Statistics

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrolment is by permission of the instructor. At the end of the course, the student has a deep knowledge of industrial applications that benefit from the use of machine learning, optimization, and simulation. The student has a domain-specific knowledge of practical use cases discussed in collaboration with industrial experts in a variety of domains such as manufacturing, automotive, and multi-media. The course is primarily delivered as a series of simplified industrial use cases. The goal is to provide examples of challenges that typically arise when solving industrial problems. Use cases may cover topics such as: anomaly detection; Remaining Useful Life (RUL) estimation; RUL based maintenance policies; resource management planning; recommendation systems with fairness constraints; power network; management problems; epidemic control; and production planning. The course emphasizes the ability to view problems in their entirety and adapt to their peculiarities. This frequently requires to combine heterogeneous solution techniques, using integration schemes both simple and advanced. The employed methods include: mathematical modeling of industrial problems; predictive and diagnostic models for time series; Combinatorial Optimization; integration methods for Probabilistic Models and Machine Learning; integration methods for constraints and Machine Learning; and integration methods for combinatorial optimization and Machine Learning. The course includes seminars on real-world use cases, from industry experts. The course contents may be (and typically are) subject to changes, so as to adapt to some degree to the interests and characteristics of the attending students.

The reliability of the findings from a research study depends critically on the design of the study. An understanding of the principles of study design is important for all consumers of scientific research, and essential for all those who will be carrying out scientific research. This course provides students with the knowledge and skills to translate a research aim into specific study objectives, construct a study design to address the objective(s), and write a plan for the statistical analysis. Students will also learn skills in critical evaluation of published research papers. Topics include survey methods, experimental and observational studies, measurement, control of confounding and bias, evaluation of competing designs, determination of study size.

This course provides an introduction to the concepts and use of statistics for economics and business. Topics include: statistical terms; types of variables; analysis of univariate data; analysis of bivariate data; probability and probability models; introduction to statistical inference. Classes are focused on problem solving and practical computing using statistical software. NOTE: Course is the same as STAT 20, but taught in English. Pre-Requisited: Calculus & Linear Algebra

This course provides research training for exchange students. Students work on a research project under the guidance of assigned faculty members. Through a full-time commitment, students improve their research skills by participating in the different phases of research, including development of research plans, proposals, data analysis, and presentation of research results. A pass/no pass grade is assigned based a progress report, self-evaluation, midterm report, presentation, and final report.

This course gives an introduction to the design of sample surveys and estimation procedures, with emphasis on practical applications in survey sampling. Topics include planning of surveys, questionnaire construction, methods of data collection, fieldwork procedures, sources of errors, basic ideas of sampling, simple random sampling, stratified, systematic, replicated, cluster and quota sampling, sample size determination, and cost. This course is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

The course equips students with the skills to be able to design, carry out, report, read, and evaluate qualitative research projects. It is taught by qualitative research experts who have experience of using the methods they teach. It covers the full cycle of a qualitative research project: design, data collection, analysis, reporting, and dissemination. Students gain a conceptual understanding of current academic debates regarding different methods, and the practical skills to put those methods into practice.

The purpose of this course is to introduce the basic principles of statistics and its application. The course focuses on introducing and proving the basic theorem of statistics, statistical data processing and computer software applications, and the interpretation of statistical analysis.