Courses

The first part contents of the course are as follows: Market Imperfections and Role of Government – Role of Externalities – The Environment as a Public Good; Quality of Institutions – Democracy and Voting Paradox – Bureaucracy; Fiscal Decentralization and Administration; Features of Taxation and Managing Tax Evasion; Statistical Macroeconomic Analyses.

The second part covers the following topics: Introduction to Panel Data and CCE Methods: Basics of panel data analysis and limitations of traditional methods. Cross-sectional dependence in macroeconomic datasets and its implications. Introduction to Common Correlated Effects (CCE) estimators. Dynamic Extensions of CCE Models: Theoretical underpinnings of dynamic panel models. Extending CCE methods to dynamic specifications.  Dealing with endogeneity and dynamic relationships in macroeconomic panels. Empirical Applications in Macroeconomics Case studies on key macroeconomic phenomena (e.g., global financial cycles, fiscal and monetary policy interactions). Practical implementation with real macroeconomic datasets. Using econometric software (e.g., Stata, R, or MATLAB) to apply CCE methods.

The course contents are as follows: Entrepreneurship in a Technology-Driven Environment. Problem Identification and Validation. Designing the Innovative Solution. Developing a Digital Representation of the Innovative Solution. Using AI Tools for Business Planning & Communication. Crafting a Clear Business Message and Elevator Pitch. Presentation Skills for Pitching. Final Pitch Presentations and Reflection.

The first aim of this course is to provide students with a structured understanding of project management principles, tools, and methodologies required for the effective planning, execution, monitoring, and completion of projects in contemporary organizations.

The second aim of the course is to develop students’ creative thinking skills and their ability to generate, evaluate, and implement creative ideas in business, marketing, and digital communication contexts. The course focuses on practical tools and methods that enhance creativity, problem-solving, and innovative decision-making. The important issues considered in this section are as follows: Creativity as a key competence in the digital economy. Myths and barriers of creativity. Creative thinking vs. critical thinking. Divergent and convergent thinking techniques. Creative problem-solving models (design thinking, SCAMPER, lateral thinking).   Idea generation tools for marketing and business communication. Creativity in digital content, branding, and social media. Team creativity and collaborative ideation. Evaluation and selection of creative ideas. Practical workshops and case-based exercises.  Finally, the third aim of this course is to introduce students to the fundamental concepts of marketing and to develop their understanding of consumer behavior in contemporary markets. The course focuses on key marketing principles such as the marketing mix, market segmentation, targeting, positioning, and the consumer decision-making process. By combining theoretical foundations with practical examples, students will gain the ability to analyze markets and design basic marketing strategies. The teacher focuses on consumer behaviors.

The second sections aims to teach students the essence of the concept of digital finance; the development directions of financial technologies; the operational mechanisms of digital payment systems and electronic banking platforms; the characteristics of the formation of innovative financial institutions; the impact of payment cards and digital payment instruments on economic development; as well as the fundamental features of virtual currencies, the FinTech ecosystem, digital banking, and risks in e-banking services.

The third section of this course is to provide students with a clear and practical understanding of how artificial intelligence can be applied in contemporary business and communication contexts. The course focuses on developing strategic thinking about AI, emphasizing its role as a supportive tool for decision-making, communication, marketing, and business processes rather than a replacement for human expertise. Through real-world examples and applied discussions, students will learn how AI-driven tools influence business efficiency, digital communication, and managerial practices. The course also aims to raise awareness of ethical considerations, risks, and responsible use of artificial intelligence, enabling students to critically evaluate AI solutions and their impact on organizations and society.

The second part aims to explain the importance of data quality, to describe the major dimensions of data quality, to identify common sources of poor data quality, evaluate data quality in a dataset using structured criteria, describe value chain for data quality, and discuss the impact of AI on data quality.

The first part of this course covers the following topics: Overview of cybersecurity and information assurance. Threats, vulnerabilities, and attack vectors. Principles of confidentiality, integrity, and availability. Authentication, authorization, and access control. Cryptography fundamentals (symmetric, asymmetric, hashing, digital signatures). Network security and secure communication protocols. Security governance frameworks (ISO/IEC 27001, NIST, CIS Controls). Cyber defense, incident response, and ethical considerations.

The second part workshops and team activities are as follows: Team Activity 1 – In-class reading and discussion: McDonald’s Poland: Record GDPR Fine for Employee Data Breach; American Heart of Poland S.A.: Healthcare Data Breach and Fine; Public Administration: NIK Audits Reveal Systemic Data Security Lapses. Team Activity 2- in class reading and discussion: development of a value chain for data quality; identification of errors differ between stages in value chain. Team Activity 3 – Data errors by industry; Answer question: Are different errors equally important across different industries. Team Activity 4 – Development of index for data quality; Answer question: How to develop an index for data quality?

The second section aims to consider the state-of-the art and the prospective of the green transformation and the implementation of the digital twins to support it. The third part of this course contains the following topics: Green pact, green transition and green transformation, State-of the-art of the green transformation in EU and elsewhere, case studies with concrete countries. Technologies implemented for the green transformation. Implementation of the Digital Twins for the green transformation.

The key concepts introduced during the course are for example: confidentiality, integrity, availability, threat, risk, incident, information assets, and security measures. (Addressing aim 1). Different attacker mechanisms of cybersecurity (e.g., Malware, social engineering, hackers). (Addressing aim 2). Different defense mechanisms of cybersecurity (e.g., user awareness, backup, system and App updates).(Addressing aim 3).

The second section content: Data Science in Marketing and Media: Applications of data analytics in understanding digital marketing dynamics. Artificial Intelligence in Marketing Decision-Making: Demonstrating predictive modeling and automation in marketing strategies. Ethical Dilemmas in AI-Driven Marketing: Exploring responsible data use, algorithmic bias, and transparency in AI applications. Behavioral Insights and Consumer Analytics: Understanding how behavioral science enhances data-driven decision-making.

The third section concerns AI with Self-service BI puts data analysis in the hands of non-technical users. In the third section, we’ll cover: Benefits of Self-Service BI for Faster Decision-Making. Reducing IT Dependency and Enhancing Agility. Data Governance for Data Accuracy and Security. Training and User Proficiency to Maximize Self-Service BI’s Potential.

The first section includes the topics: Generative AI as a toolbox for Students; AI Agents; SEO and Generative AI; Ethical implications.

The second section topics are as follows: strategic framework for planning and executing effective digital marketing campaigns; target audiences and insights required to plan a digital marketing strategy; SMART marketing objectives and Key Performance Indicators; development of customer personas representing the key target segments for non-profit organizations; identification of marketing channels that could be utilized to achieve specific marketing goals; understanding the unique challenges and opportunities of non-profit organizations.

The second part of this course aims to provide a comprehensive understanding of artificial intelligence applications in business and services, focusing on enhancing operational efficiency, improving customer experiences, supporting data-driven decision-making, fostering innovation, and gaining a competitive edge in various industries.

The third section aims to provide knowledge on the use of the Internet as a way of communication inside the e-sport sector. The third section covers the following content: the e-sport system; the stakeholders of the sport sector; the sport companies; the management of sport organizations; the sport events; global events and local events; sport funding, organization and communication; the role of Internet; Internet as a tool of promotion.