Big Data course applied to Urban Mobility
About our
The Big Data Applied to Urban Mobility course explores the use of big data analytics to optimize transportation systems and urban planning. It focuses on applying big data tools and techniques to analyze traffic, mobility, and user behavior data, with the goal of improving efficiency and sustainability in cities. Topics covered include traffic modeling, route optimization, and demand forecasting, using platforms such as Spark and Hadoop. The course aims to train professionals capable of analyzing complex data to make strategic decisions in the field of smart urban mobility. Participants will gain skills in the use of visualization tools and predictive analytics applied to public transport management and urban planning. The training prepares participants for roles such as mobility data analysts, traffic engineers, and urban planning consultants, with the goal of transforming mobility in cities.
Big Data course applied to Urban Mobility
- Format: Online
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 2
550 $
Competencies and results
What you will learn
1. Big Data Analysis for Urban Mobility Optimization
- Identification and extraction of relevant data from diverse sources (traffic sensors, social media, weather data, etc.).
- Application of data cleaning and preprocessing techniques to ensure data quality and usefulness.
- Use of big data analytics tools and platforms (Hadoop, Spark, etc.).
- Implementation of predictive analytics models to anticipate urban mobility patterns (traffic prediction, public transport demand, etc.).
- Development of optimization algorithms to improve urban transport efficiency (optimized routes, traffic light management, etc.).
- Data visualization and report creation to communicate findings and recommendations.
- Evaluation of the impact of proposed solutions on urban mobility (congestion reduction, improved air quality, etc.).
- Application of machine learning techniques for the Analysis and optimization of intelligent transportation systems.
2. Value Extraction and Strategic Decision Making with Big Data in Urban Mobility
2. Value Extraction and Strategic Decision Making with Big Data in Urban Mobility
- Identify and analyze key data sources in urban mobility (sensors, applications, social networks, etc.).
- Apply data cleaning and preprocessing techniques to ensure the quality and consistency of the information.
- Use Big Data tools and platforms (Hadoop, Spark, etc.) for storing and processing large volumes of data.
- Develop predictive models for forecasting transportation demand, route optimization, and traffic management.
- Implement machine learning algorithms for pattern identification, anomaly detection, and user segmentation in the field of urban mobility.
- Visualize and communicate results through dashboards and interactive reports for decision-making.
- Evaluate the impact of policies on Mobility based on the data obtained.
Apply data analysis for infrastructure optimization and urban planning.
Understand the ethical and privacy aspects related to the use of data in urban mobility.
Develop monetization and value creation strategies from data analysis in the mobility sector.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Discovering the Future of Mobility: Learning with Big Data
- Exploring the sources and volume of big data relevant to mobility.
- Understanding big data processing and analysis techniques applied to the transportation sector.
- Application of predictive analytics tools to optimize traffic and logistics management.
- Identifying patterns and trends in user behavior and mobility demand.
- Developing simulation models and future scenarios based on big data.
- Evaluating the impact of connected and autonomous mobility on infrastructure and services.
- Analyzing the ethical and privacy implications related to data use in mobility.
- Integrating big data into urban planning and the design of intelligent transportation systems.
5. Predicting Mobility Trends and Behaviors with Big Data
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Identify mobility patterns by analyzing large datasets (Big Data).
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Apply predictive models to anticipate future trends in mobility behavior.
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Understand the factors that influence urban and regional mobility, such as infrastructure, demographics, and the economy.
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Develop data-driven strategies to optimize traffic management and improve transportation planning.
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Evaluate the impact of new technologies, such as autonomous vehicles and shared mobility platforms, on mobility patterns.
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Learn to use Big Data platforms and analytical tools to process and analyze large volumes of mobility data.
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Interpret results from predictive models and communicate findings effectively through reports and presentations.
Use advanced tools and techniques for visualizing and exploring mobility data.
6. Implementation of Smart Mobility Solutions with Big Data
6. Implementation of Smart Mobility Solutions with Big Data
- Big Data Fundamentals in Smart Mobility: Exploration of key concepts, data architectures, and essential technologies for storing, processing, and analyzing large volumes of information generated by transportation devices and systems.
- Mobility Data Collection and Management: Techniques and tools for capturing, integrating, and cleaning data from diverse sources, such as vehicle sensors, mobile devices, road infrastructure, and social networks.
- Data Analysis for Smart Mobility: Application of advanced data analysis techniques, including data mining, machine learning, and predictive analytics, to extract valuable information about traffic patterns, user behavior, and resource optimization.
- Smart Mobility Application Development: Design and creation of data-driven solutions, such as real-time traffic management systems, intelligent navigation applications, ride-sharing platforms, and Mobility as a Service (MaaS).
- Data Visualization and Results Communication: Use of data visualization tools to effectively communicate findings and analysis results, facilitating data-informed decision-making and the presentation of relevant information to different audiences.
- Ethics and Privacy in Smart Mobility: Consideration of ethical aspects related to the collection, use, and protection of personal data in the context of smart mobility, as well as compliance with data privacy and security regulations.
