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====== Data Analytics for Digital Health (DAD) - 9 CFU A.Y. 2025/2026======
**Instructors:**
  * **Anna Monreale**
    * KDDLab, Università di Pisa
    * [[anna.monreale@unipi.it]]   
  * **Francesca Naretto**
    * KDDLab, Università di Pisa
    * [[francesca.naretto@unipi.it]]   



====== News ======

  * [08.09.2025] - Lecture of the first week will be canceled, so they will start on 22nd September 2025
  * [30.09.2025] - All Students must fill  [[https://docs.google.com/spreadsheets/d/1HFdrGb7PjjKglAFYHR1T0RJHjg5C2ufyPuysCSsfuho/edit?pli=1&gid=0#gid=0| this document ]] for exam information

====== Learning Goals ======
     * Fundamental concepts of data knowledge and discovery.
     * Data Types in Healthcare Data and Public Databases
     * Data understanding
     * Data preparation
     * Clustering
     * Classification
     * Rule-based methods
     * Outlier Detection
     * Time Series Analysis
     * Sequential Pattern Mining
====== Hours and Rooms ======

**Classes**

^  Day of Week  ^  Hour  ^  Room  ^ 
|  Monday   |   09:00 - 11:00  |  Room FIB PS4  | 
|  Tuesday  |  14:00 - 16:00    |  Room C1  | 
|  Friday   |  11:00 - 13:00 |  Room FIB PS4  | 



**Office hours - Ricevimento:**
Anna Monreale: TBD - Online using Teams or in my Office (Appointment by email).
Francesca Naretto: TBD  - Online using Teams or in my Office (Appointment by email).


A [[https://teams.microsoft.com/l/team/19%3AaixkwjuGSoUvrBNsO88NiDZsr8C2yIucNEonmj8ssSY1%40thread.tacv2/conversations?groupId=bfaf6e19-deca-4d53-921c-65b44db73608&tenantId=c7456b31-a220-47f5-be52-473828670aa1|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.
====== Learning Material -- Materiale didattico ======

===== Textbook -- Libro di Testo =====

  * 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. **[[http://shop.oreilly.com/product/0636920034919.do| Python Data Science Handbook: Essential Tools for Working with Data.]]** 1st Edition. 
  *  For Python Notions: {{ :magistraleinformatica:dmi:python_basics.ipynb.zip | Very basic notions on Python}} 


===== Slides =====

  * 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 [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item4|Slides per "Introduction to Data Mining"]].
   

  
===== Software=====

  * Python - Anaconda (at least 3.7 version!!!): Anaconda is the leading open data science platform powered by Python. [[https://www.anaconda.com/distribution/| Download page]] (the following libraries are already included)
  * Scikit-learn: python library with tools for data mining and data analysis [[http://scikit-learn.org/stable/ | 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. [[http://pandas.pydata.org/ | Documentation page]]

