) - 9 CFU A.Y. 2020/2021

DM-2019/20