- Case Studies and Practical Applications: Analysis of relevant case studies and development of practical projects to apply the knowledge acquired in the design, implementation, and evaluation of smart mobility solutions based on Big Data.
Who this program is for:
Big Data course applied to Urban Mobility
- Professionals and students of Data Engineering, Computer Science, Statistics, or related fields interested in the application of Big Data in urban environments.
- Data analysts, data scientists, software engineers, and other professionals seeking to specialize in the analysis and management of data related to urban mobility.
- Project managers, transport managers, urban planners, and other professionals in the mobility sector who wish to improve their understanding and application of Big Data techniques for decision-making.
- Entrepreneurs and startups seeking to develop innovative, data-driven solutions to improve urban mobility.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Module 1 — Introduction to Big Data in Urban Mobility
1.1 Definition and Key Concepts of Big Data in Urban Mobility
1.2 Sources of Big Data in the Context of Urban Mobility
1.3 Importance of Big Data for Traffic Optimization
1.4 Basic Tools and Technologies for Data Analysis
1.5 Challenges and Opportunities of Using Big Data in Mobility
1.6 Ethics and Privacy in Data Collection and Use
1.7 Introductory Case Studies: Application Examples
1.8 Introduction to Data Visualization and Dashboards
1.9 The Future of Big Data in Urban Planning
1.10 Introduction to Machine Learning and AI Concepts in Mobility
2. Introduction to Big Data in Urban Mobility: The Overview
2. What is Big Data and How is it Applied to Mobility?
3. Sources of Big Data in Urban Mobility
4. Types of Data: Structured, Unstructured, and Semi-structured
5. Tools and Technologies for Big Data Analysis in Mobility
6. Benefits of Big Data for Traffic Planning and Management
7. Success Stories: Real-World Applications of Big Data in Urban Mobility
8. Challenges and Ethical Considerations in the Use of Big Data
9. Introduction to the Smart Mobility Ecosystem
20. The Future of Big Data in Urban Mobility: Trends and Opportunities
3. Introduction to Big Data in Urban Mobility
2. Sources of Big Data in Mobility
3. Data Analysis Tools and Technologies
4. Data Visualization and Reporting for Mobility
5. Case Study: Traffic Data Analysis
6. Ethics and Privacy in Mobility Data Analysis
7. Challenges and Opportunities of Data Analysis
8. Hands-on Practice with Data Analysis Tools
2.3 Data Collection and Storage
2.2 Data Modeling and Structures for Mobility
2.3 Descriptive and Exploratory Data Analysis
2.4 Predictive Analysis in Urban Mobility
2.5 Data-Driven Decision Making
2.6 Public Transportation Optimization Strategies
2.7 Profitability Analysis in Mobility Projects
2.8 Implementation Case Studies
3.3 Introduction to Digital Transformation in Mobility
3.2 Data Architecture for Mobility
3.3 Data Systems Integration
3.4 Artificial Intelligence and Machine Learning in Mobility
3.5 Route and Fleet Optimization
3.6 Smart Cities and Sustainable Mobility
3.7 Innovation in Shared Transportation
3.8 Impact of Transformation on Urban Planning
4.3 Fundamentals of Machine Learning
4.2 Learning Algorithms for Mobility
4.3 Supervised and Unsupervised Learning
4.4 Neural Networks and Deep Learning
4.5 Applications of Machine Learning
4.6 Predictive Models for Traffic
4.7 Sentiment and Behavior Analysis
4.8 Future of Machine Learning in Mobility
5.3 Identifying Trends in Transportation
5.2 Time Series Analysis in Mobility
5.3 Modeling User Behavior
5.4 Transportation Demand Forecasting
5.5 Analysis of Urban Mobility Patterns
5.6 Congestion and Traffic Flow Forecasting
5.7 Impact Assessment of New Technologies
5.8 Adaptation and Planning Strategies
6.3 Definition of Smart Mobility
6.2 Technologies for Smart Mobility
6.3 Intelligent Traffic Management Systems
6.4 Implementation of Traveler Information Systems
6.5 Integration of Connected Vehicles
6.6 Charging Infrastructure for Electric Vehicles
6.7 Smart City Design
6.8 Implementation Success Stories
7.3 Big Data Fundamentals
7.2 Big Data Collection
7.3 Data Storage and Management
7.4 Data Analysis with Advanced Tools
7.5 Data Visualization and Communication
7.6 Innovative Applications in Urban Mobility
7.7 Challenges and Opportunities
7.8 Future Trends in Data Analysis
8.3 Introduction to Traffic Optimization
8.2 Vehicular Flow Analysis
8.3 Traffic Modeling and Simulation
8.4 Intelligent Traffic Control Systems
8.5 Dynamic Traffic Signal Management
8.6 Route Planning and Detours
8.7 Real-Time Monitoring and Analysis
8.8 Best Practices and Case Studies
4.4 Predictive Modeling of Urban Mobility Flows
4.2 Analysis of Historical and Real-Time Data for Simulating Future Scenarios