 
====== Class Calendar (2025/2026) ======

===== First Semester  =====

^ ^ Day ^ Topic ^ Learning material ^ References  ^ Teacher ^
|  |  22.09  | Strike | | | |
|  |  23.09  | CANCELED for Teacher's health issues | | | | 
|1.  |  26.09  | Overview. Introduction to Data Analyics for DH  + Data Types | {{ :digitalhealth:0-overview-2025.pdf | Overview}} {{ :digitalhealth:1-intro-da-dm-tecs.pdf |}}|Chap. 1 Kumar Book |Monreale |
|2.  |  29.09  | Data Understanding TD | {{ :digitalhealth:2-Data_Understanding.pdf | Data Understanding}}|Chap.2 Kumar Book and additioanl resource of Kumar Book: [[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Data Exploration Chap.]] If you have the first ed. of KUMAR this is the Chap 3  |Naretto |
|3.  |  30.09  | Data Preparation TD | {{ :digitalhealth:3-data_preparation_dad.pdf | Data Preparation}} | Chap.2 Kumar Book and additional resource of Kumar Book: [[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Data Exploration Chap.]] If you have the first ed. of KUMAR this is the Chap 3 | Monreale|
|4.  |  01.10 - Room I | Python Lab: Data Understanding & Preparation TD | | | Naretto|
|  |  03.10  | Strike | | | Naretto|
|5.  |  06.10  | Project Presentation + Data Understanding and Preparation for TD  |{{ :digitalhealth:PIMA-DU.zip | Zip file for DU e DP for TD}} {{ :digitalhealth:Integrazione.zip | Zip file for Python}}| | Naretto|
|6.  |  07.10  | Clustering: intro and k-means |{{ :digitalhealth:6-basic_cluster_analysis-intro.pdf | Intro clustering}} {{ :digitalhealth:6-basic_cluster_analysis-kmeans.pdf | kmeans}}| Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar	| Naretto|
|7.  |  08.10 Room I | Clustering: hierarchical and db-scan  | {{ :digitalhealth:7.basic_cluster_analysis-hierarchical.pdf | hierarchical}} {{:digitalhealth:10-basic_cluster_analysis-dbscan.pdf | DB-scan}}| Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar	| Naretto|
|     | 10.10 | suspension of teaching activities | | | |
|8.   | 13.10 | Density-based clusering + Clustering Validity | {{ :digitalhealth:12-basic_cluster_analysis-validity.pdf |}}| Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar	 |Naretto |
|     | 14.10 | Canceled: No Lecture| | | |
|9.   | 17.10 | Clustering Validity + Data Warehouse | {{ :digitalhealth:6-dw.pdf |}} | | Monreale |
|10.  | 20.10 | Data Warehouse| {{ :digitalhealth:6-dw.pdf |}}|Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar	 +  | Monreale|
|11.  | 21.10 | Data Warehouse + PowerBI Demo|Same Slides of the previous lecture | | Monreale|
|12.  | 22.10 Room Lab I| Pre-processing for Image | {{ :digitalhealth:2.1-data-understanding_images.pdf |}}| | Naretto|
|13.  | 24.10 |Python Lab: Clustering | {{ :digitalhealth:clustering_diabetes.zip}} | |Naretto |
|14.  | 27.10 | Pre-processing for Image | {{ :digitalhealth:2.1-data-understanding_images.pdf |}} |Digital Image processing (Gonzales, Woods) | Naretto|
|15.  | 28.10 |Time series pre-processing | {{ :digitalhealth:5-data-understanding_ts.pdf|}}| |Monreale |
|16.  | 31.10 |Time series pre-processing, similarities and project presentation (task n.2) | {{ :digitalhealth:8_time_series_similarity_2024.pdf |}} {{digitalhealth:timeseries_similarity_clustering.zip}}| |Naretto |
|17.  | 03.11 |Image clustering and presentation of the project (task 3)| {{ :digitalhealth:3.2-clustering_images.pdf |}}| |Naretto |
|18.  | 04.11 |Image clustering |{{ :digitalhealth:3.2-clustering_images.pdf |}} {{digitalhealth:images_similarity.zip}}| |Naretto |
|19.  | 07.11 | | | | |
|20.  | 10.11 | | | | |
|21.  | 11.11 | | | | |
|22.  | 14.11 | | | | |
|23.  | 17.11 | | | | |
|24.  | 18.11 | | | | |
|25.  | 21.11 | | | | |
|26.  | 24.11 | | | | |
|27.  | 25.11 | | | | |
|28.  | 28.11 | | | | |
|29.  | 01.12 | | | | |
|30.  | 02.12 | | | | |
|31.  | 05.12 | | | | |
|32.  | 09.12 | | | | |
|33.  | 12.12 | | | | |
|34.  | 15.12 | | | | |
|35.  | 16.12 | | | | |
|36.  | 19.12 | | | | |



====== Exams ======
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.

====== Previous years =====
[[DAD 2024-2025]]