The "Marketing Analytics" course aims to enable students to understand and apply data analytics in real-world business and marketing contexts. By integrating theoretical knowledge with practical skills, the course will prepare students to design and prototype analytical capabilities autonomously. Students will gain hands-on experience with low-code tools like KNIME and PowerBI, enhancing their ability to conduct analyses and present quantitative results and insights effectively. Additionally, the course will develop students' abilities to communicate data-driven insights and recommendations to both technical and non-technical audiences, fostering a comprehensive skill set for their future careers.

By the end of the course, students will: - Understand key concepts of data analytics and the importance of using appropriate algorithms to extract trends and patterns from data. - Gain practical skills in using low-code tools like KNIME, Langflow, and PowerBI for business and marketing analytics. - Develop the ability to apply AI algorithms, including supervised and unsupervised machine learning, in real-world marketing scenarios. - Learn to effectively communicate data-driven insights and recommendations to both technical and non-technical audiences. - Acquire hands-on experience with Generative AI, enhancing their understanding of its opportunities, limitations, and ethical implications. - Foster critical thinking and the ability to work both independently and collaboratively on analytical projects.

- Data Analytics Frameworks and Tools: Introduction to data analytics frameworks, low-code tools (including Knime, Power BI, and Langflow), and foundational machine learning concepts. - Supervised and Unsupervised Machine Learning: Techniques including regression, classification, clustering algorithms, and customer segmentation, tailored for marketing applications. - Generative AI: Understanding the principles, opportunities, limitations, and ethical implications of Generative AI and large language models. - Data Visualization and Communication: Building dashboards and using data visualization to effectively communicate insights and support decision-making in marketing contexts.

Core: - Andrea De Mauro: Data Analytics Made Easy: Analyze and present data to make informed decisions without writing any code. Packt, Birmingham, 2021. ISBN: 9781801074155. - Andrea De Mauro, Michele Pacifico: The Financial Times Guide to Data Transformation: How to drive substantial business value with data analytics. Pearson UK, 2024. ISBN: 9781292462141. - Andrea De Mauro: Grokking AI Applications: An illustrated guide for programmers and other curious people. Manning, 2026. ISBN: 9781633435872. Recommended: - Andrea De Mauro: Data Analytics per tutti: Imparare ad analizzare, visualizzare e raccontare i dati. Apogeo, 2022 (in Italian). ISBN: 9788850335947. - Roberto Cadili, Francisco Villarroel Ordenes: Meet Your Customers: The Marketing Analytics Collection, KNIME Press, 2023.

Lectures, discussions, labs, and group projects on relevant empirical issues will be highly interactive and based on real-world examples and datasets. Students’ participation during lectures is strongly encouraged and may impact the final grade.

Attending the course is recommended. Students who prefer not to or cannot attend (non-attending status) must inform the Student Office and the instructor by the end of the first week of the course; after this deadline, all students are considered attending students. To retain attending status, students must be tracked as present for at least 70% of the lectures and must complete every continuous-assessment activity scheduled during the course: falling below the 70% attendance threshold, or missing any of these activities, results in the loss of attending status. For attending students, the continuous assessment carried out during the course corresponds to one third (1/3) of the overall grade and may include practical exercises, group projects, presentations, individual tests or assignments, and other forms of evaluation designed to monitor progressive learning and skills acquisition. In the exam sessions, attending students then take an individual final assessment corresponding to two thirds (2/3) of the overall grade; this assessment is designed to showcase the knowledge and skills acquired and may take the form of a written and/or oral exam, a project discussion, or a synthesis test of the competences developed. Continuous-assessment grades remain valid only for attending students who take the final assessment during the exam sessions of that same semester. Non-attending students must take an individual oral exam covering the entire course programme; preparation should be based on the materials shared on the course platform and on the recommended core textbooks. Students are required to carry out all the practical exercises related to these tools — whether during classes or as part of their exam preparation — and to become fully acquainted with the functionalities of KNIME, Power BI, and Langflow. Exams may include questions on these tools. All retake exams are treated as non-attending exams (individual oral exam on the entire course programme).

Students who demonstrate a strong grasp of the subject, earn an excellent final grade, and are eager to carry out a fully experimental thesis with genuine publication potential are encouraged to submit a thesis proposal. Detailed thesis guidelines will be presented during the course.