Methodologies of Digital Humanities
A.Y. 2024/2025
Learning objectives
- Provide students with an in-depth understanding of IT methodologies applied to the humanities.
- Develop practical skills in the use of programming languages and IT tools relevant to the analysis of humanistic data.
- Promote students' ability to apply IT concepts in their disciplines of study.
- Encourage collaboration between students to develop application projects that integrate IT methodologies and humanistic disciplines.
- Develop practical skills in the use of programming languages and IT tools relevant to the analysis of humanistic data.
- Promote students' ability to apply IT concepts in their disciplines of study.
- Encourage collaboration between students to develop application projects that integrate IT methodologies and humanistic disciplines.
Expected learning outcomes
At the end of the course, students will be able to:
- Apply fundamental IT concepts to the humanities;
- Use programming languages for the analysis and management of humanistic data;
- Implement textual and linguistic analysis tools in their research projects;
- Design and manage databases to support humanities research;
- Effectively present analysis results through data visualization tools;
- Collaborate with colleagues in the development of projects that integrate IT methodologies and humanistic disciplines.
- Apply fundamental IT concepts to the humanities;
- Use programming languages for the analysis and management of humanistic data;
- Implement textual and linguistic analysis tools in their research projects;
- Design and manage databases to support humanities research;
- Effectively present analysis results through data visualization tools;
- Collaborate with colleagues in the development of projects that integrate IT methodologies and humanistic disciplines.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
Introduction to the Digital Humanities
- Introduction to the history of digital humanities
- Overview of the use of technology in the humanities
- Basic concepts of computer science and their application in the humanities
Programming languages applied to the humanities
- Introduction to programming
- Python Fundamentals
- Practical applications in humanistic data processing and analysis
Data management and visualization
- The notion of data, types of data and data management systems
- Principles of data design and modeling
- Design and creation of databases for humanities projects
Principles and tools of textual and linguistic analysis
- Notions of statistics and introduction to corpus linguistics
- Computer methods for text analysis in the humanities
- Techniques for extracting and analyzing information from texts
- Introduction to linguistic analysis tools
Application projects in the field of humanistic studies
- Overview of humanities projects: requirements, challenges and objectives
- Development of projects that integrate IT methodologies with humanistic disciplines
- Presentation and discussion of completed projects
- Introduction to the history of digital humanities
- Overview of the use of technology in the humanities
- Basic concepts of computer science and their application in the humanities
Programming languages applied to the humanities
- Introduction to programming
- Python Fundamentals
- Practical applications in humanistic data processing and analysis
Data management and visualization
- The notion of data, types of data and data management systems
- Principles of data design and modeling
- Design and creation of databases for humanities projects
Principles and tools of textual and linguistic analysis
- Notions of statistics and introduction to corpus linguistics
- Computer methods for text analysis in the humanities
- Techniques for extracting and analyzing information from texts
- Introduction to linguistic analysis tools
Application projects in the field of humanistic studies
- Overview of humanities projects: requirements, challenges and objectives
- Development of projects that integrate IT methodologies with humanistic disciplines
- Presentation and discussion of completed projects
Prerequisites for admission
Previous programming experience is not necessary, as the course includes an introductory section on programming, in particular on the Python language, nor any other specific skills other than motivation and interest in the application of IT methodologies in the humanistic fields.
Teaching methods
The course is given in the form of lectures with extensive use of examples and support materials such as Python notebooks. Slides and handouts are employed throughout the lectures and they are progressively published on the reference course website on the Ariel platform and on the GitHub repository (https://github.com/afflint/midh).
Teaching Resources
The course mainly uses notes, notebooks and materials provided by the teacher and published on the Ariel web site. For further information, it is possible to integrate these materials with some suggested readings:
- The Python Tutorial, available online at https://docs.python.org/3/tutorial/index.html
- Michael J Hernandez, Database Design for Mere Mortals, Addison-Wesley Professional; 4th edition (December 17, 2020)
- Eric Matthes, Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming, No Starch Press; 3rd edition (January 10, 2023)
- J. Glenn Brookshear, Dennis Brylow, Computer Science: An Overview, Pearson; 13th edition (March 13, 2018)
- Ekaterina Kochmar, Getting Started with Natural Language Processing, Manning (October 18, 2022)
- The Python Tutorial, available online at https://docs.python.org/3/tutorial/index.html
- Michael J Hernandez, Database Design for Mere Mortals, Addison-Wesley Professional; 4th edition (December 17, 2020)
- Eric Matthes, Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming, No Starch Press; 3rd edition (January 10, 2023)
- J. Glenn Brookshear, Dennis Brylow, Computer Science: An Overview, Pearson; 13th edition (March 13, 2018)
- Ekaterina Kochmar, Getting Started with Natural Language Processing, Manning (October 18, 2022)
Assessment methods and Criteria
The exam takes place in written form and covers the entire programme. It lasts approximately one hour. The test consists of part A with closed-ended questions and part B with exercises and/or open questions. In order to take the test it is necessary to register for one of the exam sessions indicated on the calendar. The written test is graded out of thirty and takes into account the following parameters: degree of knowledge of the topics, ability to apply knowledge to solve exercises, completeness of answers, correctness of reasoning in carrying out the exercises. If you achieve a grade higher than or equal to 25/30, you must take an oral discussion to integrate the grade of the written test. If a grade lower than 25/30 is obtained, the oral discussion is NOT possible.
INF/01 - INFORMATICS - University credits: 9
Lessons: 60 hours
Professors:
Castano Silvana, Ferrara Alfio
Professor(s)
Reception:
On appointment. The meeting will be online until the end of the Covid emergency
Department of Computer Science, via Celoria 18 Milano, Room 7012 (7 floor)