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Freie ±«²Ô¾±±¹±ð°ù²õ¾±³Ùä³Ù Bozen

Responsible Data Practices for Business and Economics

Semester 2 · 27528 · Master in Data Analytics for Economics and Management · 3KP · EN


This course introduces principles and practices for the responsible use of data in business and economic contexts. It covers ethical, legal, and societal implications of data collection, analysis, and decision-making, with a focus on real-world applications such as algorithmic decision systems, data governance, and privacy.

Vorlesungsstunden: - 12 hours of in-person lectures - 6 hours of video lectures (counted as 12 hours to account for re-watching)
Laboratoriumsstunden: -
Anwesenheitpflicht: Attendance is recommended, but not mandatory.

Themen der Lehrveranstaltung
• Introduction to responsible data practices • Ethics of data and algorithms • Data governance and management • Privacy and data protection • Data in business decision-making • Societal impacts of data use

Unterrichtsform
Recorded lectures, in-person teaching, exercises. The course adopts a blended, student-centred approach that emphasises problem-based learning and active engagement. A portion of the lecture content is made available online in advance, allowing students to explore key concepts independently and at their own pace before attending class. This preparatory work enables inperson sessions to focus on the application of knowledge through real-world problems, collaborative activities, and guided discussions — fostering critical thinking and deeper learning. The course is fully aligned with the principles of the Italian Universities Digital Hub (EDUNEXT) initiative (https://edunext.eu), which promotes the integration of digital resources and active learning strategies within university teaching.

Bildungsziele
Intended Learning Outcomes (ILO) ILO 1 Knowledge and understanding: ILO 1.1 Students will develop specialised knowledge within the economic and business domains, tailored to their areas of interest and essential for addressing decision-making and managerial challenges in both public and private organisations. This learning outcome emphasises an interdisciplinary approach to problem-solving and organisational analysis. ILO 2 Applying knowledge and understanding: ILO 2.1 Students will develop the ability to analyse business-related issues that underpin data-driven decision support by applying statistical models and computational modelling techniques. This outcome focuses on integrating quantitative methods to evaluate and optimise organisational decision-making processes. ILO 3 Making judgements: ILO 3.1 The student acquires the ability to apply acquired knowledge to interpret data in order to make directional and operational decisions in a business context. ILO 3.2 The student acquires the ability to apply acquired knowledge to support processes related to production, management and risk promotion activities and investment choices through the organisation, analysis and interpretation of complex databases. ILO4 Communication skills: ILO 4.1 The student acquires the ability to communicate effectively in oral and written form the specialised content of the individual disciplines, using different registers, depending on the recipients and the communicative and didactic purposes, and to evaluate the formative effects of his/her communication. ILO 5 Learning skills: ILO 5.1 The student acquires knowledge of scientific research tools. He/she will also be able to make autonomous use of information technology to carry out bibliographic research and investigations both for his/her own training and for further education. Furthermore, through the curricular teaching and the activities related to the preparation of the final thesis, she will be able to acquire the ability - to identify thematic connections and to establish relationships between methods of analysis and application contexts; - to frame a new problem in a systematic manner and to implement appropriate analysis solutions; - to formulate general statistical-econometric models from the phenomena studied.

Art der Prüfung
TBD

Bewertungskriterien
TBD

Pflichtliteratur

TBD



Weitere Informationen
Lecturer TBA




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