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PORTALE DELLA DIDATTICA

Data management and visualization

01TXASM

A.A. 2019/20

Course Language

English

Course degree

Master of science-level of the Bologna process in Data Science And Engineering - Torino

Course structure
Teaching Hours
Lezioni 63.5
Esercitazioni in laboratorio 16.5
Tutoraggio 56.5
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Chiusano Silvia Anna Professore Associato ING-INF/05 25 0 0 0 1
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 8 B - Caratterizzanti Ingegneria informatica
2019/20
The course, compulsory for the Master degree in Data science and Engineering, is offered on the 1st semester of the 1st year. The course introduces database management techniques for data warehouses (database systems specialized in strategic decision support), typically characterized by the need of managing very large databases. It addresses the fundamental issues in the technology of relational and NoSQL database management systems for very large data collections. The ability to visually present information correctly and effectively is a fundamental aspect not only in the engineering and scientific fields, but it represents an essential skill in communication in general. The course covers also the fundamental theoretical and methodological techniques for the effective management of quantitative (mainly numerical) information, which are used to effectively visualize and inspect input data and compute the KPIs by means of data warehouses. Laboratory sessions allow experimental activities, both on technological characteristics and data analysis and visualization, on widespread commercial and open-source products.
The course, compulsory for the Master degree in Data science and Engineering, is offered on the 1st semester of the 1st year. The course introduces database management techniques for data warehouses (database systems specialized in strategic decision support), typically characterized by the need of managing very large databases. It addresses the fundamental issues in the technology of relational and NoSQL database management systems for very large data collections. The ability to visually present information correctly and effectively is a fundamental aspect not only in the engineering and scientific fields, but it represents an essential skill in communication in general. The course covers also the fundamental theoretical and methodological techniques for the effective management of quantitative (mainly numerical) information, which are used to effectively visualize and inspect input data and compute the KPIs by means of data warehouses. Laboratory sessions allow experimental activities, both on technological characteristics and data analysis and visualization, on widespread commercial and open-source products.
- 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 by means of the SQL language. - Knowledge of the main technological characteristics of NoSQL databases. - Ability to design the conceptual model and define the physical data structures for NoSQL databases. - Ability to design dashboards and KPIs - Knowledge of the basic principles of cognitive and perceptive aspects related to visualization, and knowledge of the main visualization techniques. - Ability to design and develop simple systems for visualizing quantitative information.
- 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 by means of the SQL language. - Knowledge of the main technological characteristics of NoSQL databases. - Ability to design the conceptual model and define the physical data structures for NoSQL databases. - Ability to design dashboards and KPIs - Knowledge of the basic principles of cognitive and perceptive aspects related to visualization, and knowledge of the main visualization techniques. - Ability to design and develop simple systems for visualizing quantitative information.
• Knowledge of the relational model and SQL language and basic programming skills.
• Knowledge of the relational model and SQL language and basic programming skills.
• Data cleaning and data integration (0.6 cr.) • Data warehouses: architecture, methodology for conceptual, logical, and physical design, SQL statements for OLAP queries (3 cr.) • NoSQL databases: Conceptual modeling, technological characteristics, and query languages (2.4 cr.) • Cognitive aspects of visualization and visual integrity principles (1 cr.) • Data visualization tools (1 cr.)
• Data cleaning and data integration (0.6 cr.) • Data warehouses: architecture, methodology for conceptual, logical, and physical design, SQL statements for OLAP queries (3 cr.) • NoSQL databases: Conceptual modeling, technological characteristics, and query languages (2.4 cr.) • Cognitive aspects of visualization and visual integrity principles (1 cr.) • Data visualization tools (1 cr.)
The course includes practices on the lecture topics, and in particular conceptual, logical, and physical data warehouse design, extended SQL language, and NoSQL database design and query (6 cr.), and data visualization (2 cr.). Students will prepare individual written reports on exercises proposed during the course. Reports will contribute to the final exam grade. The course includes laboratory sessions on data warehouse design, extended SQL language, NoSQL database design and query, and data visualization. Laboratory sessions allow experimental activities on the most widespread commercial and open-source products.
The course includes practices on the lecture topics, and in particular conceptual, logical, and physical data warehouse design, extended SQL language, and NoSQL database design and query (6 cr.), and data visualization (2 cr.). Students will prepare individual written reports on exercises proposed during the course. Reports will contribute to the final exam grade. The course includes laboratory sessions on data warehouse design, extended SQL language, NoSQL database design and query, and data visualization. Laboratory sessions allow experimental activities on the most widespread commercial and open-source products.
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 teaching Portal. Reference books: • Matteo Golfarelli, Stefano Rizzi. Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill Education, 2009 • Kristina Chodorow, Shannon Bradshaw. MongoDB: The Definitive Guide (Powerful and Scalable Data Storage), 3 ed. O'Reilly Media, 2018. • Stephen Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten, 2nd Edition. Analytics Press, 2012 • Edward R. Tufte. The Visual Display of Quantitative Information. Graphics Press, 1983.
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 teaching Portal. Reference books: • Matteo Golfarelli, Stefano Rizzi. Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill Education, 2009 • Kristina Chodorow, Shannon Bradshaw. MongoDB: The Definitive Guide (Powerful and Scalable Data Storage), 3 ed. O'Reilly Media, 2018. • Stephen Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten, 2nd Edition. Analytics Press, 2012 • Edward R. Tufte. The Visual Display of Quantitative Information. Graphics Press, 1983.
Modalità di esame: prova scritta; elaborato scritto individuale;
Exam: individual practice assignments; written test. The exam includes a written part and the evaluation of the reports on the individual practices assigned during the course. The written part lasts 2 hours. The final score is defined by considering the evaluation of the written part and of the individual practices. The individual practices are considered only if the grade of the written part is 18 or above. The written part includes - theory questions on the main course topics (conceptual, logical, and physical data warehouse design, extended SQL language, technological characteristics of NoSQL databases, data visualization techniques) - 1 exercise on data warehousing, including the design of a data warehouse and SQL queries for data access - 1 exercise on NoSQL database design and queries for data access - 1 exercise on visualization analysis and design 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.
Exam: written test; individual essay;
Exam: individual practice assignments; written test. The exam includes a written part and the evaluation of the reports on the individual practices assigned during the course. The written part lasts 2 hours. The final score is defined by considering the evaluation of the written part and of the individual practices. The individual practices are considered only if the grade of the written part is 18 or above. The written part includes - theory questions on the main course topics (conceptual, logical, and physical data warehouse design, extended SQL language, technological characteristics of NoSQL databases, data visualization techniques) - 1 exercise on data warehousing, including the design of a data warehouse and SQL queries for data access - 1 exercise on NoSQL database design and queries for data access - 1 exercise on visualization analysis and design 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.


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