Guida degli insegnamenti
Syllabus
Partially translatedTradotto parzialmente
Riccardo CAPPELLI (Crediti: 6 Ore di lezioneTeaching hours: 44)
Jasmine MONDOLO (Crediti: 3 Ore di lezioneTeaching hours: 22)
Lingua di erogazione: INGLESELessons taught in: ENGLISH
Laurea Magistrale - [EM11] DATA SCIENCE PER L'ECONOMIA E LE IMPRESE Master Degree (2 years) - [EM11] DATA SCIENCE FOR ECONOMICS AND BUSINESS
Dipartimento: [040002] Dipartimento Scienze Economiche e SocialiDepartment: [040002] Dipartimento Scienze Economiche e Sociali
Anno di corsoDegree programme year : 1 - Secondo Semestre
Anno offertaAcademic year: 2021-2022
Anno regolamentoAnno regolamento: 2021-2022
Obbligatorio
Crediti: 9
Ore di lezioneTeaching hours: 66
TipologiaType: B - Caratterizzante
Settore disciplinareAcademic discipline: SECS-P/01 - ECONOMIA POLITICA
INGLESE
ENGLISH
The course requires basic knowledge (undergraduate courses) in micro and macroeconomics.
The course requires basic knowledge (undergraduate courses) in micro and macroeconomics.
The course unfolds through both traditional frontal lectures and practical sessions.
The course unfolds through both traditional frontal lectures and practical sessions.
The course presents qualitative aspects and empirical applications mainly related to the evolution and diffusion of innovation, ICT and patents.
Capacity to apply Knowledge and Understanding.
The topics that are presented and discussed are expected to be employed to understand real-world examples, business decisions and strategies. At the end of the course, students will be able to handle qualitative and quantitative tools in order to measure and assess, for instance, the evolution and spread of innovation and patent activities, how the performance of countries, industries and firms is affected by ICT and innovation, and how network analysis can be applied to the software sector, patent activities and other economic fields.
Transversal Skills.
Students are expected to strengthen their ability to perform socio-economic analyses in a critical and autonomous way. Students will be stimulated to improve their personal communication and presentation skills, while practicing their cooperation and group-work abilities.
Knowledge and Understanding.
The course presents qualitative aspects and empirical applications mainly related to the evolution and diffusion of innovation, ICT and patents.
Capacity to apply Knowledge and Understanding.
The topics that are presented and discussed are expected to be employed to understand real-world examples, business decisions and strategies. At the end of the course, students will be able to handle qualitative and quantitative tools in order to measure and assess, for instance, the evolution and spread of innovation and patent activities, how the performance of countries, industries and firms is affected by ICT and innovation, and how network analysis can be applied to the software sector, patent activities and other economic fields.
Transversal Skills.
Students are expected to strengthen their ability to perform socio-economic analyses in a critical and autonomous way. Students will be stimulated to improve their personal communication and presentation skills, while practicing their cooperation and group-work abilities.
The main topics covered during the course are the following:
-Industry life-cycles
-Business models
-Innovation activities (with a focus on patents)
-An introduction to network analysis, including some economic applications
Note:
Empirical applications based on data analysis are carried out during the course on a regular and continuous basis. Periodic assignments are given to attending students. Accordingly, on-site attendance is strongly recommended.
The main topics covered during the course are the following:
-Industry life-cycles
-Business models
-Innovation activities (with a focus on patents)
-An introduction to network analysis, including some economic applications
Note:
Empirical applications based on data analysis are carried out during the course on a regular and continuous basis. Periodic assignments are given to attending students. Accordingly, on-site attendance is strongly recommended.
The final examination consists of an oral exam partly based on the discussion of the project work.
Learning Evaluation Criteria.
Course assessment will be based on the following components: oral exam; project work; in-class assignments.
Learning Measurement Criteria.
The student passes the exam if the final grade is above 18/30. Cum laude can be bestowed to outstanding performance, reached on all the previous assessment criteria.
Final Mark Allocation Criteria.
The student passes the exam if the final grade is above 18/30. Cum laude can be bestowed to outstanding performance.
Learning Evaluation Methods.
The final examination consists of an oral exam partly based on the discussion of the project work.
Learning Evaluation Criteria.
Course assessment will be based on the following components: oral exam; project work; in-class assignments.
Learning Measurement Criteria.
The student passes the exam if the final grade is above 18/30. Cum laude can be bestowed to outstanding performance, reached on all the previous assessment criteria.
Final Mark Allocation Criteria.
The student passes the exam if the final grade is above 18/30. Cum laude can be bestowed to outstanding performance.
The material will be uploaded in the webpage of the course. More information will be provided by the teachers during the lectures.
Note:
Non-attending students are invited to contact the professors well ahead of the final exam in order to receive useful information on how to study and prepare for the exam.
The material will be uploaded in the webpage of the course. More information will be provided by the teachers during the lectures.
Note:
Non-attending students are invited to contact the professors well ahead of the final exam in order to receive useful information on how to study and prepare for the exam.
https://learn.univpm.it/
https://learn.univpm.it/
Le informazioni contenute nella presente scheda assumono carattere definitivo solo a partire dall'A.A. di effettiva erogazione dell'insegnamento. Academic year 2021-2022
Università Politecnica delle Marche
P.zza Roma 22, 60121 Ancona
Tel (+39) 071.220.1, Fax (+39) 071.220.2324
P.I. 00382520427