(in Polish) Big Data w ekonomii i zarządzaniu WSE-BD-BDEZ
The course “Big Data in Economics and Management” is designed to comprehensively prepare students to understand, analyze, and practically apply large-scale data (big data) in the context of contemporary economic and managerial processes. In today’s era of digital transformation and rapidly growing data availability, the ability to process and interpret data is a key competence in business, public administration, and scientific research.
Throughout the course, students will become familiar with the fundamental concepts, definitions, and theories related to big data, including the characteristics of large-scale data (e.g., the 5Vs: volume, velocity, variety, veracity, value), the typology of structured and unstructured data, and the role of data in decision-making and management processes. The course will also cover the main stages of data processing – from collection and cleaning, through transformation and analysis, to the visualization and interpretation of results.
Students will be introduced to a variety of analytical tools and techniques, including analytical platforms, and cloud computing services. Through practical exercises and projects, students will apply their skills to real-world economic and managerial cases using actual datasets from both the public and private sectors.
A key focus of the course is the development of critical thinking and analytical reasoning in the context of big data. Emphasis will be placed on how data analysis can support decision-making processes – both operational and strategic – through identifying trends, modeling economic phenomena, forecasting outcomes, and assessing risks and performance.
Ultimately, the course aims to prepare students for independent data work, helping them select appropriate analytical methods and formulate accurate conclusions that support rational, data-driven managerial decisions.
Course Objectives:
1. To introduce the basic concepts of big data – including definitions, characteristics, and the role of data in the digital economy.
2. To familiarize students with tools and techniques for analyzing large datasets – including the use of analytical software, programming languages, and methods of data exploration and visualization.
3. To apply big data in practical cases from economics and management – through the use of real-world data and case studies.
4. To develop analytical and interpretative skills in the context of big data – enhancing students' ability to process, structure, and draw conclusions from data.
5. To prepare students for independent work with data and decision-making based on data – promoting a data-driven mindset and the ability to draw rational conclusions for management purposes.
Key Topics Covered in the Course:
• Introduction to big data: definitions, features, and significance for economics and management
• The 5Vs model and the evolution of data in the digital era
• Data sources: internal and external data, open data, social media, IoT
• Types of data: structured, unstructured, and semi-structured data
• Data processing methods: ETL (Extract, Transform, Load), data cleaning and integration
• Basics of statistical and exploratory data analysis
• Data visualization tools
• Predictive and classification analysis in economics and management
• Cloud data processing
• Big data applications in management: process optimization, CRM, marketing, logistics
• Big data applications in economics: market analysis, forecasting, economic modeling
• Data ethics, privacy, and legal aspects of big data usage
• The role of data analytics in strategic and operational decision-making
(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się
(in Polish) E-Learning
(in Polish) Grupa przedmiotów ogólnouczenianych
Subject level
Learning outcome code/codes
Type of subject
Preliminary Requirements
Course coordinators
Learning outcomes
Knowledge (W):
• W1. The student knows and understands the fundamental concepts, definitions, and key characteristics of big data, including the 5V model and data typology.
• W2. The student has knowledge of tools, technologies, and methods for analyzing large datasets used in economics and management.
• W3. The student understands the importance of data in decision-making processes and knows how big data can be applied across various areas of economic and administrative activity.
Skills (U):
• U1. The student is able to acquire, process, and analyze data from various sources using appropriate tools and techniques.
• U2. The student can interpret the results of data analysis and draw conclusions that support managerial and economic decision-making.
• U3. The student is capable of preparing and presenting analysis results in graphical and report form using modern data visualization tools.
Social Competences (K):
• K1. The student is prepared to critically assess data, analysis results, and their application in economic and managerial practice.
• K2. The student understands the ethical, legal, and social implications of working with data, including privacy protection and responsibility for data-driven decisions.
• K3. The student demonstrates readiness for teamwork in analytical projects and is committed to continuous development in the field of data analysis.
Assessment criteria
Assessment criteria for the course ‘Big Data in Economics and Management’
2 – the student has not familiarised themselves with the curriculum, does not demonstrate knowledge of basic concepts in the field of Big Data in economics and management, and has not participated in classes or activities
3 – the student has familiarised themselves with the curriculum to a basic extent, completed the minimum range of tasks necessary to learn the basics of Big Data in economics and management, and participated in classes without demonstrating additional activity
4 – the student has familiarised themselves with the curriculum to a high degree, is able to search for information needed to complete tasks, knows the basic concepts of Big Data in economics and management, and is able to search for information on concepts beyond the basic scope, participated in classes, demonstrating additional activity
5 – the student has familiarised themselves with the curriculum to a very high degree, searching for additional information outside the curriculum, has mastered basic and additional concepts in the field, knows and is able to solve problems in the field of Big Data in economics and management, participated in classes, was an active listener and speaker, and performed additional activities.
Final assessment criteria:
Pass (test) based on activity and work performed by students in the form of case studies, preparation of own papers (essays) and compulsory and additional tasks.
