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====== Decision Support Systems - Module II (6 ECTS): LABORATORY OF DATA SCIENCE (2025/2026) ======
This is the second module of [[mds:dss:start|Decision Support Systems]] (801AA, 12 ECTS), previously called Laboratory of Data Science (664AA, 6 ECTS).


**Instructors**:
  * **Anna Monreale**
    * KDD Laboratory, Università di Pisa
    * [[http://pages.di.unipi.it/amonreale/]]
    * [[anna.monreale@unipi.it]] 
    * Office hours: Friday: 14:30-15:30 online using Teams or at the Department of Computer Science, room 374/E (Please ask for an appointment by email).
    * Telephone +39-050-2213119

 **Cristiano Landi**
    * KDD Laboratory, Univesità di Pisa
    * [[cristiano.landi@phd.unipi.it]]
    * Office hours: Wednesday: 14:00-16:00 online using Teams or at the Department of Computer Science, room 343 (Please ask for an appointment by email).

**Always email both instructors and include [LDS] at the beginning of the subject line.**


The following is the timetable for the whole Decision Support Systems course. The two modules span differently over the semester. The first module will take most of the lessons from September to October. The second module will take most of the lessons from November to December.

^  Day of Week  ^  Hour  ^  Room  ^ 
| Tuesday | 9:00 - 11:00 | Fib H-Lab |
| Wednesday | 11:00 - 13:00 | Fib C1 |
| Thursday | 11:00 - 13:00 | Fib C |
| Friday | 09:00 - 11:00 | Fib C |


A [[https://teams.microsoft.com/l/channel/19%3Aead0080c194243dbabdc9ef2b826d701%40thread.tacv2/Module%20II%20-%20Laboratory%20of%20Data%20Science?groupId=a9d127ae-4a11-4a6b-a045-555d1bab8b85&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams channel]] is used to post news, Q&A, and other stuff related to the course. The lectures will be only in person and will **NOT** be live-streamed, but recordings of the lecture from this or the previous years will be made available for non-attending students. The recording publication could receive some delays (one week of delays).

====== Learning Material ======

===== Slides & Recordings of the classes =====

  * The slides used in the course will be inserted in the calendar after each class.
  * Recordings of each lecture will be made available for non-attending students.
===== Past Exams =====

  * {{ :mds:lbi:2016midterm1text.pdf |2016/17 text}}, {{ :mds:lbi:2015fallmidterm1text.pdf | 2015/16 text}} and {{ :mds:lbi:2015wintermidterm1.zip | 2015/16 solution}}, {{:mds:lbi:2015midterm1text.pdf | 2014/15 text}} and {{ :mds:lbi:2015midterm1.zip |2014/2015 solution}}, {{ :mds:lbi:2014midterm1text.pdf | 2013/14 text}},{{ :mds:lbi:2013midterm1.pdf | 2012/13 text }} and {{ :mds:lbi:2013midterm1.zip |2012/13 solution}}.

===== Software=====

  * Anaconda
  * SQL Server 2019 Management Studio or newer:[[https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-ver16|SQL Server 2019 Management Studio]]. 
  * Visual Studio Community 2022. Install/include SSDT workload in the installation manager of Visual Studio: instructions Italian: [[https://learn.microsoft.com/it-it/sql/ssdt/download-sql-server-data-tools-ssdt?view=sql-server-ver15#ssdt-for-visual-studio-2022|Data Tools Visual Studio 2022 IT]] English: [[https://learn.microsoft.com/en-us/sql/ssdt/download-sql-server-data-tools-ssdt?view=sql-server-ver15#ssdt-for-visual-studio-2022|Data Tools Visual Studio 2022 EN]]. 
  * Microsoft Excel
  * [[https://powerbi.microsoft.com/it-it/desktop/| Power BI Desktop]]
  * [[https://start.unipi.it/help-ict/vpn/ | Unipi VPN ]]

**Note**: preconfigured virtual machines will be available to download in the Teams channel for both AMD64 (Intel/AMD) and ARM (Apple Silicon) architectures. The virtual machines come with all the required software installed and tested, except for Microsoft Excel: you can download it for free from [[http://office.com|office.com]] using your University credentials.

