Big Data 2600-ABdz1BDf
The classes are to discus such issues as:
• The concept of big data (the origins of the phenomenon, the definition of big data, aspects of the phenomenon)
• Aspects: technological, economic, and social.
• Fundamentals of data systems (Reliable, scalable, and easy-to-maintain applications, Data models and query languages, Data storage and retrieval, Encoding and changes)
• Big data architecture (Basic assumptions, Data sources)
• Data acquisition and pre-processing (Ways to access data, Real-time data acquisition, Example of the Apache Kafka solution)
• Data processing and analysis (Basic principles, Functions of a big data platform using Apache Spark as an example)
• Data storage models (Data storage architecture, In-memory processing, Methods of storing data in mass storage)
Type of course
Course coordinators
Learning outcomes
K_W01 in-depth research methodology and terminology in the field of management and quality science, in particular in the field of business data analysis and complementary disciplines (economics and finance).
K_W02 in-depth knowledge of complex processes and phenomena occurring in various types of organizations and in the world around them, uses management theory to identify, diagnose, and solve problems related to the functioning of organizations and their integration within the organization's strategy based on the results of analyses.
K_W05 complex technological, social, legal, economic, ethical, and environmental processes and phenomena, including those related to the use of numerical data and their impact on the functioning of organizations and the economy as a whole.
K_U01 use the theory of management and quality science, in particular in the field of numerical data analysis, to identify, diagnose, and solve complex and unusual problems related to key functions in an organization, in particular reasoning, strategy development, and business decision-making.
K_U03 adapt existing methods and tools or propose new ones based on them, using advanced information and communication techniques and the appropriate selection of sources to identify, diagnose, and solve problems related to data analysis in the internal and external environment of the organization.
K_U09 improve acquired qualifications and support others in this regard, and have the ability to self-educate
Assessment criteria
Test, 30 questions, closed questions, 4 answers, only one correct, 60% passes. Grading scale: 30 points 5; 27 points 4.5; 24 points 4; 21 points 3.5; 18 points 3; <18 points 2; test taken in the classroom on the Kampus web platform on student’s own computer or on paper. Date: first in the classroom; second remotely using the Kampus web platform.
Attendance at classes is mandatory.
Practical placement
Internships are not required for completion of the course.
Bibliography
•Wieczorkowski J., Chomiak-Orsa I., Pawełoszek I., Big data w zarządzaniu, Polskie Wydawnictwo Ekonomiczne, Warszawa 2021,
•Marz N., Warren J., Lachowski L., Wydawnictwo Helion, Big data: najlepsze praktyki budowy skalowalnych systemów obsługi danych w czasie rzeczywistym, Gliwice 2016.
•Kleppmann M., Walczak T., Grupa Wydawnicza Helion, Przetwarzanie danych w dużej skali: niezawodność, skalowalność i łatwość konsekwencji systemów, Gliwice 2018.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: