Smart Urbanism Course with Data Analytics
About our
The Smart Urbanism with Data Analytics course explores the application of data analytics and smart technologies to transform the design, management, and sustainability of cities. It focuses on the use of Big Data, IoT (Internet of Things), and predictive modeling to optimize key areas such as urban mobility, energy efficiency, urban planning, and citizen security, integrating concepts of sustainable development. The course prepares participants to analyze data, identify patterns, and develop innovative solutions that improve the quality of life in urban environments. Participants will learn to use data visualization, spatial analysis, and urban simulation tools to better understand the challenges of cities and design data-driven strategies. It focuses on practical case studies and applied projects, fostering skills in data-driven decision-making, artificial intelligence, and smart urbanism, with an emphasis on social and environmental impact.
Smart Urbanism Course with Data Analytics
- Format: Online
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
499 $
Competencies and results
What you will learn
1. Data Analysis and Smart Urban Design: A Practical Course
- Understand the fundamentals of geospatial data collection, processing, and analysis.
- Use specialized software for urban data analysis, including modeling and simulation tools.
- Evaluate the impact of design decisions on energy efficiency, sustainability, and urban quality of life.
- Apply data visualization techniques to effectively communicate findings and conclusions.
- Design innovative, data-driven urban solutions, addressing challenges such as mobility, land-use planning, and urban resilience.
- Explore the concept of smart cities and the role of technology in urban transformation.
- Develop critical thinking and problem-solving skills to address the challenges of contemporary urban design.
- Become familiar with the regulations and standards relevant to sustainable urban design and development planning.
- Work on practical projects and studies of cases to apply the knowledge acquired in real-world scenarios.
- Collaborate with other students and professionals in the sector to foster the exchange of ideas and the creation of professional networks.
2. **Master Smart Urban Planning: Data Analytics for Cities of the Future**
- Understand the fundamentals of smart urbanism and its impact on cities.
- Use data analytics tools for urban management.
- Acquire skills in the collection, processing, and analysis of urban data.
- Apply data visualization techniques for the interpretation of complex urban information.
- Explore the use of data for urban planning and design.
- Analyze the impact of smart technologies on urban infrastructure (transport, energy, etc.).
- Evaluate the use of data to improve the efficiency and sustainability of cities.
- Study case studies of smart cities and their data-driven strategies.
- Learn about ethics and privacy in urban data analysis.
- Develop skills for data-driven decision-making in the urban context.
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. **Smart Urbanism with Data: Learn to Transform Cities with Analytics**
- Fundamentals of Smart Urbanism: Understand the key principles of smart urban planning and its impact on the transformation of cities.
- Urban Data Collection and Analysis: Master techniques for collecting relevant urban data, including sources, data types, and tools.
- Data Visualization and Communication: Learn to present urban data effectively using visualization tools to facilitate decision-making.
- Urban Modeling and Simulation: Use data to build models and simulations that allow you to predict the impact of urban interventions.
- Applications of Analytics in Specific Areas: Explore the use of analytics to optimize areas such as transportation, energy, security, and sustainability.
- Tools and Technologies for Smart Urbanism: Become familiar with key platforms and technologies such as GIS, IoT, and Big Data to The development of smart cities.
Case Studies and Best Practices: Analyze real-world examples of successful Smart Urbanism projects and extract key lessons.
Ethical and Privacy Considerations: Understand the ethical and privacy implications related to the collection and use of urban data.
5. **Data Analytics for Smart Urban Planning: Analysis and Optimization of Cities**
- Understand the fundamentals of urban data collection, cleaning, and transformation.
- Explore data analysis tools and techniques for urban planning, including:
- Descriptive analysis of urban data (statistics, visualizations).
- Spatial data analysis (GIS, heat maps).
- Predictive modeling (regression, classification).
- Data mining and pattern detection.
- Apply data analysis to specific urban problems, such as:
- Transportation planning and traffic management (route optimization, congestion analysis).
- Urban design and sustainability (energy efficiency analysis, environmental impact modeling).
- Public safety and emergency response (crime prediction, optimization of response).
Economic and social development (identifying areas of opportunity, analyzing the impact of policies).
Use relevant software and platforms for urban data analysis (e.g., Python with libraries such as Pandas, GeoPandas, Scikit-learn, etc.; GIS software; data visualization tools).
Interpret the results of data analysis and communicate findings effectively to technical and non-technical audiences.
Design and implement strategies for smart city optimization based on data analysis.
Evaluate the impact of data-driven solutions in the urban context (e.g., key performance indicators).
Consider ethical and privacy aspects in urban data analysis.
6. **Smart Urbanism: Data Analytics Course for Sustainable and Efficient Cities**
6. **Smart Urbanism: Data Analytics Course for Sustainable and Efficient Cities**
- Understand the fundamentals of Smart Urbanism and its relevance to urban transformation.
- Acquire knowledge about the collection and management of urban data from diverse sources.
- Learn to use Data Analytics tools for urban data analysis.
- Apply data visualization techniques to communicate key findings.
- Analyze key performance indicators (KPIs) to evaluate urban performance.
- Identify opportunities to improve efficiency and sustainability in cities.
- Model and simulate urban scenarios using data and predictive analytics.
- Develop data-driven solutions to address specific urban challenges (e.g., mobility, energy, waste).
- Explore the use of smart technologies (IoT, sensors, etc.) in the urban context.
Evaluate the social and economic impact of Smart Urbanism initiatives.
Become familiar with the policies and regulations related to smart cities.
Design and present Smart Urbanism projects based on data and analysis.
Who this program is for:
Smart Urbanism Course with Data Analytics
- Professionals and students in fields related to smart urban planning and data analytics, such as Civil Engineering, Architecture, Urban Planning, Systems Engineering, Computer Science, or related areas.
- Data analysts, data scientists, and professionals who wish to apply their knowledge in the context of smart urban planning.
- Public officials and local government staff involved in the planning and management of smart cities.
- Entrepreneurs and professionals interested in developing technological solutions to improve the quality of life in cities.
- 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.
1.1 Fundamentals of Smart Urbanism and Data Analytics
1.2 Importance of Data Analysis in Urban Design
1.3 Introduction to Smart Cities: Concepts and Definitions
1.4 Data Sources in the Urban Context: Types and Origins
1.5 Key Tools and Technologies for Data Analysis in Cities
1.6 Introduction to Data Visualization and Dashboards
1.7 Ethics and Privacy in the Use of Urban Data
1.8 Examples of Data Analytics Applications in Urban Planning
1.9 The Role of the Data Scientist in the Development of Smart Cities
1.10 Trends and Future of Smart Urbanism
2.2 Urban Data Sources: Identification and Access
3.2 Data Cleaning and Preprocessing: Essential Techniques
4.3 Data Quality: Evaluation and Control
5.4 Data Transformation: Formats and Structures
6.5 Tools and Technologies for Data Collection
7.6 Urban Data Storage: Databases and Data Warehouses
8.7 Data Visualization: Getting Started
9.8 Ethics and Privacy in Urban Data Collection
20.9 Case Studies: Data Collection and Cleaning in Smart Cities
22.20 Challenges and Solutions in Urban Data Collection
3.3 What is Smart Urbanism? Definition and key concepts.
3.2 Importance of data analysis in modern urban planning.
3.3 The role of smart cities: an overview.
3.4 Sources of urban data: types and examples.
3.5 Tools and technologies for collecting urban data.
3.6 The data lifecycle in Smart Urbanism.
3.7 Ethics and privacy in urban data management.
3.8 Examples of Smart Urbanism applications worldwide.
3.9 Challenges and opportunities in smart city development.
3.30 Introduction to urban key performance indicators (KPIs).
4.4 What is Smart Urbanism and its Impact?
4.2 Smart Cities: Definition and Key Characteristics
4.3 Evolution of Urban Planning and the Need for Intelligence
4.4 The Role of Data in Smart Urbanism
4.5 Essential Components of a Smart City
4.6 Enabling Technologies: Sensors, IoT, Big Data
4.7 Case Studies: Examples of Successful Smart Cities
4.8 Challenges and Opportunities of Smart Urbanism
4.9 Ethics and Privacy in Urban Data Management
4.40 Future Trends and the Vision of Smart Urbanism
5.5 Introduction to Urban Data Analysis
5.5 Collection and Preparation of Urban Data
5.3 Data Visualization Tools in Urban Planning
5.4 Descriptive Analysis of Urban Data
5.5 Exploratory Analysis of Urban Data
5.6 Predictive Modeling in Urban Planning
5.7 Machine Learning Applications in Smart Cities
5.8 Urban Network Analysis
5.9 Urban Impact Assessment Using Data
5.50 Case Studies of Data Analysis in Urban Planning
6.6 Introduction to Advanced Analytics in Urban Planning
6.2 Collection and Management of Urban Data
6.3 Spatial Data Analysis and GIS
6.4 Predictive Modeling for Urban Development
6.5 Data Visualization and Communication of Results
6.6 Environmental Impact Analysis and Urban Sustainability
6.7 Optimization of Urban Resources and Services
6.8 Evaluation of Urban Policies with Data
6.9 Tools and Technologies for Urban Analytics
6.60 Case Studies: Real-World Applications of Analytics in Cities
- 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”).