===== F.A.Q. =====
  * [[http://www.sid.unipi.it/polo2/2015/03/26/connessione-alle-reti-wifi/ | Connection to wi-fi]]
  * [[http://www.sid.unipi.it/polo2/studenti/ | F.A.Q.s about the labs]]
  * [[https://start.unipi.it/help-ict/vpn/ | Unipi VPN ]]
  * [[https://autenticazione.unipi.it/auth/auth.signin | Unipi Authentication]] to access the VPN, make sure that network access services are enabled on your profile. Follow this link to access your Unipi profile.
====== Class calendar - (2025-2026) ======
^N. ^ Day  ^Room ^ Topic ^Lecture Material ^ Video Lectures ^ Refs ^
|1.|01.10| |Introduction to the Course. BI Architectures. File data access. |{{ :mds:lbi:2025-lds.01.introduction.pdf |}} {{ :mds:lbi:2025-lds.02.bi_architectures.pdf |}} {{ :mds:lbi:2025-lds.03.file_data_access.pdf |}} |[[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQDh0G1PpN6sT6CsDcFP6rcCARFDfU1sHLWvevsVzH__aYI?e=VVvwW7|Video]] |-** BI technology:** [[https://cacm.acm.org/magazines/2011/8/114953-an-overview-of-business-intelligence-technology/fulltext | An Overview of Business Intelligence Technology]] - **File access:** {{ :mds:lbi:filesystem.pdf | File System Interface}}   |
|2.|02.10| |File data access. Python Recap. | {{ :mds:lbi:2025-lds.04.python_no_hint.pdf | Python Recap}} {{ :mds:lbi:2024-lds.04_supplementary.code.zip | supplementary code}} | [[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQDpwaIIRE7kR59OFasMQnGrARRFlLnNYNHuccTtZXQ6vdU?e=3nhEFt|File data access]] [[https://tinyurl.com/337t8hxf|Python Recap (from previous years)]] |  - **File Formats:** [[http://www.stat.auckland.ac.nz/~paul/ItDT | Introduction to data technologies(Chps. 5, 6)]],  [[http://weka.wikispaces.com/ARFF+(stable+version)|Weka ARFF Format]], [[http://weka.wikispaces.com/XRFF|XRFF Format]] - **Python reference:** [[https://www.spronck.net/pythonbook/ | Free python book with exercises]] |
|3.|22.10| |Project Presentation + Python Excercises + File Access in Python |{{ :mds:lbi:2025-lds.05.fileaccess-python.pdf |}} {{ :mds:lbi:2025-10-23-code.zip |}} |[[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQB5nDV9rjo7SrjShEp85YBPAbPYpxpTrq7LGKmthX-kE6w?e=KrlfP9|Project Presentation]] [[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQBEFtKr75UVQYDVYHjE7LHYAbL0XEMoA2aCGHvphTEg73Q?e=M8fUNQ|File access Py]]|
|4.|29.10| |Python File Access + Exercises | same slides used in the previous lecture{{ :mds:lbi:data1.zip | Data ex files}} | [[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQDMPfG7S6G0QKu3-hvN1pRNAZiDPUVuXhHlDjeQlD6-qX8?e=W2T6zj|Video]]  | 
| |30.10| |Lecture canceled  |  | 
|5.|04.11| |RDBMS access protocols + Python DB Access | {{ :mds:lbi:2024-lds.06.relational_data_access.pdf | Slides}} | 
|6.|05.11| |Exercises correction + Stratified sampling Exercise | {{ :mds:lbi:lds05_fileaccesspython_solutions.zip |}} {{ :mds:lbi:ex-customers.pdf |}} {{ :mds:lbi:data-customers.zip|}}  | |
|7.|11.11| Fib H|Python DB Access: exercises | {{ :mds:lbi:stratifiedsampling.zip |}} | |
|8.|13.11| Fib C |SQL Server + SSIS + ToCSV + FromCSV| {{ :mds:lbi:2024-lds.07.sqlserver.pdf |SQL SERVER}} {{ :mds:lbi:2024-lds.08.etlandssis.pdf | SSIS}} |[[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQD7nBKuQMn2T4uPA28VKrm_AfY_DcoRW8at7xwcPkfDHZA?e=vo3Lfi|SISS-Part1]] [[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQD9kEOKGLhpToMk41gH9ikSAVKYBWiH3egoU888EmkVKh0?e=loKj3J|SSIS-Part2 includes SQL Server Connection by Management Studio]] |
|9.|14.11| Lab H |SSIS: Pipeline + Stratified Sampling assignmt|  | [[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQBmsWKTFkCPSbiCe2BaveyTATyy8FlAzaLqw60uwBpLhJo?e=3DOuJE|Pipeline-Worksflows]]|
|10.|18.11|Lab H |Correction of exercise in python on customers  | {{ :mds:lbi:2025_excustomer_sol.zip | ExCustomer sol}} |[[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQBHQr2Y2ZZSQrm9wm8FWwDZAZKDita_Y36dUWYoeeJ7LlM?e=jFgkBP|Correction Python Excercise on Customers]]|
|11.|19.11|Lab I |SSIS:Update, Surrogate Keys | | [[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQBjYKiUz8BZR68-hqrdmrxUAcFto20QK4okEdRRlnKC4Tw?e=VNf9pv|Video]]|
|12.|20.11| Fib C | Correction of Stratified Sampling + SCD workflow for new customers | | [[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQD4y5RXLCDAQZEMM9iJ0BbkAbJwUcYo-yZO1VmT9TnGWaw?e=x1bpzr|Video]]|
|13.|21.11|Lab H  | SCD workflow for update customers +  SSIS practice |{{ :mds:lbi:ssis_dissimilarity_idx.pdf |Dissimilarity}} {{ :mds:lbi:2015midterm1text.pdf | MPD}} | [[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQD2Bb5-iJQaTorx40XlgsHjAdO_so2YLUVHCub0JIvp_5Y?e=mOReyv|Video]]|
|14.|25.11|Lab H  | SSIS practice + Solutions of Dissimilarity and MPD| | [[https://unipiit.sharepoint.com/:v:/s/a__td_69781/IQAS8i9_GYxyTax_x1t8OdgJARZVvDauNw62C8e4gdPN9zo?e=JcJ7ma|Video]]|
|15.|26.11|Lab I  | CDC Process  |  | |
|16.|27.11|Fib C  | Introduction to SSAS + DW | {{ :mds:lbi:2024-lds.09.dwandolap.pdf | SSAS Intro}} | |1) SSAS (olap): documentation; 2) S. Harinath et al. Professional Microsoft SQL Server Analysis Services 2012 with MDX and DAX, Wrox publisher, 2012. Chps. 4-6 |
|17.|28.11|Lab H  | Olap Cube| {{ :mds:lbi:2024-lds.09.ssas.pdf |slides}}{{ :mds:lbi:foodmart_monreale_full_2025.zip |}} Instructions for the SSAS project: to avoid conflicts in deployment/process follow this steps once the solution is opened: (1) rename the project as <your account>_foodmart (2) from project properties select 'Deployment', then rename the database as <your account>_foodmart; (3) click on the button “show all files” just above “Solution explorer” right click on “view code” on the .database file that is visualized, and then change the ID from current name into <your account>_foodmart, and finally save the file. (4) change the credentials of connection to database on SQL Server.|  
|18.|02.12|Lab H  | PROJECT CHECK -MANDATORY |  | |
|19.|03.12|  | Metrics + Excel power pivot integration	|  Same slides as in the previous lectures	|
|20.|05.12| Lab H | MDX Queries	| Same slides as in the previous lectures	| 
|21.|09.12|Lab H  | MDX Queries	| Same slides as in the previous lectures	|
|22.|10.12|Lab H | MDX Queries	|  Same slides as in the previous lectures	|
|  |12.12|Lab H  | Strike |  |
|23.|17.12|Lab H  | PowerBI + MDX practice|  Same slides as in the previous lectures|



