Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Academic calendar
The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
Period | From | To |
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Primo semestre | Oct 3, 2022 | Jan 27, 2023 |
Secondo semestre | Mar 6, 2023 | Jun 16, 2023 |
Exam calendar
Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.
Should you have any doubts or questions, please check the Enrolment FAQs
Academic staff
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.
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1° Year
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2° Year
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Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Programming and database (2022/2023)
Teaching code
4S009064
Credits
12
Coordinatore
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
The teaching is organized as follows:
Programming
Database
Credits
6
Period
Secondo semestre
Academic staff
Elisa Quintarelli
Learning objectives
The course is structured as follows [Programming Module] The purpose of the module is to provide skills and knowledge in programming in Python and R, giving the basic concepts of algorithm with particular reference to the use of the Python language (syntax, data structures, data import / export in Python, data visualization in Python) and its applications in data science [Database form] The course aims to provide the skills necessary for the design of data according to the requirements with reference to different application contexts and within the production process of software systems; for the management and effective and efficient use of data and for the study of a system for the management of relational databases in order to create, manage and query databases.
At the end of the course the student has to show to have acquired the following skills:
- ability to develop Python code to solve concrete examples
- ability to evaluate algorithms in terms of complexity in time and space
- knowledge of the syntax and semantics of the language used
- knowledge of the bases of: database management; architecture and functionality of a database management system; concepts of physical independence, logical independence, persistence, competition, reliability, query and updating of a database; advantages of a database management system compared to an operating system file system
- ability to conceptually design databases, e.g., conceptual models for data design; the Entity-Relationship (E-R) model; elements of the E-R model: entities, attributes, relationships, generalization hierarchies and cardinality constraints; the conceptual scheme of a database
- knowledge of the basics of the logical design of a database: data models for database management systems; the relational model; relationship definitions, integrity constraints and relationship scheme; the logical scheme of a database; rules for the translation of conceptual schemes into relationship schemes
- understanding of the mechanisms of interaction with a database: introduction to languages for the definition, modification and query of a database; relational algebra; optimization of algebra expressions; the SQL language; the selection construct (Select-From-Where), nested queries, sorting and grouping of data in SQL; the concept of sight.
Prerequisites and basic notions
Basic concepts of logic and of the notion of algorithm
Program
The course is structured in two parts:
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Programming
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1. Introduction to Software Development
- Procedural programming recap
- Jupyter Notebooks, Python
- Software product requirements analysis
2. Data analysis
- Loading structured and unstructured data
- Data manipulation with Numpy and Pandas
- Data visualization with Matplotlib
3. Scientific computing
- NumPy and overall Python open-source ecosystem
- Introduction to Sklearn
4. Object-oriented programming
- OOP fundamentals
- OOP in open-source products
- Redesign of procedural programming
5. Operationalization
- Operationalization with Notebooks
- Operationalization with Streamlit
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Database
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1. Introduction to database management systems (DBMS): architectures and functionalities of a DBMS. Physical and logical data independence. Data models. Concepts of model, schema and instance of a database. Languages for database systems. DBMS vs. file system. Information Systems and Data Science.
2. The relational theory: the model and the algebra.
3. Interacting with a database system: languages for the definition, querying and update of a database. SQL: select-from-where statement, join in SQL, the GROUP BY and ORDER BY clauses, using subqueries. Views.
4. Conceptual database design: conceptual data models. The Entity-Relationship model (ER). Elements of the ER model: entities, attributes, relationships, ISA hierarchies and cardinality constraints. Logical database design: logical data models, the relational data model. Elements of the relational data models: relations and integrity constraints. Mapping between conceptual schemas in ER model and logical schema in the relational model.
5. Decision support systems.
1. Datawarehouse systems
2. Designing Data Warehouse on integrated data (GAV and LAV approaches)
3. OLAP queries
6. Beyond the Relation Model: main differences with NO-SQL Models
Bibliography
Didactic methods
In-person classes and practical sessions
Learning assessment procedures
The exam consists of a written test (2 hours), possibly supplemented by an oral, containing some open questions about theory concepts, an exercise about the conceptual modeling (using the E-R model or DFM schema) and the logical modeling (using the relational model) of a database, and some exercises about the specification of queries in SQL on a given database.
