Master's in Big Data Analysis and Visualization

This course provides the foundational concepts and techniques required to identify potential data sources and evaluate their applicability using data quality parameters. Students will work with MongoDB, a leading NoSQL database, gaining hands-on experience in managing and structuring large datasets. Special emphasis is placed on schema design patterns in document-oriented databases, reflecting the growing importance of this technology in modern data ecosystems.

In this course, students will explore the role and impact of data science in today’s society. Through the analysis of real-world use cases, strategies, and projects, they will learn how to identify technological solutions that generate value from data within organizations. The course encourages critical thinking and a practical understanding of how data-driven initiatives support business decision-making

This subject introduces the core principles of statistics and data analysis. Students will develop the ability to reason statistically, understand relationships between variables, and interpret data effectively. The course covers data visualization techniques, linear relationships, and places special emphasis on inferential statistics, enabling students to draw meaningful conclusions from data.

This course focuses on the technologies and frameworks used for large-scale data processing. Students will learn how to address the three key dimensions of big data: volume, variety, and velocity. The program introduces widely used tools and architectures that enable efficient data processing, preparing students to work with complex and large-scale data environments.

Students will explore how artificial intelligence enables machines to replicate human cognitive abilities such as pattern recognition, learning from past experiences, and decision-making. The course covers key machine learning concepts and methods, with a focus on techniques most relevant to data mining and large-scale data analysis. Practical applications are emphasized to solve real-world problems using AI.

This course focuses on the graphical representation of data through charts, maps, diagrams, and interactive tools. Students will learn how to present data effectively to different audiences, from general users to specialized professionals, while also using visualization as a tool for data exploration and insight generation. The course introduces the fundamental principles of visual analytics, a discipline widely applied across industries.

This course explores how organizations store, manage, protect, and leverage data to support strategic decision-making. Students will learn about key concepts such as data warehouses, data lakes, and master data management. The course also addresses how data can enhance customer interactions, particularly in marketing, and how to align data strategies with business objectives.

With a strong practical focus, this course enables students to create their own data visualizations using industry-standard tools and libraries. Students will work with technologies such as Google Charts, jQuery plugins, and D3.js, one of the most powerful visualization libraries available. By working with real or self-selected datasets, students will gain hands-on experience in transforming data into compelling visual stories.

This course teaches students how to design and implement business strategies supported by data and business intelligence systems. It focuses on adapting data-driven strategies to different organizational structures and understanding the regulatory framework surrounding data usage. Topics include data privacy, data protection, and international data transfer regulations.

The objective of this course is to analyze the processes and phases involved in auditing information systems. Students will learn how to ensure the security of applications deployed in online environments and their associated data repositories, including database management systems and directory services. The course emphasizes best practices for achieving optimal data security.

In the Capstone Project, students will tackle a real-world problem through a practical and hands-on approach. They will develop an original project from scratch, applying the knowledge and skills acquired throughout the program. Each student will be guided by a faculty advisor and, upon completion, will present their project to an academic committee.

This final project allows students to consolidate their learning and demonstrate their ability to apply data analytics, visualization, and big data techniques in a professional context.