Data Science: Qualitative and Quantitative Analysis II (Rescheduling)

This research project addresses the critical need to integrate and systematize heterogeneous data in the health and education sectors in Ecuador. Faced with the lack of unified systems, the team proposes a solution based on Big Data, data mining, and distributed cloud architectures to facilitate informed decision-making. The methodological approach combines qualitative and quantitative analysis, utilizing advanced artificial intelligence tools and natural language processing. Beyond data integration, the project includes the development of a virtual laboratory for GNU/Linux practices, allowing students to access learning environments without physical restrictions. Additionally, a cybersecurity training model is designed, analyzing cognitive factors to mitigate social engineering risks. Expected outcomes include the creation of optimized data warehouses, computing platforms for research groups, and scientific publications. The impact focuses on improving the quality of public health services and strengthening the technical capabilities of students at the Universidad Politécnica Salesiana, promoting a more efficient and resilient technological infrastructure.<br/><br/><b>Goal</b>: <br/>Develop a comprehensive qualitative and quantitative data analysis system to improve decision-making in the health and education sectors in Ecuador. The project aims to integrate distributed cloud architectures, artificial intelligence, and cybersecurity models to optimize information management.<br/><br/><b>Research lines</b>: <br/>Data science and simulation