70% final assignment
20% student work (project/essay/case study)
10% class participation
TEACHING METHODS:
Case study, discussion, presentation
Methods of verifying learning outcomes:
final assignment, paper, project.
CLASS SCHEDULE: full-time teaching.
% of points – Assessment:
0-50 – ndst.
51-60 – dost.
61-70 – dost. plus
71-80 – db.
81-90 – db. plus
91-100 – bdb.
Bibliography
J. Wieczorkowski, I. Chomiak-Orsa, I. Pawełoszek, Big Data w zarządzaniu, Polskie Wydawnictwo Ekonomiczne, Warszawa 2021, ISBN 978-83-208-2472-8
D. E. Holmes, Big Data, Wydawnictwo Uniwersytetu Łódzkiego, Łódź 2021. ISBN 978-83-822-0062-1
D. Natingga, Algorytmy Data Science. Siedmiodniowy przewodnik, Wydanie II, Wydawnictwo Helion, Gliwice 2019. ISBN 978-83-283-5603-0
D. Stephenson, Big data, nauka o danych i AI bez tajemnic. Podejmuj lepsze decyzje i rozwijaj swój biznes!, Wydawnictwo Helion, Gliwice 2019. ISBN 978-83-283-5797-6
S. Spałek (pod red.), Analiza danych w zarządzaniu projektami, Wydawnictwo Helion, Gliwice 2020, ISBN 978-83-283-6776-0
F. Buisson, Analiza danych behawioralnych przy użyciu języków R i Pyton, Wydawnictwo Promise, Warszawa 2022. ISBN 978-83-754-1465-3
N. Marz, J. Warren, Big Data. Najlepsze praktyki budowy skalowalnych systemów obsługi danych w czasie rzeczywistym, Wydawnictwo Helion, Gliwice 2016. ISBN 978-83-283-1895-3
S. Lau, J. Gonzalez, D. Nolan, Poznaj Data Science. Przekształcanie, eksplorowanie, wizualizacja i modelowanie danych w Pytonie, Wydawnictwo Promise, Warszawa 2024. ISBN 978-83-754-1563-6
A. J. Gutman, J. Goldmeier, Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym, Wydawnictwo Helion, Gliwice 2023. ISBN 978-83-289-0216-9
W. Weber, T. Zwingmann, Analityka rozszerzona. Automatyzacja i sztuczna inteligencja w podejmowaniu decyzji, Wydawnictwo Helion, Gliwice 2025. ISBN 978-83-289-1970-9
A. Opolska-Bielańska, Logistyka i administrowanie w mediach. Zarządzanie Big Data, Wydawnictwo Aspra, Warszawa 2020. ISBN 978-83-754-5952-4
D. Prokopowicz, S. Gwoździewicz, The Big Data technologies as an important factor of electronic data processing and the development of computerized analytical platforms, Business Intelligence (in:) “International Journal of Small and Medium Enterprises and Business Sustainability”, volume 2, issue 4, November 2017, Center for Industry, SME and Business Competition Studies, University of Trisakti in Jakarta, Indonesia. University of Social Sciences, Warsaw, Poland, pp. 27-42. eISSN: 2442-9368.
D. Prokopowicz, S. Gwoździewicz, J. Grzegorek, M. Dahl, Application of data base systems Big Data and Business Intelligence software in integrated risk management in organization (w:) "International Journal of New Economics and Social Sciences", Międzynarodowy Instytut Innowacji „Nauka-Edukacja-Rozwój”, nr 2 (8) 2018, Warszawa, grudzień 2018, s. 43-56. ISSN 2450-2146.