====== Exams ======

The exam of Decision Support Systems (801AA, 12 ECTS) consists of a written part and an oral part on the topics of the first module (50% of the final grade), and a lab project or written test with discussion on the topics of the second module (50% of the final grade). See [[mds:dsd:start|Module I: Decision Support Databases]] for the theory part. **The project of Module II can be discussed only after passing Module I and not later than one year since then.**

The lab project has to be submitted within the specified deadline that will be communicated asap.
If the project is not submitted within the deadline, a practical test and a discussion on the topics of the second module will be required for the exam instead.


**PROJECT **

A project consists of a set of assignments corresponding to a BI process: data integration, construction of an OLAP cube, querying of the OLAP cube, and reporting.

The project has to be performed by a team of 3 students.
  * Project assignments: {{ :mds:lbi:lds_project_2025-2026_-_20251218.pdf | LDS Project 2025-26 **Updated dec 18th 2025**}}
  * Data: [[https://unipiit-my.sharepoint.com/:u:/g/personal/c_landi7_studenti_unipi_it/EZPEZTUo4ZBDqAbPfp3uGXUBFph9TafaN7o4oSlrz6GE6w?e=EZCAYM|Data]]




===== Exam sessions =====

Registration to the written exam is mandatory (**pay attention at the deadline for registering!**): [[https://esami.unipi.it/esami2/|register here]]\\

Please indicate in the notes "Only Lab" for doing only the discussion of the lab project; "Only DSD" for doing only the written+oral part of the DSD module; or "DSD+Lab" for doing both.

**Important:** The date of the discussion of the lab project will be communicated to you by the instructors. The dates reported in the
[[https://esami.unipi.it/esami2/|registration website]] refear **only** the written part of the DSD module.

**Oral Exam Date for LDS during the Winter Sessions:** TBD
 
=====Past Editions =====
  * [[LDS 2024-2025]]
  * [[LDS 2023-2024]]
  * [[LDS 2022-2023]]
  * [[LDS 2021-2022]]
  * [[LDS 2020-2021]]
  * [[LDS 2019-2020]]
  * [[LDS 2018-2019]]
  * [[LBI 2017-2018]]