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Politecnico di Torino
Academic Year 2017/18
01RLBPG, 01RLBNG
Business intelligence for big data
Master of science-level of the Bologna process in Engineering And Management - Torino
Master of science-level of the Bologna process in Mathematical Engineering - Torino
Teacher Status SSD Les Ex Lab Tut Years teaching
Cerquitelli Tania ORARIO RICEVIMENTO A2 ING-INF/05 50 9 21 0 7
SSD CFU Activities Area context
ING-INF/05 8 F - Altre attivitΰ (art. 10) Altre conoscenze utili per l'inserimento nel mondo del lavoro
Esclusioni:
02ENZ; 01QKL; 01PDZ; 01PQT; 03BYK; 19AKS; 03BNT; 02HEF; 03AQR; 05ENL; 01JEF; 01RLF; 01PCE; 01RLG; 01KSU; 01NNE; 01QGD
Subject fundamentals
The course, which is an optional choice for the Master degree in Industrial and Management Engineering, is offered on the 2nd semester of the 2nd year. Business intelligence for big data means a variety of data analytics activities to effectively support the business decision making. The course introduces techniques for storing, managing, analyzing and querying large data collections. Such databases, denoted data warehouses, are exploited to support strategic decision support. Relational and non relational database technologies, together with the traditional OLAP (On Line Analytical Processing) analysis techniques and complex data mining techniques will be addressed. Laboratory sessions allow experimental activities on data analysis and exploit the most widespread commercial and open-source products.
Expected learning outcomes
• Knowledge of data warehouse architecture and of the methodology for conceptual, logical, and physical design of a data warehouse.
• Ability to design a data warehouse.
• Knowledge of the SQL statements for OLAP queries in a data warehouse.
• Ability to write OLAP queries in the SQL language.
• Knowledge of the major data mining algorithms for classification, clustering, and association rule mining.
• Knowledge of data analysis techniques applied to CRM (Customer Relationship Management).
• Ability to perform data analysis by means of data mining techniques.
Prerequisites / Assumed knowledge
Knowledge of the relational model and SQL language and basic programming skills.
Contents
• Data warehouses: architecture, methodology for conceptual, logical, and physical design, SQL statements for OLAP queries (1.5 cfu)
• Non relational databases (0.3 cfu)
• Data mining algorithms: classification, clustering, and association rule mining (1.8 cfu)
• Data analysis techniques for CRM (Customer Relationship Management) (0.2 cfu)
• Applicative case studies for data warehouse design and data analysis (0.6 cfu).
Delivery modes
The course includes practices on the lecture topics, and in particular SQL language, and conceptual, logical, and physical data warehouse design (1.6 cfu). Students will prepare an individual written report on exercises proposed during the course. The report will contribute to the final exam grade. The course includes laboratory sessions on the SQL language. data warehouse design, and data mining techniques. The laboratory sessions entail the development of a complete data warehouse design based on case studies (2.0 cfu). Laboratory sessions allow experimental activities on the most widespread commercial and open-source products.
Texts, readings, handouts and other learning resources
Reference books:
- Golfarelli, Rizzi, "Data warehouse: teoria e pratica della progettazione", 2 ed., McGraw Hill, 2006.
- Tan, Steinbach, Kumar, "An introduction to data mining", Addison Wesley, 2005.

Copies of the slides used during the lectures, examples of written exams and exercises, and manuals for the activities in the laboratory will be made available. All teaching material is downloadable from the course website or the Portal.
Assessment and grading criteria
The exam includes a written part, the evaluation of the reports on the individual practices assigned during the course, and an oral part on a data analytics project on a real dataset developed in a team of two students. The individual practices are optional. The written part lasts 2 hours. The final score is defined by considering the evaluation of the written part, and, optionally, of the individual practices, and the oral part on the data analytics project. The individual practices are considered only if the grade of the written part is 18 or above. The final grade is the (approximated) average computed on the grade on the written part, the evaluation of the report on the individual practices, and the grade on the oral part.

The written part includes:
- 1 exercise on data warehousing, including the conceptual and logical design of a data warehouse (max 12 points)
- 3 queries for data access through the extended SQL language (max 15 points)
- 1 exercise on physical design of a data warehouse (max 2 points)
- discussion of the data warehouse issue related to the slowly changing dimension (max 1 point)
Students can use textbooks or notes during the exam. Exercises are evaluated according to the correctness of the proposed solution and to the appropriateness of the adopted resolution methodologies.

The oral part includes the presentation of the developed team project on data analytics and questions on the main topics of the lectures (max 30 with honors). Reports on the individual practices assigned during the course are on the main topics of the lectures (max 2 points).

Programma definitivo per l'A.A.2017/18
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