The Statistical Learning track of the Master of Science in Mathematical Engineering at Politecnico di Milano offers a truly unique and rigorous educational program designed for students passionate about extracting knowledge and building solutions from data through a blend of mathematical and computational tools.
What sets this track apart is its hybrid approach: it bridges between the model-based mathematical reasoning typical of Statistics and the algorithmic and computational approach typical of Machine Learning. Students are trained to not only master state-of-the-art methodologies but also to critically assess and apply them to real-world problems of significant societal and/or industrial impact, virtually in any possible field of application where data can be collected.
Whether you are fascinated by discovering the theoretical foundations of modern data science or driven by an urgent desire to solve practical challenges through algorithms and models, the Statistical Learning track provides all the needed skills and competences to pursue your goal.
Courses
The program is designed to offer students a T-shaped education — characteristic of modern data scientists — combining deep vertical theoretical knowledge with a horizontal, practical, and trans-disciplinary breadth.
Compulsory Courses
A solid core of six compulsory courses lays indeed the groundwork in mathematics, statistics, and computation:
- Applied Statistics
- Bayesian Statistics
- Model Identification and Data Analysis
- Stochastic Dynamical Models
- Real and Functional Analysis
- Algorithms and Parallel Computing
These courses equip prospective graduates with a solid mathematical foundation and the statistical thinking approach needed to face complex data-driven problems, complemented by computational techniques essential for handling large-scale applications and computationally intensive solutions.
Elective Courses
To tailor the experience to individual interests, students choose six elective courses from a pool of over 100 offerings. Examples include:
- Nonparametric Statistics
- Computational Statistics
- Numerical Analysis for Machine Learning
- Data Mining
- Streaming Data Analytics
- Machine Learning
- Artificial Neural Networks and Deep Learning
- Data Driven Control System Design
- Stochastic Differential Equations
- Insurance & Econometrics
- Design and Analysis of Experiments B
- Reliability Engineering and Quantitative Risk Analysis A+B
This wide range of electives allows students to specialize according to their academic interests and career ambitions.
Master Thesis
The final thesis is far more than an academic exercise: it is always part of a real research project. Theses can be methodological, application-oriented, or algorithmic, depending on the student’s focus and the project context. Each thesis provides an opportunity to work closely with faculty, PhD students, and research groups, often in collaboration with industry or international institutions. Students tackle cutting-edge problems, contributing new ideas and gaining firsthand experience in the world of research and innovation.
Work Placement
Today, the role of the data scientist is among the most sought-after professions worldwide. Graduates of the Statistical Learning track consistently find employment immediately after graduation and often even before completing their studies. Career opportunities span in both the private and public sectors with most of the alumni:
- working in the research and development units of private service companies, industrial firms, or public institutions.
- pursuing roles in advanced consulting, either in generalist consulting firms or in consulting companies specialized in finance, healthcare, mobility, energy, or manufacturing.
- continuing their academic journey by pursuing a Ph.D. program at prestigious international universities.