Big Data Engineering and Analysis I- lecture WSE-BD-IABD-I-w
During the course, students will become familiar with a comprehensive approach to Big Data engineering and analysis. The course covers both the theoretical foundations of large-scale data processing and the practical applications of modern technologies and analytical tools used in the digital economy. The content will be complemented by an overview of current market trends in this field.
Students will learn the fundamental concepts, models, and architectures of Big Data systems, including distributed data processing systems (e.g., Hadoop, Spark), NoSQL databases, data stream processing, and the concepts of Data Lake and Data Warehouse. The course will also address current trends in data processing, such as cloud computing, real-time data analytics, AI-driven analytics, and the automation of analytical processes.
Special emphasis will be placed on the data lifecycle – from data acquisition, through cleaning and preparation, modeling and analysis, to visualization and interpretation of results. Throughout the course, students will develop skills in designing and implementing data processing workflows, interpreting analytical outcomes, and creating visualizations that support decision-making processes. An important component of the course will also be the enhancement of presentation skills – students will learn how to present their analytical results in a clear, logical, and persuasive manner, supported by data-driven reasoning and theoretical frameworks
Course Content
1. Introduction to Data Engineering
• Definition and importance of data engineering
• Fundamental concepts related to data management
• The role of large data sets and the use of Big Data in organizations
• The role of data engineering and analytics professionals in an organization
• Case study (e.g., media sector)
2. Data Acquisition, Storage, and Processing
• Big Data and data sources
• Databases and data warehouses
• Storage and analytics of large data sets
• Identifying relationships and dependencies within data
• Data security
• Artificial intelligence, machine learning, and deep learning
• Case study (e.g., banking sector)
3. Machine Learning and Artificial Intelligence in Data Engineering
• Introduction to machine learning
• Machine learning algorithms
• Applying machine learning to data engineering problems
• Benefits of deep learning approaches
• Artificial intelligence in data engineering
• Choosing technologies and programming environments for data engineering
• Case study (e.g., telecommunications sector)
4. Data Analysis and Visualization
• Analysis of large data sets
• Overview of data analysis tools
• Data visualization: importance and applications
• Tools for data visualization
• Case study (e.g., social sector)
(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się
(in Polish) Grupa przedmiotów ogólnouczenianych
(in Polish) Opis nakładu pracy studenta w ECTS
Term 2023/24_Z: Student Workload:
• 15 hours – participation in classes
• 10 hours – individual work (e.g., reviewing literature) | Term 2024/25_Z: Student Workload:
• 15 hours – participation in classes
• 10 hours – individual work (e.g., reviewing literature) | Term 2025/26_Z: Student Workload:
• 15 hours – participation in classes
• 10 hours – individual work (e.g., reviewing literature)
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Subject level
Learning outcome code/codes
Type of subject
Course coordinators
Learning outcomes
Knowledge:
• W01. Has structured and in-depth knowledge of Big Data engineering.
• W02. Possesses knowledge in the field of data acquisition, refinement, and storage in Big Data environments.
• W03. Understands methods for conducting analyses based on Big Data.
Skills:
• U01. Is able to select and apply appropriate methods and procedures for building large data sets.
• U02. Is able to identify and acquire Big Data related to social phenomena across various domains.
Social Competences:
• K01. Is prepared to design and implement social projects involving data analysis.
• K02. Is capable of interdisciplinary collaboration in the performance of assigned tasks.
Assessment criteria
50% – participation and engagement during classes,
20% – project implementation,
20% – justification (in discussion) of adopted assumptions and solutions,
10% – responses to individual questions.
Bibliography
Firley-Buzon, A. (2020). Big Data in the Humanities and Social Sciences. Warsaw.
Holmes, D. (2021). Big Data. University of Łódź Press, Łódź.
Szeliga, M. (2019). Practical Machine Learning. PWN Scientific Publishers, Warsaw.
Szeliga, M. (2017). Data Science and Machine Learning. PWN Scientific Publishers, Warsaw.
Kapczyński, A. (2021). Introduction to Big Data: Theoretical and Practical Aspects. Lublin.
Kisielnicki, J. (2014). Management and Informatics. Placet Publishing House, Warsaw.
Jemielniak, D., & Przegalińska, A. (2020). The Cooperation Society. Scholar Scientific Publishing, Warsaw.
Przegalińska, A., & Jemielniak, D. (2023). Strategizing AI in Business and Education. Cambridge University Press, Cambridge.
Śledziewska, K., & Włoch, R. (2020). The Digital Economy: How New Technologies Are Changing the World. University of Warsaw Press, Warsaw.
Stephenson, D. (2020). Big Data, Data Science, and AI Without Secrets. Helion Publishing, Gliwice.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: