Digital Economy

  • Credits: 4
  • Ending: Examination
  • Range: 2P + 2C
  • Semester: summer
  • Year: 1
  • Faculty of Business Economics with seat in Košice

Teachers

Included in study programs

Corporate Business and Marketing

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

Lectures:
1. Nature of Data and data collection.
2. Data analytics.
3. Digitalization of economy.
4. Digital goods and services.
5. B2B Platforms.
6. Digital Markets.
7. Trust building mechanism.
8. Reputation on the Internet.
9. Digital business, strategy, and innovation.
10. Business Intelligence.
11. Introduction to Datamining.
12. Datamining methods.
13. Security, privacy and GDPR.
Seminars:
1. Selected case studies and examples related to data types.
2. Selected case studies and examples related to data preparation.
3. Selected case studies and examples related to visualization multivariable datasets.
4. Selected case studies and examples related to data analytics.
5. Selected case studies and examples related to preparation of electronic auction.
6. Selected case studies and examples related to realisation of electronic auction.
7. Selected case studies and examples related to evaluation of electronic auction.
8. Selected case studies and examples related to evaluation of reputation mechanisms.
9. Selected case studies and examples related to basic datamining methods I (e.g. basic classification trees).
10. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network modelling).
11. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network training).
12. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network validating).
13. Midterm assignment.

Support literature

Elementary literature:
ZHOU, Hong. Learn Data Mining Through Excel. Apress, 2020.
OVERBY, Harald; AUDESTAD, Jan Arild. Digital Economics: How Information and Communication Technology is Shaping Markets, Businesses, and Innovation. Sp, 2018.
PEITZ, Martin; WALDFOGEL, Joel (ed.). The Oxford handbook of the digital economy. Oxford University Press, 2012.
POCHIRAJU, Bhimasankaram; SESHADRI, Sridhar (ed.). Essentials of Business Analytics: An Introduction to the Methodology and Its Applications. Springer, 2019.
HODEGHATTA, Umesh R.; NAYAK, Umesha. Business analytics using R-a practical approach. Apress, 2016.

Syllabus

Lectures: 1. Nature of Data and data collection. 2. Data analytics. 3. Digitalization of economy. 4. Digital goods and services. 5. B2B Platforms. 6. Digital Markets. 7. Trust building mechanism. 8. Reputation on the Internet. 9. Digital business, strategy, and innovation. 10. Business Intelligence. 11. Introduction to Datamining. 12. Datamining methods. 13. Security, privacy and GDPR. Seminars: 1. Selected case studies and examples related to data types. 2. Selected case studies and examples related to data preparation. 3. Selected case studies and examples related to visualization multivariable datasets. 4. Selected case studies and examples related to data analytics. 5. Selected case studies and examples related to preparation of electronic auction. 6. Selected case studies and examples related to realisation of electronic auction. 7. Selected case studies and examples related to evaluation of electronic auction. 8. Selected case studies and examples related to evaluation of reputation mechanisms. 9. Selected case studies and examples related to basic datamining methods I (e.g. basic classification trees). 10. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network modelling). 11. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network training). 12. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network validating). 13. Midterm assignment.

Requirements to complete the course

Midterm evaluation:
Midterm written assignment: 40% of course (total points 40)
Minimal points required to pass midterm written assignment are 21 points (out of 40 points)

Final written exam:
Final written exam 60% of course - 60 points
Minimal points required to pass final written exams are 31 points

Student workload

104 hours in total, of which:
26 hours of participation in lectures
13 hours preparation for lectures
26 hours of participation in seminars
13 hours preparation for seminars
13 hours preparation for midterm written assignment
13 hours preparation for the final exam

Language whose command is required to complete the course

english

Date of approval: 20.02.2023

Date of the latest change: 22.02.2023