D. Prokopowicz, S. Gwoździewicz, J. Grzegorek, Wykorzystanie platform analitycznych Big Data Analytics technologii informacyjnych ICT w analizie sentymentu dla wybranej problematyki związanej z Przemysłem 4.0, (w:) P. J. Suwaj, S. Gwoździewicz, K. Samulska (red.), Bezpieczeństwo informacyjne jednostek organizacyjnych. Wybrane problemy, Wydawnictwo Naukowe Akademii im. Jakuba z Paradyża w Gorzowie Wielkopolskim, Gorzów Wielkopolski 2021, s. 101-142. ISBN 978-83-66703-34-6
W. Pizło, O. Kulykovets, D. Prokopowicz, A. Mazurkiewicz-Pizło, A. Kałowski, M. W. Paprocka, E. Stawicka, E. Skarzyńska, The importance of Big Data Analytics technology in business management (w:) “Cybersecurity and Law”, nr 2 (10) 2023, Akademia Sztuki Wojennej w Warszawie, Akademickie Centrum Polityki Cyberbezpieczeństwa, Warszawa 2023, s. 270-282. ISSN 2658-1493 DOI: https://doi.org/10.35467/cal/174940
D. Prokopowicz, A. Kwasek, Doskonalenie metod zarządzania poprzez zastosowanie technologii teleinformatycznych ICT, Business Intelligence i Big Data Analytics (w:) I. Protasowicki (red.) „Bezpieczeństwo i zarządzanie we współczesnych organizacjach. Wybrane zagadnienia”, Uczelnia WSB Merito w Warszawie, Oficyna Wydawnicza Politechniki Rzeszowskiej, Rzeszów 2023, s. 133-142. ISBN 978-83-7934-684-4
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Term 2023/24_L:
J. Wieczorkowski, I. Chomiak-Orsa, I. Pawełoszek, Big Data w zarządzaniu, Polskie Wydawnictwo Ekonomiczne, Warszawa 2021, ISBN 978-83-208-2472-8 D. E. Holmes, Big Data, Wydawnictwo Uniwersytetu Łódzkiego, Łódź 2021. ISBN 978-83-822-0062-1 D. Natingga, Algorytmy Data Science. Siedmiodniowy przewodnik, Wydanie II, Wydawnictwo Helion, Gliwice 2019. ISBN 978-83-283-5603-0 D. Stephenson, Big data, nauka o danych i AI bez tajemnic. Podejmuj lepsze decyzje i rozwijaj swój biznes!, Wydawnictwo Helion, Gliwice 2019. ISBN 978-83-283-5797-6 S. Spałek (pod red.), Analiza danych w zarządzaniu projektami, Wydawnictwo Helion, Gliwice 2020, ISBN 978-83-283-6776-0 F. Buisson, Analiza danych behawioralnych przy użyciu języków R i Pyton, Wydawnictwo Promise, Warszawa 2022. ISBN 978-83-754-1465-3 N. Marz, J. Warren, Big Data. Najlepsze praktyki budowy skalowalnych systemów obsługi danych w czasie rzeczywistym, Wydawnictwo Helion, Gliwice 2016. ISBN 978-83-283-1895-3 S. Lau, J. Gonzalez, D. Nolan, Poznaj Data Science. Przekształcanie, eksplorowanie, wizualizacja i modelowanie danych w Pytonie, Wydawnictwo Promise, Warszawa 2024. ISBN 978-83-754-1563-6 A. J. Gutman, J. Goldmeier, Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym, Wydawnictwo Helion, Gliwice 2023. ISBN 978-83-289-0216-9 W. Weber, T. Zwingmann, Analityka rozszerzona. Automatyzacja i sztuczna inteligencja w podejmowaniu decyzji, Wydawnictwo Helion, Gliwice 2025. ISBN 978-83-289-1970-9 A. Opolska-Bielańska, Logistyka i administrowanie w mediach. Zarządzanie Big Data, Wydawnictwo Aspra, Warszawa 2020. ISBN 978-83-754-5952-4 |
Term 2024/25_L:
J. Wieczorkowski, I. Chomiak-Orsa, I. Pawełoszek, Big Data w zarządzaniu, Polskie Wydawnictwo Ekonomiczne, Warszawa 2021, ISBN 978-83-208-2472-8 D. E. Holmes, Big Data, Wydawnictwo Uniwersytetu Łódzkiego, Łódź 2021. ISBN 978-83-822-0062-1 D. Natingga, Algorytmy Data Science. Siedmiodniowy przewodnik, Wydanie II, Wydawnictwo Helion, Gliwice 2019. ISBN 978-83-283-5603-0 D. Stephenson, Big data, nauka o danych i AI bez tajemnic. Podejmuj lepsze decyzje i rozwijaj swój biznes!, Wydawnictwo Helion, Gliwice 2019. ISBN 978-83-283-5797-6 S. Spałek (pod red.), Analiza danych w zarządzaniu projektami, Wydawnictwo Helion, Gliwice 2020, ISBN 978-83-283-6776-0 F. Buisson, Analiza danych behawioralnych przy użyciu języków R i Pyton, Wydawnictwo Promise, Warszawa 2022. ISBN 978-83-754-1465-3 N. Marz, J. Warren, Big Data. Najlepsze praktyki budowy skalowalnych systemów obsługi danych w czasie rzeczywistym, Wydawnictwo Helion, Gliwice 2016. ISBN 978-83-283-1895-3 S. Lau, J. Gonzalez, D. Nolan, Poznaj Data Science. Przekształcanie, eksplorowanie, wizualizacja i modelowanie danych w Pytonie, Wydawnictwo Promise, Warszawa 2024. ISBN 978-83-754-1563-6 A. J. Gutman, J. Goldmeier, Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym, Wydawnictwo Helion, Gliwice 2023. ISBN 978-83-289-0216-9 W. Weber, T. Zwingmann, Analityka rozszerzona. Automatyzacja i sztuczna inteligencja w podejmowaniu decyzji, Wydawnictwo Helion, Gliwice 2025. ISBN 978-83-289-1970-9 A. Opolska-Bielańska, Logistyka i administrowanie w mediach. Zarządzanie Big Data, Wydawnictwo Aspra, Warszawa 2020. ISBN 978-83-754-5952-4 |
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: