Digital Economy
- Ending: Examination
- Range: 20sP
- Semester: summer
- Year: 2
- Faculty of Business Economics with seat in Košice
Teachers
Included in study programs
Teaching results
Knowledge:
• Knowledge of the digital economy
• Knowledge of B2B and B2C platforms
• Knowledge of trust building mechanisms and building reputation in electronic markets
• Knowledge of the basic tools of dataminig and extraction of information from data
• Knowledge of security, privacy and GDPR in the digital environment.
Competences:
• Orientation in different types of electronic markets
• Operating in the environment of B2B and B2C trading platforms
• Evaluate business data using database and basic data mining tools.
• Evaluate the trustworthiness of business partners and building trustworthiness in electronic markets
• Introduce digital innovation in enterprises
• Management of Business Intelligence systems in the company
Skills:
• Buying and selling on electronic markets using B2B and B2C platforms,
• Adjusting the settings in the functionalities of market platforms
• Creation and parameterization of electronic product lists and electronic catalogues
• Reporting, control and management of Business Intelligence systems
• Creating a strategy for building reputation in electronic markets.
Indicative content
Indicative content:
Consultation:
Nature of Data and data collection
Data analytics
Digitalization of economy
Digital goods and services
B2B Platforms
Digital Markets
Trust building mechanism
Reputation on the Internet
Digital business, strategy, and innovation
Business Intelligence
Introduction to Datamining
Datamining methods
Security, privacy and GDPR
Self-study:
Selected case studies and examples related to data types
Selected case studies and examples related to data preparation
Selected case studies and examples related to visualization multivariable datasets
Selected case studies and examples related to data analytics
Selected case studies and examples related to preparation of electronic auction
Selected case studies and examples related to realisation of electronic auction
Selected case studies and examples related to evaluation of electronic auction
Selected case studies and examples related to evaluation of reputation mechanisms
Selected case studies and examples related to basic datamining methods I (e.g. basic classification trees)
Selected case studies and examples related to basic datamining methods II (e.g. simple neural network modelling)
Selected case studies and examples related to basic datamining methods II (e.g. simple neural network training)
Selected case studies and examples related to basic datamining methods II (e.g. simple neural network validating)
Midterm assignment
Support literature
1. ZHOU, Hong. Learn Data Mining Through Excel. Apress, 2020.
2. OVERBY, Harald; AUDESTAD, Jan Arild. Digital Economics: How Information and Communication Technology is Shaping Markets, Businesses, and Innovation. Sp, 2018.
3. PEITZ, Martin; WALDFOGEL, Joel (ed.). The Oxford handbook of the digital economy. Oxford University Press, 2012.
4. POCHIRAJU, Bhimasankaram; SESHADRI, Sridhar (ed.). Essentials of Business Analytics: An Introduction to the Methodology and Its Applications. Springer, 2019.
5. HODEGHATTA, Umesh R.; NAYAK, Umesha. Business analytics using R-a practical approach. Apress, 2016.
Requirements to complete the course
Midterm written assignment
Final written exam
Midterm evaluation:
Midterm written assignment: 40% of course (total points 40)
Final written exam:
Final written exam 60% of course - 60 points
Student workload
104 hours in total, of which:
• 20 hours of participation in consultation
• 13 hours preparation for consultation
• 32 hours self-study
• 19 hours preparation for midterm written assignment
• 20 hours preparation for the final exam
Language whose command is required to complete the course
English
Date of approval: 17.02.2023
Date of the latest change: 25.03.2024