This repository is a structured archive of my projects and assignments completed during my second year Master's M2 in Data Science. Each directory corresponds to a specific course module and contains relevant notebooks, python scripts, and summaries.
Algorithms_for_Data_Science: Theory and Jupyter Notebooks of the most important algorithms used in Data Science.Semantic_Web_&_Ontologies: Theory and exercises related to the Semantic Web and Ontologies, providing a solid understanding of foundational concepts and reasoning tasks.Signal_Processing: Jupyter Notebooks with solved exercises and clearly explained theory, covering fundamental concepts and techniques in Signal Processing.Optimization: Materials and solved exercises covering optimization techniques, algorithms, and applications in data science.Web_of_Data: Projects and theory related to Linked Data and Web of Data, focusing on integration, querying, and semantic representation of information on the web.Social_Graph_&_Data_Management: Analysis of social networks, graph theory, and community detection. Includes exercises on modularity, spreading phenomena, probabilistic graphs, and influence algorithms.Communication: Materials related to the theory and practice of communication within the context of data science, including techniques for presenting data insights.Constraints_&_Data_Mining: Theory and exercises on constraint satisfaction problems and their applications in data mining.Interactive_Information_Visualization: Projects focused on creating interactive visualizations to represent data and insights effectively.Knowledge_Discovery_Graph_Data: Exercises and theory related to knowledge discovery using graph-based data structures and algorithms.
Clone the repository to explore the materials:
git clone https://github.com/Pablo-Molla-Charlez/Master_M2_Data_Science.gitYour contributions are welcome! Please fork the repository, create a feature branch, and submit your pull request for review.