Instructors - Docenti:
Teaching assistant - Assistente:
… a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. Hal Varian, Google’s chief economist, predicts that the job of statistician will become the “sexiest” around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them.
Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.
La grande disponibilità di dati provenienti da database relazionali, dal web o da altre sorgenti motiva lo studio di tecniche di analisi dei dati che permettano una migliore comprensione ed un più facile utilizzo dei risultati nei processi decisionali. L'obiettivo del corso è quello di fornire un'introduzione ai concetti di base del processo di estrazione di conoscenza, alle principali tecniche di data mining ed ai relativi algoritmi. Particolare enfasi è dedicata agli aspetti metodologici presentati mediante alcune classi di applicazioni paradigmatiche quali il Basket Market Analysis, la segmentazione di mercato, il rilevamento di frodi. Infine il corso introduce gli aspetti di privacy ed etici inerenti all’utilizzo di tecniche inferenza sui dati e dei quali l’analista deve essere a conoscenza. Il corso consiste delle seguenti parti:
Classes - Lezioni: DM 1
Giorno | Orario | Aula |
---|---|---|
Giovedì/Thursday | 14:00 - 16:00 | Aula B |
Venerdì/Friday | 14:00 - 16:00 | Aula A1 |
Classes - Lezioni: DM 2
Giorno | Orario | Aula |
---|---|---|
Monday | 9:00 - 11:00 | Aula N1 |
Wednesday | 9:00 - 11:00 | Aula L1 |
Office hours - Ricevimento:
Day | Aula | Topic | Learning material | Instructor | |
---|---|---|---|---|---|
1. | 26.09.2013 14:00-16:00 | B | Intro: data mining & knowledge discovery process | Textbook, Chapt. 1 dm_intro-2011.pdf | Pedreschi |
2. | 27.09.2013 14:00-16:00 | A1 | Intro: data mining & knowledge discovery process | Textbook, Chapt. 1 dm_intro-2011.pdf | Pedreschi |
3. | 03.10.2013 14:00-16:00 | B | Data: types and basic measures | Textbook, Chapt. 2 chap2_data_new.pdf | Pedreschi |
4. | 10.10.2013 14:00-16:00 | B | Data: types and basic measures | Textbook, Chapt. 2 chap2_data_new.pdf | Pedreschi |
5. | 11.10.2013 14:00-16:00 | A1 | Exploratory data analysis and data understanding. | Textbook, Chapt. 3 chap3_data_exploration.pdf | Pedreschi |
6. | 17.10.2013 14:00-16:00 | B | Frequent Pattern Mining. | Textbook, Chapt. 6 2-3tdm-restructured_assoc_2013.pdf | Giannotti |
7. | 18.10.2013 14:00-16:00 | A1 | Frequent Pattern Mining. | Textbook, Chapt. 6 2-3tdm-restructured_assoc_2013.pdf | Giannotti |
8. | 24.10.2013 14:00-16:00 | B | Association Rule Mining. | Giannotti | |
9. | 25.10.2013 14:00-16:00 | A1 | Association Rule Mining and Knime | Textbook, Chapt. 6 Example on AR Knime | Monreale |
10. | 31.10.2013 14:00-16:00 | B | Classification and predictive methods | Textbook, Chapt. 4 chap4_basic_classification.pdf | Pedreschi |
11. | 14.11.2013 14:00-16:00 | B | Classification. Decision trees | Textbook, Chapt. 4 chap4_basic_classification.pdf | Pedreschi |
12. | 15.11.2013 14:00-16:00 | A1 | Classification. Decision trees | Textbook, Chapt. 4 chap4_basic_classification.pdf | Pedreschi |
13. | 21.11.2013 14:00-16:00 | B | Classification. Rule-based and bayesian methods | Textbook, Chapt. 4 chap4_basic_classification.pdf | Pedreschi |
14. | 22.11.2013 14:00-16:00 | A1 | Classification. Validation and Weka Lab | Pedreschi | |
16. | 28.11.2013 14:00-16:00 | B | Classification. Validation and Weka Lab. Clustering: introduction. | Textbook, Chapt. 8 dm2014_clustering_intro.pdf | Nanni |
15. | 29.11.2013 14:00-16:00 | A1 | Clustering analysis. Centroid-based methods | Textbook, Chapt. 8 dm2014_clustering_kmeans.pdf | Nanni |
16. | 05.12.2013 14:00-16:00 | B | Clustering analysis. Hierarchical methods | Textbook, Chapt. 8 dm2014_clustering_hierarchical.pdf | Nanni |
17. | 06.12.2013 14:00-16:00 | A1 | Clustering analysis. Density-based methods | Textbook, Chapt. 8 dm2014_clustering_dbscan.pdf | Nanni |
18. | 12.12.2013 14:00-16:00 | B | Clustering analysis. Validation and Weka Lab | Textbook, Chapt. 8 dm2014_clustering_validation.pdf | Nanni |
