Instructors:
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 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.
| 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 | Overview 1-intro-da-dm-tecs.pdf | Chap. 1 Kumar Book | Monreale |
| 2. | 29.09 | Data Understanding TD | Data Understanding | Chap.2 Kumar Book and additioanl resource of Kumar Book: Data Exploration Chap. If you have the first ed. of KUMAR this is the Chap 3 | Naretto |
| 3. | 30.09 | Data Preparation TD | Data Preparation | Chap.2 Kumar Book and additional resource of Kumar Book: 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 | Zip file for DU e DP for TD Zip file for Python | Naretto | |
| 6. | 07.10 | Clustering: intro and k-means | Intro clustering 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 | hierarchical 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 | 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 | 6-dw.pdf | Monreale | |
| 10. | 20.10 | Data Warehouse | 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 | 2.1-data-understanding_images.pdf | Naretto | |
| 13. | 24.10 | Python Lab: Clustering | clustering_diabetes.zip | Naretto | |
| 14. | 27.10 | Pre-processing for Image | 2.1-data-understanding_images.pdf | Digital Image processing (Gonzales, Woods) | Naretto |
| 15. | 28.10 | Time series pre-processing | 5-data-understanding_ts.pdf | Monreale | |
| 16. | 31.10 | Time series pre-processing, similarities and project presentation (task n.2) | 8_time_series_similarity_2024.pdf | Naretto | |
| 17. | 03.11 | Image clustering and presentation of the project (task 3) | 3.2-clustering_images.pdf | Naretto | |
| 18. | 04.11 | ||||
| 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 |
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.