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Data Science and Database Technology

01SQJOV

A.A. 2020/21

2020/21

Data Science and Database Technology

The course is taught in English. The course, compulsory for the Master degree in Computer Engineering, is offered on the 1st semester of the 1st year. The course addresses the fundamental issues in the technology of database management systems and introduces database management techniques for data warehouses (database systems specialized in strategic decision support), typically characterized by the need of managing very large databases. Both traditional OLAP (On Line Analytical Processing) analysis techniques and complex data mining techniques will be addressed. Laboratory sessions allow experimental activities, both on technological characteristics and data analysis, on the most widespread commercial and open-source products.

Data Science and Database Technology

The course is taught in English. The course, compulsory for the Master degree in Computer Engineering, is offered on the 1st semester of the 1st year. The course addresses the fundamental issues in the technology of database management systems and introduces database management techniques for data warehouses (database systems specialized in strategic decision support), typically characterized by the need of managing very large databases. Both traditional OLAP (On Line Analytical Processing) analysis techniques and complex data mining techniques will be addressed. Laboratory sessions allow experimental activities, both on technological characteristics and data analysis, on the most widespread commercial and open-source products.

Data Science and Database Technology

- Knowledge of the main technological characteristics of a database management system: concurrent data access management, reliability, physical level structures, data access optimization. - Ability to design the physical data structures for a relational database. - Knowledge of distributed database system architecture and replication management. - Knowledge of active database systems and SQL statements for trigger definition. - Ability to write triggers in the SQL language. - 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.

Data Science and Database Technology

- Knowledge of the main technological characteristics of a database management system: concurrent data access management, reliability, physical level structures, data access optimization. - Ability to design the physical data structures for a relational database. - Knowledge of distributed database system architecture and replication management. - Knowledge of active database systems and SQL statements for trigger definition. - Ability to write triggers in the SQL language. - 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.

Data Science and Database Technology

Knowledge of the relational model and SQL language and basic programming skills.

Data Science and Database Technology

Knowledge of the relational model and SQL language and basic programming skills.

Data Science and Database Technology

- Technological characteristics of a database management system: concurrent data access management, reliability, physical level structures, data access optimization (1.8 cr.) - Active database systems and SQL statements for trigger definition (0.4 cr.) - Distributed database system architecture and replication management (0.4 cr.) - Data warehouses: architecture, methodology for conceptual, logical, and physical design, SQL statements for OLAP queries (1.4 cr.) - Data mining algorithms: classification, clustering, and association rule mining (1.6 cr.)

Data Science and Database Technology

- Technological characteristics of a database management system: concurrent data access management, reliability, physical level structures, data access optimization (1.8 cr.) - Active database systems and SQL statements for trigger definition (0.4 cr.) - Distributed database system architecture and replication management (0.4 cr.) - Data warehouses: architecture, methodology for conceptual, logical, and physical design, SQL statements for OLAP queries (1.4 cr.) - Data mining algorithms: classification, clustering, and association rule mining (1.6 cr.)

Data Science and Database Technology

Data Science and Database Technology

Data Science and Database Technology

The course includes practices on the lecture topics, and in particular SQL language, physical database design, and conceptual, logical, and physical data warehouse design (1.8 cr.). 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 (also for database physical design) and data warehouse design (1.2 cr.). Laboratory sessions allow experimental activities on the most widespread commercial and open-source products.

Data Science and Database Technology

The course includes practices on the lecture topics, and in particular SQL language, physical database design, and conceptual, logical, and physical data warehouse design (1.8 cr.). 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 (also for database physical design) and data warehouse design (1.2 cr.). Laboratory sessions allow experimental activities on the most widespread commercial and open-source products.

Data Science and Database Technology

Reference books: - Atzeni, Ceri, Paraboschi, Torlone, 'Database systems', 1 ed., McGraw Hill, 1999. - 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.