19. | 13.12.2013 14:00-16:00 | A1 | Wrap-up. Presentation of Second Semester syllabus | Nanni |
Day | Aula | Topic | Learning material | Instructor | |
---|---|---|---|---|---|
1. | 17.02.2014 9:00-11:00 | N1 | Introduction + Advanced Classification Methods / 1 | Textbook, Chapt. 5 chap5_alternative_classification.pdf | Pedreschi |
2. | 19.02.2014 9:00-11:00 | L1 | Advanced Classification Methods / 2 | Pedreschi | |
3. | 24.02.2014 9:00-11:00 | N1 | Advanced Classification Methods / 3 | Pedreschi | |
4. | 26.02.2014 9:00-11:00 | L1 | Case study- CRM1- Customer Segmentation - CRISP | 1.dm2-intro-airmiles-stulong-crisp.ppt.pdf | Giannotti |
5. | 3.03.2014 9:00-11:00 | N1 | Sequential patterns / 1 | 2.dm2_association_analysis_in_short_sequentialpatterns.ppt.pdf | Giannotti |
6. | 5.03.2014 9:00-11:00 | L1 | Case Study: CRM on retail selling / 1 - Churn analysis | 2.dm3_churn-analysis.ppt.pdf | Giannotti |
7. | 10.03.2014 9:00-11:00 | N1 | Sequential patterns / 2 | 3.dm2_sequentialpatterns.ppt.pdf | Giannotti |
12.03.2014 9:00-11:00 | L1 | Suspended | |||
8. | 17.03.2014 9:00-11:00 | N1 | Graph mining | graph_mining_2014_fixed.pdf | Nanni |
9. | 19.03.2014 9:00-11:00 | L1 | Case Study: CRM on retail selling - Promotions/ 1 | dm2_crm_promotional-sales_2014.pdf Paper on promotions | Giannotti |
10. | 24.03.2014 9:00-11:00 | N1 | Time series / 1 | time_series_from_keogh_tutorial.pdf | Nanni |
11. | 26.03.2014 9:00-11:00 | L1 | Case Study: CRM on retail selling - Promotions / 2 | Giannotti | |
12. | 07.04.2014 9:00-11:00 | N1 | Time series / 2 | Nanni | |
13. | 09.04.2014 9:00-11:00 | L1 | Case Study: Geo-marketing | Geo-churn, crm2014_pennacchioli_bigdata13.pdf | Nanni |
14. | 14.04.2014 9:00-11:00 | N1 | Spatial/Spatiotemporal analysis / 1 | 7.dm2_mobilitydatamining_.pptx.pdf chap06_mobility_data_mining-1.pdf | Giannotti |
15. | 16.04.2014 9:00-11:00 | L1 | Spatial/Spatiotemporal analysis / 2 & Projects presentation | dm2_projects_2014.pdf | Giannotti & Nanni |
16. | 28.04.2014 9:00-11:00 | N1 | Case study: Mobility / 1 | Mobility case studies 1 | Giannotti |
17. | 30.04.2014 9:00-11:00 | L1 | Platform M_Atlas | Nanni | |
18. | 05.05.2014 9:00-11:00 | N1 | Students' short seminars | Mining changes in customer behavior in retail marketing., An e-customer behavior model with online analytical mining for internet marketing planning. | Nanni |
19. | 07.05.2014 9:00-11:00 | L1 | Case study: Mobility / 2 | Mobility case studies 2 | Nanni |
20. | 12.05.2014 9:00-11:00 | N1 | Ethical Issues in Data Analytics | Privacy: Regulations and and Privacy Aware Data Mining | Giannotti |
21. | 14.05.2014 9:00-11:00 | L1 | Ethical Issues / Fraude Detection Case Study | Giannotti | |
22. | 19.05.2014 9:00-11:00 | N1 | Projects discussion | Giannotti/Nanni |
L'esame consiste in una prova scritta ed in una prova orale:
[ Italian ]
L'esame consta di tre parti:
[ English ]
The exam is composed of three parts:
Data | Orario | Luogo | Note | Voti | |
---|---|---|---|---|---|
I Esercizio e II Esercizio |
Session | Date | Time | Room | Notes | Results |
---|---|---|---|---|---|
1. | Thursday 16 January 2014 | 9.30 | TBD | A1 | |
2. | Monday 10 February 2014 | 9.30 | TBD | C | |
3. | Thursday 20 February 2014 | 14.00 | TBD | Predreschi's office | |
4. | Tuesday 25 February 2014 | 14.00 | TBD | Predreschi's office | |
5. | Monday 9 June 2014 | 9.00 | N1 | If needed, exams will continue on 10/6 and 11/6 in room L1 | Data Mining I: Results of written exam, June 9th, 2014 |
6. | Monday 30 June 2014 | 9.00 | N | If needed, exams will continue on 1/7 and 2/7 in rooms P and E | Data Mining II: Results of written exam, June 30th, 2014 |
7. | Monday 21 July 2014 | 9.00 | L1 | If needed, exams will continue on 10/6 and 11/6 in room L1 | |
8. | Tuesday 9 September 2014 | 15.30 | C1 |
Session | Date | Time | Room | Notes | Results |
---|---|---|---|---|---|
1. | Monday 19 January 2015 | 9.00 | C | ||
2. | Monday 16 February 2015 | 9.00 | C |
Date | Time | Room | Notes | Results |
---|---|---|---|---|
7 November 2014 | 9:00-11:00 | C1 |