- 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 non-relational databases (NoSQL). - Ability to write queries on non-relational databases (NoSQL). - 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 main machine learning algorithms for classification, clustering, and association rule mining.

Knowledge of the relational model. Ability to design queries using relational algebra. Ability to design complex instructions using SQL language. Basic programming skills.

- Technological characteristics of a database management system: concurrent data access management, reliability, physical level structures, data access optimization (1.6 cfu) - NoSQL databases (MongoDB, Elastic): data model and query language (0.6 cfu) - Distributed database system architecture and replication management (0.4 cfu) - Data warehouses: architecture, methodology for conceptual, logical, and physical design, data preparation, SQL statements for OLAP queries (1.4 cfu) - Data science process (0.4 cfu) - Machine learning algorithms: classification, clustering, and association rule mining (1.2 cfu) - Analysis of case studies through classroom exercises and laboratories on the topics covered during lessons (2.4 cfu)

The course includes lessons, classroom exercises and laboratories on the covered topics. Classroom exercises focus on the lecture topics, and in particular on physical design of a relational database; design and query of NoSQL databases; conceptual, logical, and physical data warehouse design and related processes for data query and preparation based on SQL language (1.2 cfu). Students will have individual practices during the course. The course includes laboratory sessions on the SQL language (also for database physical design), data warehouse design, design of NoSQL databases and process for data analysis (1.2 cfu). Laboratory sessions allow experimental activities on the most widespread commercial and open-source products. Four individual homeworks will be proposed, mainly aimed at encouraging critical thinking and problem solving skills on the teaching topics. For the homework, written reports must be submitted. Carrying out the homework is optional. If delivered, the homework will contribute to the final grade.

Reference books: - Atzeni, Ceri, Fraternali, Paraboschi, Torlone, 'Basi di dati ', 5 ed., McGraw Hill, 2018. - Golfarelli, Rizzi, 'Data Warehouse Design: modern principles and methodologies', McGraw Hill, 2021. - Tan, Steinbach, Karpatne, Kumar, 'Introduction to data mining', 2 ed., Pearson, 2019. - Raghu Ramakrishnan and Johannes Gehrke. Database Management Systems. Third edition, McGraw Hill, 2003 - Dan Sullivan, NoSQL for Mere Mortals, Addison-Wesley Professional, 2015 - Kristina Chodorow, Shannon Bradshaw, MongoDB: The Definitive Guide (Powerful and Scalable Data Storage), 3 ed. O'Reilly Media, 2018 - Gormley, Tong, Elastic Search: The Definitive Guide, O’Reilly, 2015 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.

Lecture slides; Text book; Exercises; Exercise with solutions ; Lab exercises; Lab exercises with solutions; Video lectures (current year); Video lectures (previous years); Self-assessment tools;

You can take this exam before attending the course

Exam: Individual project; Computer-based written test in class using POLITO platform;

The exam includes a written part and the evaluation of the reports on the individual homework assigned during the course. The homework are optional. The teacher may request an integrative test to confirm the obtained evaluation. 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 design a data warehouse - the ability to define operations for data preparation in the SQL language - 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 ability to write queries for non-relational databases - the knowledge of the main technological characteristics of distributed database systems - the knowledge of the main machine learning 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 the homework assigned during the course. The homework are optional. The teacher may request an integrative test to confirm the obtained evaluation. The written part lasts 95 minutes. The final score is defined by considering the evaluation of the written part, and, optionally, of the reports on the homework. If the final score is strictly greater than 31 the registered score will be 30 with honor. The reports on the homework 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 during the written part. Structure and topics of the written part. - 6-8 questions on the main topics of the course (technological characteristics of a database management system, distributed database systems, data preparation, data analytics algorithms) (max 8 points) - 1-3 exercises on physical design (max 5 points) - 1-3 exercises on data warehouse design (max. 3 points) - 1 exercises on data preparation for data warehouse (max. 5 points) - 3 exercises on the design of SQL instructions for data access in a data warehouse (max. 11 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 optional homework are assigned and must be delivered at predefined deadlines during the course. They deal with the main topics of the lectures (max 2 points).

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.