Data are at the heart of modern business, providing strategic insights for informed decision-making, process optimization, and continuous innovation. This course will guide students in using data to develop analytics and predictive models capable of generating value for decision-making in business, organizational, and societal contexts.

Knowledge and understanding: Through concrete data sets, machine learning, and algorithmic toolkits, the course will provide a good understanding on how to apply data analytics methodologies in order solve concrete business problems. Applying knowledge and understanding: On successful completion of this course students will be able to: • Design effective solutions to a given data-driven problem using concrete data analytics, machine learning, and data visualization methodologies. • Go through the full data analysis process, starting from data cleaning, building and training models, execution and quality/performance refinement. • Deal with real business analytics applications. Making judgements: Students are expected to be able to analyze different techniques, approaches and models for business analytics applications. Throughout the entire course, students will be invited to assess critically strengths and weaknesses of different solutions for the same problem. Communications Skills: This course will enhance students’ capabilities to communicate effectively their ideas, findings, proposals, analysis and critical reasoning throughout the completion of their project work. A special emphasis will be given to oral presentations and pitches in project group works, and to writing technical reports and documentation. Learning skills: The course will empower students with the capability to carry out concrete data analysis projects of industrial interest, also through machine learning techniques. A strong emphasis will be given to solving complex business problems that are typical of today’s data-driven companies.

Through a hands-on approach, the course will go through the different steps involved in achieving a business goal using data analysis and building machine learning models. In particular, it will cover the following topics: • Data understanding (collect, describe, explore and verify data quality) • Data preparation (select, clean and integrate data) • Data visualization • Modeling, evaluation, deployment (build, train, and assess models; evaluate results and review process; plan deployment, monitoring and maintenance).

Notes, slides, notebooks and other course material will be made available on the e-learning platform.

The course consists of lectures, practical lab sessions, and testimonials from companies, complemented by group project works and a final challenge.

Students are expected to demonstrate their ability to design innovative solutions to concrete business problems; critically analyze and assess the strengths and weaknesses of different techniques; apply the most appropriate methods in an independent and critical way; and communicate effectively their ideas, findings, proposals, analyses, and reasoning. The project work will account for 100% of the final grade and will include both an individual and a teamwork evaluation component. The following aspects will be evaluated: • overall quality of the exploratory data analysis results; • performance of the machine learning models; • quality and relevance of the business insights extracted; • any additional work or extra features included in the report, such as interactive dashboards; • quality, clarity, and reproducibility of the Python notebook; • quality of the final presentation.

Final thesis projects may be assigned, upon specific request to the professor, to students who demonstrate a serious and motivated interest in the application of data analytics and machine learning techniques to business and social domains.