DATA AND ARTIFICIAL INTELLIGENCE LABS

The Data and AI Labs course aims to equip students with practical knowledge and hands-on experience in data analytics and AI applications relevant to marketing. This course will help students understand key analytical concepts and techniques from both a business and technical perspective. By the end of the course, students will have built a minimalistic toolkit using low-code tools, enabling them to perform data analysis and communicate insights effectively. They will be prepared to advocate for data-driven decision-making within their organizations.

No previous knowledge or IT/engineering skills are required, but some basic understanding of statistics and programming may be of help.

By the end of the course, students will be familiar with key concepts of data analysis and the importance of using suitable algorithms to extract trends and patterns from data. They will learn to combine techniques of predictive modeling, machine learning, and AI, including Generative AI, with a specific focus on marketing applications. The course will teach students to adopt a data-driven approach to problem-solving and decision-making, fostering critical thinking and the ability to work both independently and collaboratively. Additionally, students will gain skills in data visualization to maximize the impact of their insights.

- Data Analytics Frameworks and Tools: Introduction to data analytics frameworks, low-code tools like KNIME and PowerBI, 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 and Advanced Topics: 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.

- 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. - 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.

The assessment will be either PASS or NO PASS. To pass this course, the student must reach the minimum threshold for attendance in class and obtain the minimal score threshold on the assignments presented during the course.

Students who demonstrate a strong grasp of the subject, pass the exam, 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.