Bachelor's Degree in Data Engineering and Artificial Intelligence
RVOE 20233646
Objectives:
To train graduates in Data Engineering and Artificial Intelligence with knowledge, skills and abilities in Data Engineering and Artificial Intelligence capable of preparing infrastructures for large masses of data for their subsequent analysis, of designing and building intelligent systems capable of integrating data from various resources and managing large volumes of data with the aim of optimizing the performance of the data ecosystem of a company, organization or entity. In addition, of converting raw data into knowledge, applying statistical techniques, intelligent learning, and pattern recognition, which allow solving critical problems. With skills for the creation of innovation and entrepreneurship projects in a digital context.
First quarter:
- Programming I
- Calculus I
- Linear algebra
- Introduction to data science and artificial intelligence engineering
- Emotional intelligence
Second quarter:
- Programming II
- Calculus II
- Linear systems
- Probabilities and data analysis
- Digital evolution
Third quarter:
- Data structure
- Calculus III
- Numerical methods
- Advanced spreadsheet
- Innovation I: formulation and validation of business idea
Four quarter:
- Automata and compiler theory
- Database
- Cybersecurity
- Statistical modeling
- Leadership and teamwork
Fifth quarter:
- Programming III
- Web programming
- Artificial intelligence
- Statistical learning
- Innovation II: Development of a business idea prototype
Sixth quarter:
- Computational perception
- Communications networks and services
- Data acquisition systems
- Statistical signal processing
- Meditation
Seventh quarter:
- Machine Learning I
- Sensor networks
- Programming for blog data
- Web analytics
- Innovation III: Raising capital
Eighth quarter:
- Predictive modeling
- Cloud computing
- Communication systems for data engineering
- Analytical optimization
- Personal Finance
Ninth quarter:
- Machine Learning II
- Ethics in artificial intelligence
- Intelligence for data science
- Massive computing
- Innovation IV: Legal constitution and commercial launch of the business
RVOE 20233646
Objectives:
To train graduates in Data Engineering and Artificial Intelligence with knowledge, skills and abilities in Data Engineering and Artificial Intelligence capable of preparing infrastructures for large masses of data for their subsequent analysis, of designing and building intelligent systems capable of integrating data from various resources and managing large volumes of data with the aim of optimizing the performance of the data ecosystem of a company, organization or entity. In addition, of converting raw data into knowledge, applying statistical techniques, intelligent learning, and pattern recognition, which allow solving critical problems. With skills for the creation of innovation and entrepreneurship projects in a digital context.
First quarter:
- Programming I
- Calculus I
- Linear algebra
- Introduction to data science and artificial intelligence engineering
- Emotional intelligence
Second quarter:
- Programming II
- Calculus II
- Linear systems
- Probabilities and data analysis
- Digital evolution
Third quarter:
- Data structure
- Calculus III
- Numerical methods
- Advanced spreadsheet
- Innovation I: formulation and validation of business idea
Four quarter:
- Automata and compiler theory
- Database
- Cybersecurity
- Statistical modeling
- Leadership and teamwork
Fifth quarter:
- Programming III
- Web programming
- Artificial intelligence
- Statistical learning
- Innovation II: Development of a business idea prototype
Sixth quarter:
- Computational perception
- Communications networks and services
- Data acquisition systems
- Statistical signal processing
- Meditation
Seventh quarter:
- Machine Learning I
- Sensor networks
- Programming for blog data
- Web analytics
- Innovation III: Raising capital
Eighth quarter:
- Predictive modeling
- Cloud computing
- Communication systems for data engineering
- Analytical optimization
- Personal Finance
Ninth quarter:
- Machine Learning II
- Ethics in artificial intelligence
- Intelligence for data science
- Massive computing
- Innovation IV: Legal constitution and commercial launch of the business