The course aims to prepare students for real-world data analytics applications they will encounter in business environments, by equipping them with both technical and business perspectives on data analytics. They will gain a comprehensive understanding of various analytical methods and their practical applications in marketing and business decision-making. Students will build a "toolkit" of low-code tools for analytics, enhancing their capability to perform data transformations, machine learning (including GenAI applications), and visualization tasks. Additionally, the course emphasizes the importance of effectively communicating data insights to impact business decisions. Through state-of-the-art examples and hands-on exercises, students will develop the skills necessary to derive actionable insights from large consumer datasets and articulate these insights to stakeholders in a persuasive manner.

- Knowledge and Understanding: This course enhances students' understanding of how to utilize business and marketing analytics tools, processes, and methods to analyze large-scale marketplace data. It will help students grasp the implications of descriptive, predictive, and prescriptive analytics for achieving significant business outcomes. - Applying Knowledge and Understanding: Students will develop the ability to work both individually and in teams to address business and marketing challenges using analytics. The course involves practical applications of descriptive and predictive analytics on large datasets from various organizations, enabling students to solve real-world business problems. - Making Judgements: Students will learn to integrate knowledge of business strategy and data analytics, with a particular focus on the ethical implications and biases of AI applications. These skills are crucial for making informed managerial and policy decisions. Students are expected to critically discuss and assess the impact of various analytics metrics on business decision-making processes, ensuring they understand the potential and limitations of these capabilities. - Communication Skills: Throughout the course, students will gain practical experience in solving business and marketing problems using analytics algorithms and visualization techniques. They will also develop the ability to communicate their findings effectively through reports and group presentations, tailored for both technical and non-technical audiences. This will equip them to become evangelists of data analytics within their organizations, capable of advocating for its use and discussing its potential and limitations with diverse stakeholders. - Learning Skills: This course will foster the development of students' problem-solving skills. They will enhance their technical, analytical, and creative thinking abilities to analyze market data and derive actionable business insights. Students will learn to formulate solutions that support and enhance the marketing decision-making process.

The content of the course is organized in five sections: 1. Analytics for Business Value: Covers data analytics value frameworks, enablers for business analytics, and creating organizational roadmaps for value creation. 2. Applied Machine Learning: Teaches descriptive and machine learning analytics, supervised and unsupervised learning methods, and advanced analytics topics. 3. Generative AI: Introduces Generative AI, focusing on large language models, their applications, ethical implications, and practical skills in prompt engineering and LLM orchestration. 4. Business Data Communication: Focuses on data visualization techniques, building business dashboards, and developing data storytelling skills for C-suite presentations. 5. Business Analytics Toolkit: This toolkit provides hands-on experience with tools like KNIME, PowerBI, and Langflow, enabling students to be autonomous in designing and prototyping data analytics capabilities.

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: - Roberto Cadili, Francisco Villarroel Ordenes: Meet Your Customers: The Marketing Analytics Collection, KNIME Press, 2023.

Lectures, discussion, method labs, and group projects on relevant empirical issues. 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 complete every in-progress assessment scheduled during the course (group projects and mid-term assessment), and must be tracked as present for at least 70% of the lectures: missing even one of these assessments, or attending fewer than 70% of the lectures, results in the loss of attending status. Attending students: 50% group projects, 20% individual mid-term assessment, 30% individual written final exam. Attending students may sit the final exam at the end of the semester in which the course is delivered. Group project and mid-term assessment grades remain valid only for attending students who take the final exam 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).

No specific criteria.