Bachelor's Degree in Data Science
Duration: 8 semesters
Modality: Face-to-face
Call: Annual
Whoever graduates from the Bachelor of Data Science educational program will be able to work in:
- Government institutions
- Public educational institutions
- Linked research institutes or centers
- Non-governmental organizations and non-profit civil society associations
- Educational institutions in the private sector
- Private institutions
- International organizations
- In managing massive amounts of data for organizations
- Proposing appropriate techniques to clean, prepare and optimize data for analysis or consumption
- In the search for order and patterns in the data that allow us to understand, describe or predict phenomena
Train professionals with a comprehensive and critical vision of data science who implement data protection models, protocols and standards, facilitating the understanding of phenomena or scenarios studied, for decision making in an organization; that guarantee the integrity and confidentiality of the data, with a sense aimed at achieving sustainable development, research and innovation in the instance where they operate.
1
Meet the demand for professionals who implement data protection protocols and standards, to maintain their physical and logical integrity, with ethics and responsibility.
2
Train professionals with solid foundations to identify data sources of relevance to the phenomena or scenarios studied, which enable them to carry out tasks of collection, processing, transformation, extraction and visualization of patterns, with an analytical and abstraction attitude.
3
Strengthen competent professionals to implement predictive and descriptive models in large-scale data management for sustainable development, through the application of methodologies and tools that allow the optimization of resources, with a proactive and critical attitude.
It must possess the following characteristics:
Basic knowledge of:
- Arithmetic
- Introduction to Higher Algebra
- Precalculus
- Computing (ophymatics)
Skills:
- Technology management
- Use of application software
- Creative problem solving using technology
- Reading comprehension
- Express yourself appropriately orally and in writing
- Adequately manage times and activities
Attitudes and values:
- Perseverance and discipline
- Organizational capacity
- Willingness to work in interdisciplinary teams
- Critical and reflective sense
- Initiative and proactivity
- Interest in solving business and social problems
- Implement data protection protocols and standards, using methodologies and tools that guarantee their precision and coherence, to maintain your physical and logical integrity, with ethics and responsibility.
- Propose descriptive and predictive models of sustainable exploitation, through the implementation of resource optimization methodologies, to analyze the phenomena of the natural sciences and support decision-making, with an analytical and critical attitude.
- Implement descriptive and predictive models, through methodologies and data analytics tools, to identify strategies business competitiveness, with a critical, purposeful and collaborative attitude.
- Evaluate descriptive and predictive models, using methodologies and data analytics tools, to analyze social phenomena that affect the transfer of knowledge and communication, with ethics and social responsibility.
- Develop strategic projects of a multidisciplinary nature, through project management methodologies, to solve problems in organizations, with a proactive and collaborative attitude.
- Evaluate cutting-edge technologies that organizations require, through data management and analysis techniques, to guarantee the manipulation and appropriate management of data (efficiency, security, integrity, optimization), with creativity, openness to change and work collaborative.
- You must also demonstrate command of English with some of the options: level 4 of the diagnostic exam of the Faculty of Languages, 459 TOEFL points, international stays authorized by the UA, accredit three subjects of a foreign language taught by the UA.
Curriculum
| Subject | Credits | Mandatory |
|---|---|---|
| Oral and Written Communication | 5 | ✓ |
| Algorithm Design | 7 | ✓ |
| Differential Calculus | 8 | ✓ |
| Higher Algebra | 7 | ✓ |
| Vector Geometry | 5 | ✓ |
| History and Impact of Computing | 5 | ✓ |
| Legal, Social and Ethical Aspects of Computing | 5 | ✓ |
| Introduction to Programming | 8 | ✓ |
| Integral calculus | 8 | ✓ |
| Linear Algebra | 8 | ✓ |
| Unix Type Operating Systems | 5 | ✓ |
| Data Structure and Algorithms | 8 | ✓ |
| Object Oriented Programming | 7 | ✓ |
| Vector Calculus | 8 | ✓ |
| Discrete Mathematics | 7 | ✓ |
| Probability | 7 | ✓ |
| Introduction to Text Analysis in English | 8 | |
| Web Development | 7 | |
| General English | 8 |
| Subject | Credits | Mandatory |
|---|---|---|
| Algorithm Analysis | 7 | ✓ |
| Programming for Data Science | 7 | ✓ |
| Ordinary Differential Equations | 8 | ✓ |
| Databases | 7 | ✓ |
| Probability and Statistics | 8 | ✓ |
| Numerical Methods I | 8 | ✓ |
| Acquisition and Processing of Information | 8 | ✓ |
| Supervised Machine Learning | 7 | ✓ |
| Advanced Databases | 7 | ✓ |
| Regression Analysis and Correlation | 7 | ✓ |
| Cryptography | 7 | ✓ |
| Data Mining | 6 | ✓ |
| Pattern Recognition | 7 | ✓ |
| Massive Data | 7 | ✓ |
| Research Methodology | 7 | |
| Introduction to Stochastic Processes and Simulation | 7 | |
| Internet of Things | 7 | |
| Numerical Methods II | 7 | |
| Information Recovery | 7 |
| Subject | Credits | Mandatory |
|---|---|---|
| Cybersecurity | 7 | ✓ |
| Text Mining | 6 | ✓ |
| Signal and Image Processing | 8 | ✓ |
| Analytics and Data Visualization | 7 | ✓ |
| Strategic Leadership and Business Management in Data Science | 7 | ✓ |
| Project Management for Data Science | 6 | ✓ |
| Unsupervised Machine Learning | 8 | ✓ |
| Natural Language Processing | 7 | ✓ |
| Linear Modeling | 8 | |
| Deterministic Simulation | 8 | |
| Bioinformatics | 8 | |
| Bioseignal Processing and Medical Imaging | 8 | |
| Entrepreneurship | 8 | |
| Deep Learning | 8 |
Curriculum Map
