This course is designed for engineering/computer related fields and business/management students students who want to bridge analytical thinking with practical decision-making skills using modern data tools. It introduces essential concepts in data analyticssuch as data preparation, visualization, statistical reasoning, multi-criteria decision modeling, and an introduction to process miningand applies them to realistic industrial and managerial challenges. Students learn how to transform raw data into meaningful insights through dashboards, prioritization models, performance indicators, and simulation-based evaluations. The course also exposes students to the fundamentals of process mining, enabling them to extract event-log data, visualize real organizational workflows, identify bottlenecks, and support improvement opportunities based on evidence rather than assumptions. Throughout the course, students work with accessible software such as Excel, Power BI, and Python, developing solutions that mirror professional decision-support systems. From constructing decision matrices to applying techniques like AHP, TOPSIS, utility modeling, and discovering process behavior through mining tools, students gain insight into the same reasoning processes used by analysts, consultants, and operations leaders in industry. By the end of the course, students will be able to turn data into decisionsdesigning analytical models, interpreting results, mining process flows, and supporting managers in choosing optimal strategies. This hands-on approach gives learners a career-ready edge, enabling them to build decision-support artifacts that enhance real engineering projects, process intelligence, and organizational performance.
Questions about de course?
Prof Matheus Beltrame Canciglieri – [email protected]