Data Science and Database Technology

Reference books: - Atzeni, Ceri, Paraboschi, Torlone, 'Database systems', 1 ed., McGraw Hill, 1999. - 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.

Data Science and Database Technology

Modalità di esame: Prova scritta a risposta aperta o chiusa tramite PC con l'utilizzo della piattaforma di ateneo Exam integrata con strumenti di proctoring (Respondus); Elaborato progettuale individuale;

Data Science and Database Technology

The exam includes a written part and the evaluation of reports on individual exercises assigned during the course. The individual exercises are optional. Learning objectives assessment The written part will assess by means of design exercises - the ability to design the physical structure of a database - the ability to write triggers in the SQL language - the ability to design a data warehouse - the ability to write OLAP queries in the SQL language The written part will assess by means of theory questions and exercises - the knowledge of the main technological characteristics of a database management system (concurrent data access, reliability) - the knowledge of the main technological characteristics of distributed database systems - the knowledge of the major data mining algorithms for classification, clustering, and association rule mining. Exam structure and grading criteria The exam includes a written part and the evaluation of reports on individual practices assigned during the course. The individual practices are optional. The written part lasts 80 minutes. The final score is defined by considering the evaluation of the written part, and, optionally, 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 box-to-fill and multiple choice questions. For multiple choice questions, wrong answers are penalized. Missing answers are evaluated zero. Textbooks, notes, electronic devices of any kind are not allowed. Structure and topics of the written part. - 4-6 questions on the main topics of the course (technological characteristics of a database management system, distributed database systems, data preparation, data analytics algorithms) (max 6 points) - 1-3 exercises on physical design (max 5 points) - 1-2 exercises on trigger design (max. 9 points) - 1-3 exercises on data warehouse design (max. 3 points) - 2-3 exercises on SQL queries for data access in a data warehouse (max. 8 points) The score of each question will be specified in the exam text. Exercises are evaluated according to the correctness of the proposed solution and to the appropriateness of the adopted resolution methodologies. Reports on the individual practices are assigned and must be delivered at predefined deadlines during the course. They deal with the main topics of the lectures (max 2 points).

Data Science and Database Technology

Exam: Computer-based written test with open-ended questions or multiple-choice questions using the Exam platform and proctoring tools (Respondus); Individual project;

Data Science and Database Technology

The exam includes a written part and the evaluation of reports on individual exercises assigned during the course. The individual exercises are optional. Learning objectives assessment The written part will assess by means of design exercises - the ability to design the physical structure of a database - the ability to write triggers in the SQL language - the ability to design a data warehouse - the ability to write OLAP queries in the SQL language The written part will assess by means of theory questions and exercises - the knowledge of the main technological characteristics of a database management system (concurrent data access, reliability) - the knowledge of the main technological characteristics of distributed database systems - the knowledge of the major data mining algorithms for classification, clustering, and association rule mining. Exam structure and grading criteria The exam includes a written part and the evaluation of reports on individual practices assigned during the course. The individual practices are optional. The written part lasts 80 minutes. The final score is defined by considering the evaluation of the written part, and, optionally, 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 box-to-fill and multiple choice questions. For multiple choice questions, wrong answers are penalized. Missing answers are evaluated zero. Textbooks, notes, electronic devices of any kind are not allowed. Structure and topics of the written part. - 4-6 questions on the main topics of the course (technological characteristics of a database management system, distributed database systems, data preparation, data analytics algorithms) (max 6 points) - 1-3 exercises on physical design (max 5 points) - 1-2 exercises on trigger design (max. 9 points) - 1-3 exercises on data warehouse design (max. 3 points) - 2-3 exercises on SQL queries for data access in a data warehouse (max. 8 points) The score of each question will be specified in the exam text. Exercises are evaluated according to the correctness of the proposed solution and to the appropriateness of the adopted resolution methodologies. Reports on the individual practices are assigned and must be delivered at predefined deadlines during the course. They deal with the main topics of the lectures (max 2 points).

