CODE 106837 ACADEMIC YEAR 2023/2024 CREDITS 6 cfu anno 1 ECONOMICS AND DATA SCIENCE 11267 (LM-56) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR SECS-S/06 LANGUAGE English TEACHING LOCATION GENOVA SEMESTER 2° Semester MODULES Questo insegnamento è un modulo di: MATHEMATICS FOR ECONOMICS AND DATA SCIENCE TEACHING MATERIALS AULAWEB OVERVIEW The aim of the course is to provide the student with the knowledge of advanced mathematical methods to successfully deal with economic models from a quantitative viewpoint. AIMS AND CONTENT LEARNING OUTCOMES The course will provide the mathematical tools needed to successfully attend the other courses of the Master program. Specifically, the course will offer the student: i) a more in-depth study of linear algebra; ii) the tools to face both unconstrained and constrained optimization problems for functions of several variables; iii) basic knowledge and methods to analyze systems of differential equations. Further, the student will acquire the fundamental concepts of probability necessary to model uncertain events. AIMS AND LEARNING OUTCOMES At the end of the course, the student will be able to deal, autonomously and properly, with the learned mathematical topics and apply them to economic problems with the needed methodological rigor. Knowledge and understanding: The students must learn the main mathematcal techniques for the optmization and the description of dynamical systems. Ability to apply knowledge and understanding: The students must be able to model and solve mathematical problems of static optimization and dynamical system in the field of social sciences. Making judgments: The students must be able to use the acquired knowledge with an autonomous evaluating assessment. Communication skills: Students must be able to use the correct technical language for the communication of the results and for the description of the techniques. Learning skills: Students will develop adequate learning skills in order to continue with further studies about other aspects of the subject and different fields of application than those illustrated. PREREQUISITES The contents of the course of Mathematics for Economics and Data Sciences I. TEACHING METHODS The course will be taught by frontal lectures. SYLLABUS/CONTENT Advandced topics in linear algebra Constrained optimization and applications to Economics Differentiable equations in two variables and applications to Economics RECOMMENDED READING/BIBLIOGRAPHY Sydsaeter, K., Hammond, P., Seierstad, A., Strom, A.: Further Mathematics for Economic Analysis (2008), Pearson Simon, C., Blume, L.: Mathematics for Economists (1994), Norton and Company Peccati, L., D'Amico, M., Cigola, M. : Maths for Social Sciences (2018), Springer TEACHERS AND EXAM BOARD MAURO ROSESTOLATO MARIA LAURA TORRENTE Ricevimento: Office hours: will be communicated at the beginning of the semester Exam Board MARIA LAURA TORRENTE (President) MAURO ROSESTOLATO LESSONS LESSONS START Second semester, February 2024 Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION The exam will be written and will contain questions on the the theoretical and modeling features treated in the course as well as exercises. ASSESSMENT METHODS The exam will evaluate the understanding of the contents of the course with the goal of assessing the reached skill of applying the tools and the methods learned in an economic perspective Exam schedule Data appello Orario Luogo Degree type Note 08/01/2024 14:00 GENOVA Scritto 22/01/2024 14:00 GENOVA Scritto 06/02/2024 14:00 GENOVA Scritto 29/05/2024 14:00 GENOVA Scritto 12/06/2024 14:00 GENOVA Scritto 10/07/2024 14:00 GENOVA Scritto 11/09/2024 14:00 GENOVA Scritto FURTHER INFORMATION Other information will be provided during the course. For non-attending students the same rules apply. Students with DSA certification ("specific learning disabilities"), disability or other special educational needs are advised to contact the teacher at the beginning of the course to agree on teaching and examination methods that, in compliance with the teaching objectives, take account of individual learning arrangements and provide appropriate compensatory tools.