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Argomenc dl curs
Module 1: Programming in Python The course is designed to provide specific professional skills for advanced programming in Python. The students will learn how to develop a Python program, starting from designing it, and going through coding, testing and validation. They will master Python in its full object-oriented features, learning how to develop complex programs that are well structured, and make use of techniques for code re-use, pipelining, maintenance, and deployment. Module 2: Data Analysis and Visualization The course is designed to acquire professional skills and knowledge useful when dealing with large-scale datasets. In particular, the students will master data collection, exploration, transformation, curation, analysis, and visualization, choosing the most appropriate technique for the data at hand. They will make insights from the data, supported by a rigorous data science pipeline, which starts with raw data, produces machine learning models, and ends with advanced visualizations. This module, addresses common pitfalls that can mislead the analysis and makes extensive use of specialized Python libraries, acquiring the best practices of reproducible, data-driven analysis and research.

Modalité de ensegnament
The course adopts a blended, student-centered approach that emphasizes problem-based learning and active engagement. Selected lecture content is made available online in advance, enabling students to explore key concepts independently and at their own pace. This preparatory work allows in-person sessions to focus on applying knowledge through problem-solving, collaborative activities, and guided discussions—fostering critical thinking and deeper understanding. The teaching format combines frontal lectures, hands-on lab assignments, and project work, ensuring that students develop both theoretical knowledge and practical skills in python programming, and in data analysis, modelling, and visualization. The course is aligned with the principles of the EDUNEXT initiative (https://edunext.eu), promoted by Italian universities, which supports the integration of digital resources and active learning strategies in higher education.

Obietifs formatifs
The course belongs to the type "caratterizzanti – discipline informatiche". The course is designed to provide specific professional skills for advanced programming in Python and professional skills and knowledge useful when dealing with large-scale datasets. Knowledge and understanding: • D1.1 - Knowledge of the key concepts and technologies of data science disciplines • D1.2 - Understanding of the skills, tools and techniques required for an effective use of data science • D1.3 - Knowledge of principles, methods and techniques for processing data in order to make them usable for practical purposes, and understanding of the challenges in this field • D1.9 - Knowledge of the challenges in the field of man-machine interface and of the methods and techniques for overcoming these challenges • D1.12 - Basic knowledge of the main ethical and social implications of data science Applying knowledge and understanding: • D2.1 - Practical application and evaluation of tools and techniques in the field of data science • D2.4 - Ability to develop programmes and use tools for the analysis and management of data and related infrastructures • D2.8 - Practical application and evaluation of tools and techniques for data analysis • D2.9 - Design, application and evaluation of technologies and tools for human-machine interaction, data exploration and data visualization Making judgments • D3.2 - Ability to autonomously select the documentation (in the form of books, web, magazines, etc.) needed to keep up to date in a given sector. Communication skills • D4.1 - Ability to use English at an advanced level with particular reference to disciplinary terminology • D4.2 - Ability to present one's work in a clear and comprehensible way in front of an audience, including non-specialists • D4.3 - Ability to structure and draft scientific and technical documentation Learning skills • D5.3 – Ability to deal with problems in a systematic and creative way and to appropriate problem solving techniques.

Sort de ejam
The exam modalities are the same for both the attending and the non-attending students. Project work (70% of the final grade) and oral exam (30% of the final grade). All project works must have been submitted, at the very latest, 15 days ahead of the oral exam. In case of a positive mark, the projects will count for all 3 regular exam sessions.

Criters de valutazion
70% project work, 30% oral exam. • Relevant for project work: clarity of presentation, ability to gain useful and novel insights from data, creativity, critical thinking, ability to adhere to reproducible research best practices • Ability to use Python to write, evaluate and deploy advanced, object-oriented computer programs • Ability to use Python to employ (understand, recall and use) data analytics methods in practical settings, from data collection and curation, to data analysis, modelling and visualization.

Bibliografia obligatora

Data Visualization. A practical introduction. Haley.

A layered grammar of graphics. Wickham.

Python Data Science Handbook, by Jake VanderPlas. O'Reilly Media (1st Edition, 2016).

Subject Librarian: David Gebhardi, David.Gebhardi@unibz.it

Bibliografia aconsieda

Fundamentals of Data Visualization. Wilke.

Visualization Analysis and Design. Munzer.

Data Visualization: Charts, Maps, and Interactive Graphics. Grant.

Doing Data Science. Cathy O'Neil, Rachel Schutt. O’Reilly, 2013,

Python for Data Analysis. By Wes McKinney. O’Reilly, 2nd Edition, 2017,

Deplù informazions
Software used: Jupyter Notebook (for Python programming)