This project is part of a medical data analysis course that I am developing with a colleague. It uses Quarto and R to introduce students to practical healthcare data analysis workflows. Using Kaggle’s Mental Health in Tech Survey dataset, the project demonstrates how to load, clean, explore, visualize, and model medical survey data. The analysis includes data preparation, exploratory data analysis, correlation analysis, visualizations with ggplot2, and a simple logistic regression model to predict mental health treatment-seeking behavior. This project is designed as a beginner-friendly learning module for students, healthcare researchers, and early-career data analysts interested in applying R to real-world health data.

Analýza vývoje úhrad zdravotnických prostředků hrazených zdravotními pojišťovnami v letech 2020–2024. Report sleduje dlouhodobé trendy, sezónnost, rozklad růstu na objemový a cenový efekt, koncentraci úhrad (Gini, HHI), rozdíly mezi typy péče, odbornostmi, skupinami prostředků a regiony.

This study applies data analytics to LTE network performance data to identify the key drivers of customer experience degradation during peak traffic periods. Using a structured approach combining exploratory data analysis, visualisation, hypothesis testing, correlation, and regression modelling, the analysis demonstrates that network congestion—driven primarily by high resource utilisation and traffic demand—is the dominant factor reducing throughput. A Capacity Stress Framework was developed to identify high-risk cells, enabling targeted, data-driven recommendations for capacity investment to improve network performance and optimise resource allocation.

This study applies data analytics to LTE network performance data to identify the key drivers of customer experience degradation during peak traffic periods. Using a structured approach combining exploratory data analysis, visualisation, hypothesis testing, correlation, and regression modelling, the analysis demonstrates that network congestion—driven primarily by high resource utilisation and traffic demand—is the dominant factor reducing throughput. A Capacity Stress Framework was developed to identify high-risk cells, enabling targeted, data-driven recommendations for capacity investment to improve network performance and optimise resource allocation.

Data Analysis Exam

E5 Anna K

This study applies data analytics to LTE network performance data to identify the key drivers of customer experience degradation during peak traffic periods. Using a structured approach combining exploratory data analysis, visualisation, hypothesis testing, correlation, and regression modelling, the analysis demonstrates that network congestion—driven primarily by high resource utilisation and traffic demand—is the dominant factor reducing throughput. A Capacity Stress Framework was developed to identify high-risk cells, enabling targeted, data-driven recommendations for capacity investment to improve network performance and optimise resource allocation.