4.3 Machine Learning Applied to Route Optimization and Public Transport Management
4.4 Identification of User Behavior Patterns and Preferences
4.5 Data Visualization and Dashboard Creation for Decision-Making
4.6 Evaluation of the Impact of New Technologies on Mobility (Autonomous Vehicles, V2X)
4.7 Design of Data-Driven Urban Mobility Strategies
4.8 Development of Simulation Models to Predict Congestion and Travel Times
4.9 Application of Deep Learning Techniques for Data Classification and Segmentation
4.40 Case Studies: Emerging Trends in Mobility and Their Analysis with Big Data
5.5 Introduction to Big Data in Urban Mobility
5.5 Data Sources: Sensors, GPS, Social Networks
5.3 Data Cleaning and Preparation: Techniques and Tools
5.4 Data Visualization: Interactive Dashboards and Charts
5.5 Descriptive Analysis: Trends and Patterns
5.6 Exploratory Data Analysis (EDA): Discovering Insights
5.7 Tools and Platforms for Data Analysis
5.8 Case Studies: Practical Applications
5.9 Challenges and Ethical Considerations in Data Collection
5.50 Data Analysis Practices
5.5 Data Collection and Organization Strategies
5.5 Data Mining for Decision Making
5.3 Audience Segmentation and Service Personalization
5.4 Identifying Business Opportunities
5.5 Revenue Models and Data Monetization
5.6 Cost-Benefit Analysis in Mobility Projects
5.7 Risk Assessments and Mitigation
5.8 Case Studies: Data-Driven Decision Making
5.9 Legal Framework and Regulatory Compliance
5.50 Implementation of Data Strategies
3.5 Introduction to Digital Transformation in Mobility
3.5 Big Data Architecture for Mobility
3.3 Integration of Data from Multiple Sources
3.4 Machine Learning for Demand Forecasting
3.5 Route Optimization and Fleet Management
3.6 Traffic Simulation Modeling
3.7 Optimization of Public Transportation Networks
3.8 Smart Cities and Sustainable Mobility
3.9 Environmental Impact of Mobility
3.50 The Future of Mobility
4.5 Introduction to Machine Learning for Mobility
4.5 Algorithms of Supervised and Unsupervised Learning
4.3 Predictive Models for Transportation Demand
4.4 Anomaly Detection in Mobility Data
4.5 The Role of Machine Learning in Urban Planning
4.6 Development of Predictive Models
4.7 Model Evaluation and Validation
4.8 Case Studies: Applications of Machine Learning
4.9 Future Trends in Machine Learning for Mobility
4.50 Integration of Models in the Real World
5.5 Time Series and Trend Analysis
5.5 Traffic Flow and Congestion Prediction
5.3 Forecasting Public Transportation Demand
5.4 Sentiment and User Behavior Analysis
5.5 Behavioral Modeling in Transportation
5.6 Incident and Accident Forecasting
5.7 Predictive Models in Planning Urban Mobility
5.8 Impact Assessment of Mobility Policies
5.9 Applications of Artificial Intelligence in Mobility
5.50 Future of Mobility Prediction
6.5 Introduction to Smart Mobility Solutions
6.5 Infrastructure for Smart Mobility
6.3 Intelligent Transportation Systems (ITS)
6.4 Intelligent Traffic Management
6.5 Mobility as a Service (MaaS) Platforms
6.6 Integration of Connected Vehicles
6.7 Implementation of Smart Parking Systems
6.8 Case Studies: Implementation of Smart Mobility Solutions
6.9 Challenges and Opportunities in Smart Mobility
6.50 The Future of Smart Mobility
7.5 Mobility Data Collection and Curation
7.5 Database Design and Structure
7.3 Techniques for Data Visualization for Mobility
7.4 Application of Machine Learning Algorithms
7.5 Innovation in Urban Planning
7.6 Predictive Demand Analysis
7.7 Identifying Patterns and Trends
7.8 Public Transport Optimization
7.9 Smart City Design
7.50 Ethics and Privacy in Data Collection
8.5 Introduction to Urban Traffic Optimization
8.5 Data Sources for Traffic Optimization
8.3 Traffic Modeling and Simulation
8.4 Route Optimization Techniques
8.5 Intelligent Traffic Signal Management
8.6 Public Transport Planning with Big Data
8.7 Data Analysis for Mobility Improvement
8.8 Implementation of Traveler Information Systems
8.9 Evaluating the Impact of Traffic Policies
8.50 The Future of Traffic Optimization
6.6. Fundamentals of Smart Mobility: Concepts and Architectures
6.2. Data Collection and Preparation: Sources and Methods for Urban Mobility
6.3. Descriptive Data Analysis: Visualization and Summarization for Understanding Traffic
6.4. Predictive Modeling: Forecasting Mobility Flows and Demand
6.5. Transportation Optimization: Algorithms and Data-Driven Strategies
6.6. Geographic Information Systems (GIS) in Urban Mobility
6.7. Smart Application Design: Interfaces and User Experience
6.8. Impact Assessment and KPIs: Measuring the Success of Solutions
6.9. Data Governance and Ethics in Smart Mobility
6.60. Case Studies: Successful Big Data Implementations in Mobility
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).