(in Polish) Algorytmy specjalnego zastosowania w Big Data (I)- wykład WSE-BD-ASZwBD(I)-w

Module 1: Cluster Analysis and User Profiling

1. Cluster analysis – K-Means methods – Basic techniques for grouping objects in Big Data.
2. Advanced clustering techniques – Data segmentation in more complex datasets.
3. User profiling and model curve fitting – Identifying patterns in data.

Module 2: Gradational Analysis and Correspondence Analysis
4. Gradational correspondence analysis – introduction – A method for exploring qualitative data.
5. Advanced models of gradational correspondence analysis.
6. Application of gradational correspondence analysis in Big Data – Analysis of consumer behavior and exploration of relationships between qualitative variables.

Module 3: Social Network Analysis (SNA)
7. Basics of social network analysis (SNA) – Network structure, centrality degree, connection analysis.
8. Applications of SNA in research – Influence analysis, detection of key nodes in a network.
9. Modeling interactions and information diffusion in networks – How Big Data supports the analysis of network dynamics.

Module 4: Market Basket Analysis and Association Algorithms
10. Market basket analysis – Introduction to the Apriori algorithm – Discovering relationships between products based on transactional data.
11. Advanced market basket analysis algorithms.
12. Application of market basket analysis in marketing and e-commerce – Personalization of recommendations and prediction of purchasing trends.

Module 5: Random Forest and Ensemble Methods
13. Random Forest – principles of operation and algorithm structure – Introduction to the method, division into classification and regression.
14. Application of Random Forest in Big Data – Use in predictive and exploratory analysis.

(in Polish) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

(in Polish) Opis nakładu pracy studenta w ECTS

Direct participation in classes: 30 hours Participation in assessments outside of class: 2 hours Participation in consultations: 15 hours Total: 2 ECTS Independent work: Preparation for classes (reading, written work, translation, etc.): 10 hours Preparation for assessment (e.g. reading, presentation, project, etc.): 20 hours Total: 1 ECTS

Subject level

elementary

Learning outcome code/codes

enter learning outcome code/codes

Type of subject

obligatory

Course coordinators

Agnieszka Szymańska

Learning outcomes

Knowledge:

Knows basic and advanced data analysis methods used in Big Data, such as cluster analysis, correspondence analysis, SNA, and association algorithms.

Understands the principles of key algorithms, including K-Means, Apriori, Random Forest, and methods used in user segmentation.

Is familiar with the applications of these techniques in user profiling, consumer behaviour analysis, and the study of social network structures.

Skills:

Can perform cluster analysis, data segmentation, and basic user profiling.

Is able to apply gradational correspondence analysis and association algorithms to discover hidden patterns in large datasets.

Can carry out basic social network analysis (SNA) and use Random Forest for predictive tasks in Big Data.

Social Competences:

Understands the importance of responsible use of data analysis algorithms in the context of privacy and ethics.

Can critically evaluate analytical results and correctly interpret relationships uncovered in qualitative and quantitative data.

Is prepared to independently expand knowledge in modern data exploration methods and their practical applications.

Assessment criteria

The basis for passing the course is a test covering the knowledge acquired during the lecture. To pass the test, a student must provide 60% correct answers.

Bibliography

Elder, J., Hill, T., Miner, G., Nisbet, B., Delen, D., & Fast, A. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Application. Oxford: Elsevier.

Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical analysis and data mining applications. Burlington, MA: Academic Press (Elsevier).

Szymańska, A. (2017a). Wykorzystanie algorytmów Text Mining do analizy danych tekstowych w psychologii [Usage of text mining algorithms to analyze textual data in psychology]. Socjolingwistyka, 33, 99–116.

Szymańska, A. (2017b). Wykorzystanie Analizy Skupień Metodą Data Mining Do Wykreślania Profili Osób Badanych. Studia Psychologiczne, 55, 26–42. https://doi.org/10.2478/V1067-010-0160-1

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of WSE-BD-ASZwBD(I)-w in USOSweb