INTRODUCTION TO COMPUTER SCIENCE AND PROGRAMMING MOD 2

Module 2 deepens Python programming skills, focusing on advanced data structures, external data handling, and the use of libraries specific to Digital and Public Humanities.

By the end of Module 2, students will be able to:
- Understand and use compound data types in Python, such as lists, dictionaries, and tuples, to represent and manipulate complex data typical of Digital Humanities.
- Apply iterators and list comprehensions to write more efficient and readable Python code.
- Develop and organize modular programs through the advanced use of functions, modules, and packages.
- Manage external data by reading and writing text and CSV files, integrating them into analysis workflows.
- Use regular expressions for advanced text processing and data cleaning in digital and archival contexts.

Knowledge acquired during Module 1.
Basic computing knowledge (basic functions of the operating system, web browsing, e-mail, Office working suites).

1. Data Structures in Python
- Compound data types: lists, dictionaries, tuples
- Iterators and list comprehensions
2. Interacting with Data Structures
- Advanced use of functions and modules
3. Working with External Data
- Reading and writing files (text, CSV)
Using regular expressions (Python re module) for text processing
4. Introduction to Libraries for Data Handling in Digital Humanities
- Overview of libraries and practical examples

Material and slides is given by the teacher. References from Module 1.

There will be regular assignments throughout the course to support learning.

The exam for Digital and Public Humanities students of Modules 1 and 2 is a single 12-credit exam, graded on a scale of 30, with 18 as the minimum passing grade.
The written exam is primarily based on programming exercises that reflect the topics and activities covered throughout the two course modules. It will be conducted using students’ personal computers and the programming environment adopted during the course (Google Colaboratory).

written

Grading Criteria:
- Grade below 18/30: Exam not passed: Insufficient knowledge and understanding of the course topics; inadequate ability to apply knowledge in solving programming exercises.
- Grades in the range 18–22/30: Sufficient: Sufficient knowledge and understanding of the topics covered in the syllabus; basic level of participation in class and home activities; correct but limited ability to complete programming exercises.
- Grades in the range 23–25/30: Fair/Satisfactory: Fair knowledge and understanding of the course topics; decent participation and commitment in class and home activities; adequate ability to solve programming exercises, with minor inaccuracies.
- Grades in the range 26–28/30: Good: Good knowledge and understanding of the topics covered in the syllabus; good level of participation and completion of class and home activities; correct and efficient resolution of programming exercises.
- Grades in the range 29–30/30 cum laude: Very Good / Excellent: In-depth and autonomous knowledge and understanding of the course topics; excellent participation in class and home activities; strong mastery in solving programming exercises, including more complex tasks or non-standard variations.

The course is blended. There will be interactive frontal classes for acquiring methodologies and theoretical concepts, and online lessons consisting of assisted practical exercises, assisted by the teacher.
The course will use tools available in Moodle (chat, wiki, workshop, peer-review) and other complementary online services.
Use of interactive tool for sharing data and snippets of code (Google Colaboratory)

It is highly recommended to attend the in-person course.

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development