Methodologies of Digital Humanities

PART A

DH - Introduction to Computer Methodologies in the Humanities (6 hours)
What is computer science in its disciplinary sense? We will discuss the object of study, information, introducing its theory and formalization as a measurable quantity. From here, we will see how information relates to computing machines through the notion of information encoding. The idea of a calculating machine will introduce us to the concept of automation and algorithmic problem-solving.
We will then discuss the definition of digital humanities, questioning the status of the humanities in relation to information technologies. We will examine the limits of a merely instrumental view of computer science in service of digitization and cataloging projects.
This will lead us to address the topic of generative artificial intelligence and how it changes the very epistemological status of information technologies in relation to cultural processes, as well as how this can alter the role of the humanist and the practices of humanistic disciplines.

PY - Programming Languages Applied to the Humanities (8 hours)
Here, we will delve into information technologies through the activity of programming an electronic computer. Understanding what a program is and how programming enables the automation of tasks and processes allows us to better grasp how problems and processes can be solved with an algorithmic approach. General concepts specific to different programming languages will be introduced, along with more specific notions related to the Python language. This module will have only a partial practical component, with numerous examples carried out in class by the instructor, but it will be possible to complement the classroom teaching with the Python lab activities offered by the degree program.
We will also address cases where programming can be successfully integrated into the research process in the humanities, discussing how this changes the perspective on sources and data.

DB - Data Management and Visualization (14 hours)
Data and its availability in large quantities are among the key enabling factors for recent technological advancements in the field of information science. We will first focus on understanding what data is and how it differs from information. Data science is primarily the process of transforming data into information, which involves the use of models that define relationships between data and place them in an interpretive context.
Through specific seminar meetings held during the course, we will delve into the topic of conceptual data modeling, learning the fundamental principles that govern the proper and effective handling of data.
We will then move on to the activity of data analysis and visualization, introducing some conceptual tools essential for understanding graphs and statistical reports.

TA - Principles and Tools for Textual and Linguistic Analysis (16 hours)
Text, especially written text, has always been a primary source in humanities research, but also in professions that rely on text, from communication to information, publishing to teaching. We will see how information technologies can greatly expand the tools available to humanists for analyzing the complexity and layering of written text. We will understand what a text is from the machine's perspective and how to extract information from it without having the linguistic skills and knowledge that a human reader possesses.
We will then understand how to turn text into an object of automation and what formal tasks machines can perform with textual material.
Based on these skills, we will examine some commonly used applications that utilize these methods, ranging from linguistic corpora databases to how our phones suggest completing messages, to search engines, and finally to the idea of machines generating language and text.

PART B

PR - Applied Projects in Humanities Studies (16 hours)
In the final part of the course, we will apply the skills acquired by carrying out some simple projects in the humanities using information technologies. In particular, we will introduce generative AI tools, discussing their use, limits, risks, and potential, and then use them to achieve objectives that require integrating humanistic knowledge with technological skills.
We will also discuss how to design and organize a study or research activity that actively and critically uses these technologies.