Big Data Analytics

The course is designed to provide students with the critical theoretical and practical expertise required for big data analysis in the advertising and marketing domains. Students become familiar with data preprocessing and explanatory analysis steps, data visualization and analysis techniques applied in forecasting, classification and clustering tasks. Through real-world scenarios, students will develop the ability to identify and implement theoretical methods, evaluate the accuracy, and interpret results effectively.

The course aims to provide students with the knowledge and skills essential for big data analysis in advertising and marketing

Scientific research methodology

1. Introduction to Data Analytics 2. Descriptive analytics 3. Data visualization 4. Recommendation Systems 5. Market segmentation (data clustering) 6. Predictive analytics: regression and tree algorithms 7. Churn prediction (data classification) 8. Modelling customer choice (multiclass classification) 9. Dimensionality reduction (PCA)

1. The student is able to perform initial data analysis and correctly visualize the data. 2. The student appropriately idenitfies and applies big data analysis techniques. 3. The student properly summarizes, interprets the results of the research, presents reasonable conclusions and formulates evidence-based recommendations. 4. The student demonstrates the ability to use the data visualization and analysis tool Orange for solving standard data analysis tasks. 5. The student demonstrates the ability to communicate, both orally and in writing, using appropriate terminology to present the results of tasks and proposed solutions and to write reports.