Evaluation criteria
To pass the exam, the students must show that:
- they have understood the concepts related to the theory of relational databases and data warehouses and their design;
- they are able to describe the concepts in a clear and exhaustive way;
- they are able to apply the acquired knowledge to solve application scenarios described by means of questions and exercises.
The written exam will be evaluated with at most 33 points (30 cum Laude).
Criteria for the composition of the final grade
The final mark will be the average of the marks obtained in the Programming and Database parts.
Exam language
Inglese
Type D and Type F activities
Le attività formative di tipologia D sono a scelta dello studente, quelle di tipologia F sono ulteriori conoscenze utili all’inserimento nel mondo del lavoro (tirocini, competenze trasversali, project works, ecc.). In base al Regolamento Didattico del Corso, alcune attività possono essere scelte e inserite autonomamente a libretto, altre devono essere approvate da apposita commissione per verificarne la coerenza con il piano di studio. Le attività formative di tipologia D o F possono essere ricoperte dalle seguenti attività.
1. Insegnamenti impartiti presso l'Università di Verona
Comprendono gli insegnamenti sotto riportati e/o nel Catalogo degli insegnamenti (che può essere filtrato anche per lingua di erogazione tramite la Ricerca avanzata).
Modalità di inserimento a libretto: se l'insegnamento è compreso tra quelli sottoelencati, lo studente può inserirlo autonomamente durante il periodo in cui il piano di studi è aperto; in caso contrario, lo studente deve fare richiesta alla Segreteria, inviando a carriere.scienze@ateneo.univr.it il modulo nel periodo indicato.
2. Attestato o equipollenza linguistica CLA
Oltre a quelle richieste dal piano di studi, per gli immatricolati dall'A.A. 2021/2022 vengono riconosciute:
- Lingua inglese: vengono riconosciuti 3 CFU per ogni livello di competenza superiore a quello richiesto dal corso di studio (se non già riconosciuto nel ciclo di studi precedente).
- Altre lingue e italiano per stranieri: vengono riconosciuti 3 CFU per ogni livello di competenza a partire da A2 (se non già riconosciuto nel ciclo di studi precedente).
Tali cfu saranno riconosciuti, fino ad un massimo di 6 cfu complessivi, di tipologia F se il piano didattico lo consente, oppure di tipologia D. Ulteriori crediti a scelta per conoscenze linguistiche potranno essere riconosciuti solo se coerenti con il progetto formativo dello studente e se adeguatamente motivati.
Gli immatricolati fino all'A.A. 2020/2021 devono consultare le informazioni che si trovano qui.
Modalità di inserimento a libretto: richiedere l’attestato o l'equipollenza al CLA e inviarlo alla Segreteria Studenti - Carriere per l’inserimento dell’esame in carriera, tramite mail: carriere.scienze@ateneo.univr.it
3. Competenze trasversali
Scopri i percorsi formativi promossi dal TALC - Teaching and learning center dell'Ateneo, destinati agli studenti regolarmente iscritti all'anno accademico di erogazione del corso https://talc.univr.it/it/competenze-trasversali
Modalità di inserimento a libretto: non è previsto l'inserimento dell'insegnamento nel piano di studi. Solo in seguito all'ottenimento dell'Open Badge verranno automaticamente convalidati i CFU a libretto. La registrazione dei CFU in carriera non è istantanea, ma ci saranno da attendere dei tempi tecnici.
4. Periodo di stage/tirocinio
Oltre ai CFU previsti dal piano di studi (verificare attentamente quanto indicato sul Regolamento Didattico): qui informazioni su come attivare lo stage.
Insegnamenti e altre attività che si possono inserire autonomamente a libretto
Modules not yet included
Career prospects
Module/Programme news
News for students
There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.
Further services
I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.
Graduation
Attachments
Title | Info File |
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387 KB, 27/04/22 |
List of theses and work experience proposals
theses proposals | Research area |
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Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games |
Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video) |
Domain Adaptation | Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION |
Domain Adaptation | Computing methodologies - Machine learning |
Attendance
As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.Please refer to the Crisis Unit's latest updates for the mode of teaching.