Program Overview

Program Overview

The MOOC introduces fundamental concepts and methods in scientific computing and computational learning to solve applied engineering and science problems. Through a hands-on, problem-based approach, learners integrate mathematical modeling, numerical simulation, and data-driven techniques using Python and MATLAB to develop key computational skills for modern applications in research and industry.

Course Description

This MOOC is provided by Politecnico di Milano and was produced as part of the Edvance project – Digital Education Hub per la Cultura Digitale Avanzata. The project is funded by the European Union – Next Generation EU, Component 1, Investment 3.4 "Didattica e competenze universitarie avanzate".

Intended Learning Outcomes

At the end of this course, learners will be able to:

• Formulate mathematical models for applied science and engineering problems, identifying key variables and governing principles.
• Implement basic numerical methods in Python and MATLAB to approximate and simulate model behavior.
• Analyze computational results and assess accuracy and stability of solutions.
• Integrate data-driven approaches with model-based methods to enable hybrid computational learning.
• Develop and present a practical solution to an engineering or scientific problem using a problem-based computational workflow.

Prerequisites

Basic knowledge of calculus and linear algebra is required for this course.

Course Structure

The course is delivered in online mode and is available free of charge. It consists of 5 weeks of study, with a total workload of 30 hours.

Assessment

The final grade for the course will be based on the results of answers to assessed quizzes. Learners have an unlimited number of attempts at each quiz, but must wait 15 minutes before trying again. To successfully complete the course, learners must score 60% (or higher) in each one of the assessed quizzes.

Certificate

Learners can achieve a certificate in the form of an Open Badge for this course if they reach at least 60% of the total score in each one of the assessed quizzes and fill in the final survey.

Course Faculty

The course faculty includes:

• Domenico Savio Brunetto, Associate Professor of Mathematics Education
• Anna Scotti, Associate Professor in Numerical Analysis
• Marco Verani, Full Professor of Numerical Analysis
• Alessio Fumagalli, Associate Professor in Numerical Analysis
• Ilario Mazzieri, Associate Professor in Numerical Analysis
• Francesco Regazzoni, Associate Professor in Numerical Analysis
• Stefano Pagani, Associate Professor in Numerical Analysis
• Edie Miglio, Associate Professor in Numerical Analysis
• Carlo De Falco, Associate Professor in Numerical Analysis
• Julian Venè, Machine Learning Scientist
• Andrea Re Fraschini, PhD student in Mathematical Models and Methods in Engineering

Research Areas

The course covers various research areas, including:

• Mathematical modeling
• Numerical simulation
• Data-driven techniques
• Scientific computing
• Computational learning
• Engineering applications
• Applied science problems

Learning Mode

The course is offered in a single-course learning mode, targeting MOOCs for Bachelor of Science students.

Language

The course is taught in English.

Length

The course is 5 weeks long.

European Qualifications Framework Level

The course is classified as EQF 6.

Thematic Area

The course falls under the thematic area of 0541 – Mathematics, 0611 - Computer use, according to the ISCED-F classification.