Data is at the heart of modern business, offering strategic insights for informed decision-making, optimized processes, and continuous innovation. The training path in this course will guide students in using data to build models capable of generating value in business, organizational, and personal decision-making.

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. Basic knowledge of fundamental algorithms. machine learning and computer programming skills. Working knowledge of Python is strongly recommended.

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).

Lecture notes, research papers and course material will be made available on the e-learning platform.

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

Project work will count for 100% of the grade. Students are required to demonstrate that they are able to design innovative solutions for concrete business problems, to analyze and assess critically strengths and weaknesses of different techniques, applying the most appropriate techniques in an independent and critical way, communicate effectively their ideas, findings, proposals, analysis and critical reasoning.

The final work will be assigned (upon specific request to the professor) to students who demonstrate a serious and motivated interest to the course topics.