Specialization in Analytics and Big Data Virtual Modality
Professional Profile
As a graduate of the Specialization in Analytics and Big Data, you will be a professional with comprehensive, ethical, and social training who manages a broad vision of Big Data technologies and usability and Analytics. With solid knowledge to design, analyze, configure, implement, and deploy Big Data systems and services in any context, as well as analyze data to add value to society.
General Information
Applicant Profile
Keep the following characteristics in mind to study the Specialization in Analytics and Big Data:
○ You have an affinity for information analysis and want to be professionally involved in these areas.
○ You are autonomous and disciplined to meet the requirements of virtual learning.
○ You have computer skills.
○ You like innovation, teamwork, and you project yourself as a leader.
Occupational Profile
Upon completing the Specialization in Analytics and Big Data, you will have the necessary tools to perform as:
○ Big Data Infrastructure Architect in on-premise or cloud paradigm architecture.
○ Data Analyst and data visualization for knowledge management and discovery.
○ Data Manager aligning business strategy with information for the benefit of the organization, using Artificial Intelligence techniques.
Why study with us?
Our tutorial support spaces in the virtual methodology contribute to fostering aspects such as
○ Flexibility: which makes the students' training relevant, significant, and accessible by respecting their interests, learning styles, and rhythms of knowledge appropriation.
○ Socio-Cultural Relevance: which responds to the adaptation of universal knowledge to their own cultural idiosyncrasy, to the economic possibilities of appropriation, and to the relevance of particular social applications, without excluding the community's own knowledge.
○ Research Training: which welcomes and promotes the participation and inclusion of students in various projects, allowing them access to the research tools typical of the disciplines.
○ Theory-Practice Articulation: understood as the articulation of theories, principles, and models with demonstrative, experimental, and field experiences.
○ Learning Self-regulation: the possibility for students to be trained in conditions of freedom and autonomy within a clear ethical sense.
○ Motivation: the constant awakening of knowledge and challenges faced by teacher-tutors and students in a globalized world.
○ Collaboration: the way of exposing knowledge with other peers and academic communities to work together and strengthen bonds of support, social and emotional competencies.
Program Objective
Different organizations, both public and private, generate, collect, process, store, and require an immense amount of data to qualify their decision-making, both for designing and evaluating public policies, and for advertising or production decisions. For this reason, professionals with the capacity to conduct a rigorous analysis of this data and generate information that adds value, both to the organization and its stakeholders, are increasingly indispensable.
Curriculum
Semester 1
| Subject | Credits |
|---|---|
| Integral elective. Sustainable development | 2 |
| Knowledge Management and Big Data | 3 |
| Research Competencies and Processes | 2 |
| Cloud Computing for Big Data | 3 |
| High Performance Computing for Big Data | 3 |
| Cognitive Computing for Big Data | 3 |
Semester 2
| Subject | Credits |
|---|---|
| Professional Elective | 2 |
| Degree Option | 2 |
| Statistical Methods and Algorithms for Data Analysis | 3 |
| Paradigms for Big Data Storage and Processing | 3 |
| Algorithms and Data Visualization | 3 |