Computing for Language Studies: Cultural Analytics
A.Y. 2023/2024
Learning objectives
The course aims to provide an introduction to computing techniques for the humanities and to help students tap into the potential of linked data for humanities research.
In particular, it aims to help students develop fundamental knowledge and skills related to computer tools for information systems design, querying digital sources and visual exploratory analysis. The tools introduced in the course will be used to collect, integrate, query and present information for research in the humanities. The course is also intended to foster the development of skills related to object-oriented design, communication tools and to the ability to efficiently interact with scholars and professionals in the field of information technology.
In particular, it aims to help students develop fundamental knowledge and skills related to computer tools for information systems design, querying digital sources and visual exploratory analysis. The tools introduced in the course will be used to collect, integrate, query and present information for research in the humanities. The course is also intended to foster the development of skills related to object-oriented design, communication tools and to the ability to efficiently interact with scholars and professionals in the field of information technology.
Expected learning outcomes
Knowledge:
- Basic knowledge of information representation and related processes
- Notions of data modelling
- Notions of information architecture and semantic web
- Notions of visual data exploration
Competences:
- Conceptual design of an information system and a database
- Querying, scraping and population of databases and semantic web
- Interpretation and visual communication of data
- Basic knowledge of information representation and related processes
- Notions of data modelling
- Notions of information architecture and semantic web
- Notions of visual data exploration
Competences:
- Conceptual design of an information system and a database
- Querying, scraping and population of databases and semantic web
- Interpretation and visual communication of data
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Second semester
Course syllabus
Part 1: Theory and Tools
1. Data, information, information systems - data storage, database management systems (DBMS), data and abstraction levels
2. Conceptual and logical design - entities and attributes, relationships between entities, cardinality of relationships, identifiers, generalisation hierarchies, methodological design guidelines
3. Creating and querying a database - the SQL language, defining and populating a database, referential integrity constraints, querying a database, selection conditions, table joins, sorting criteria
4. Data Semantics and Semantic Web - knowledge representation, introductory concepts, definitions and content, querying web sources via REST, JSON and XPath
5. Visual analysis - exploratory and explanatory, purposes and interpretations of spatial and iterative representation
Part 2: Practical project
Individual project within a collaborative research project on cultural exchange in Milan after World War II. Each project must contain: 1) a conceptual design part, 2) a part of scraping external sources and integration with data collected in one's own research, and 3) a part of visualisation of the integrated data.
1. Data, information, information systems - data storage, database management systems (DBMS), data and abstraction levels
2. Conceptual and logical design - entities and attributes, relationships between entities, cardinality of relationships, identifiers, generalisation hierarchies, methodological design guidelines
3. Creating and querying a database - the SQL language, defining and populating a database, referential integrity constraints, querying a database, selection conditions, table joins, sorting criteria
4. Data Semantics and Semantic Web - knowledge representation, introductory concepts, definitions and content, querying web sources via REST, JSON and XPath
5. Visual analysis - exploratory and explanatory, purposes and interpretations of spatial and iterative representation
Part 2: Practical project
Individual project within a collaborative research project on cultural exchange in Milan after World War II. Each project must contain: 1) a conceptual design part, 2) a part of scraping external sources and integration with data collected in one's own research, and 3) a part of visualisation of the integrated data.
Prerequisites for admission
No previous knowledge is required.
Teaching methods
The course contents will be delivered in lectures and programming exercises, with the support of slides and teaching materials that the lecturer will make available on the Ariel platform on a regular basis. Special emphasis is placed on practical case studies aimed at illustrating and developing first-hand experience with the specific applications of the theoretical topics covered during the course of the lectures.
Attendance, although not required, is strongly recommended. Students unable to attend are advised to contact the lecturer to agree on an alternative programme.
Attendance, although not required, is strongly recommended. Students unable to attend are advised to contact the lecturer to agree on an alternative programme.
Teaching Resources
- Slides and handouts from class
- Manovich, Maraschi. 2023. "Cultural analytics". MIT Press
- Castano, Ferrara, Montanelli. 2009. "Informazione, conoscenza e Web per le scienze umanistiche". Pearson
- Ward, Grinstein, Keim. 2015. "Interactive data visualization: foundations, techniques, and applications". CRC
- Moretti. 2022. "Falso movimento: la svolta quantitativa nello studio della letteratura". Nottetempo
- Manovich, Maraschi. 2023. "Cultural analytics". MIT Press
- Castano, Ferrara, Montanelli. 2009. "Informazione, conoscenza e Web per le scienze umanistiche". Pearson
- Ward, Grinstein, Keim. 2015. "Interactive data visualization: foundations, techniques, and applications". CRC
- Moretti. 2022. "Falso movimento: la svolta quantitativa nello studio della letteratura". Nottetempo
Assessment methods and Criteria
The assessment will consist of a project concerning a particular case study, which allows the student to work on a topic related to their training plan. The following will be assessed: possession and ability to put into practice the knowledge acquired during the course, completeness of the project modelling and the integrity of the reasoning carried out. Please refer to Part 2 of the programme for further details on the practical project.
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours
Professor:
Ruskov Martin Petkov
Educational website(s)