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: AI Applications Made Easy: RAG, agents, and other GenAI solutions. Manning, 2025. ISBN: 9781633435872. - 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. 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.

Students attending the course: 50% group projects, 20% mid-term assessment, 30% final exam. For non-attending students: 100% Single exam on the entire program.

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.