Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year activated in the A.Y. 2021/2022
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1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Marketing research for agrifood and natural resources (2021/2022)
Teaching code
4S009083
Academic staff
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
AGR/01 - AGRICULTURAL ECONOMICS AND RURAL APPRAISAL
Period
Primo semestre dal Oct 4, 2021 al Jan 28, 2022.
Learning outcomes
The course aims to provide students with an introduction to data analysis methodologies relating to the agri-food sector with extension to related sectors. Applications will be presented about both business, market supply and demand data.
At the end of the course the student has to show to have acquired the following skills:
● knowledge about the main methods of data analysis in the agrifood sector
● knowledge of the basic methods for data analysis in sectors related to the agrifood industry
● ability to exploit basic methods for data analysis in sectors related to the agrifood industry
Program
The Food systems
Natural Resources and Common Goods
Food marketing mix
Product differentiation
Food Distribution and Supply Chain Management
Performance indicators for food supply chains
Agri-Food and Natural Resources Data
Food and natural resources demand
Value Theory and Product Valuation: WTP and WTA
The experimental methods applied to Food marketing
Bibliography
Examination Methods
The exam consists of group works and group presentation during the course and an oral examination t the end of the course