Data Science and Database Technology

Modalità di esame: Test informatizzato in laboratorio; Prova scritta a risposta aperta o chiusa tramite PC con l'utilizzo della piattaforma di ateneo Exam integrata con strumenti di proctoring (Respondus); Elaborato progettuale individuale;

Data Science and Database Technology

The exam includes a written part and the evaluation of reports on individual exercises assigned during the course. The individual exercises are optional. Learning objectives assessment The written part will assess by means of design exercises - the ability to design the physical structure of a database - the ability to write triggers in the SQL language - the ability to design a data warehouse - the ability to write OLAP queries in the SQL language The written part will assess by means of theory questions and exercises - the knowledge of the main technological characteristics of a database management system (concurrent data access, reliability) - the knowledge of the main technological characteristics of distributed database systems - the knowledge of the major data mining algorithms for classification, clustering, and association rule mining. Exam structure and grading criteria The exam includes a written part and the evaluation of reports on individual practices assigned during the course. The individual practices are optional. The written part lasts 80 minutes. The final score is defined by considering the evaluation of the written part, and, optionally, 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 box-to-fill and multiple choice questions. For multiple choice questions, wrong answers are penalized. Missing answers are evaluated zero. Textbooks, notes, electronic devices of any kind are not allowed. Structure and topics of the written part. - 4-6 questions on the main topics of the course (technological characteristics of a database management system, distributed database systems, data preparation, data analytics algorithms) (max 6 points) - 1-3 exercises on physical design (max 5 points) - 1-2 exercises on trigger design (max. 9 points) - 1-3 exercises on data warehouse design (max. 3 points) - 2-3 exercises on SQL queries for data access in a data warehouse (max. 8 points) The score of each question will be specified in the exam text. Exercises are evaluated according to the correctness of the proposed solution and to the appropriateness of the adopted resolution methodologies. Reports on the individual practices are assigned and must be delivered at predefined deadlines during the course. They deal with the main topics of the lectures (max 2 points).

Data Science and Database Technology

Exam: Computer lab-based test; Computer-based written test with open-ended questions or multiple-choice questions using the Exam platform and proctoring tools (Respondus); Individual project;

Data Science and Database Technology

The exam includes a written part and the evaluation of reports on individual exercises assigned during the course. The individual exercises are optional. Learning objectives assessment The written part will assess by means of design exercises - the ability to design the physical structure of a database - the ability to write triggers in the SQL language - the ability to design a data warehouse - the ability to write OLAP queries in the SQL language The written part will assess by means of theory questions and exercises - the knowledge of the main technological characteristics of a database management system (concurrent data access, reliability) - the knowledge of the main technological characteristics of distributed database systems - the knowledge of the major data mining algorithms for classification, clustering, and association rule mining. Exam structure and grading criteria The exam includes a written part and the evaluation of reports on individual practices assigned during the course. The individual practices are optional. The written part lasts 80 minutes. The final score is defined by considering the evaluation of the written part, and, optionally, 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 box-to-fill and multiple choice questions. For multiple choice questions, wrong answers are penalized. Missing answers are evaluated zero. Textbooks, notes, electronic devices of any kind are not allowed. Structure and topics of the written part. - 4-6 questions on the main topics of the course (technological characteristics of a database management system, distributed database systems, data preparation, data analytics algorithms) (max 6 points) - 1-3 exercises on physical design (max 5 points) - 1-2 exercises on trigger design (max. 9 points) - 1-3 exercises on data warehouse design (max. 3 points) - 2-3 exercises on SQL queries for data access in a data warehouse (max. 8 points) The score of each question will be specified in the exam text. Exercises are evaluated according to the correctness of the proposed solution and to the appropriateness of the adopted resolution methodologies. Reports on the individual practices are assigned and must be delivered at predefined deadlines during the course. They deal with the main topics of the lectures (max 2 points).

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