Data Analytics in Smart Cities Course
About our
The Data Analytics in Smart Cities course trains participants in the use of data analytics to optimize urban management. It includes big data, data visualization, and the development of predictive models for areas such as mobility, sustainability, and security. It focuses on the practical application of tools and techniques for data-driven decision-making in the context of Smart Cities, addressing key challenges in urban planning.
The course provides experience in analyzing data from IoT sensors, geolocation platforms, and municipal databases. Real-world case studies are explored, integrating knowledge of statistics, data science, and artificial intelligence (AI). Participants acquire skills to implement data-driven solutions, improving efficiency and quality of life in urban environments.
Data Analytics in Smart Cities Course
- Format: Online
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 2
399 $
Competencies and results
What you will learn
1. Data Analysis for the Planning and Optimization of Smart Cities
- Master the analysis of spatial and temporal data to identify urban patterns.
- Apply data visualization techniques to effectively communicate complex findings.
- Use predictive analytics tools to simulate and anticipate the impact of urban interventions.
- Understand and apply network analysis to optimize city infrastructure and services.
- Evaluate the performance of smart cities using key performance indicators (KPIs).
- Design and implement data management strategies to ensure the security, privacy, and quality of urban data.
- Analyze the impact of public policies on the development of smart cities, using data for evaluation and informed decision-making.
- Apply data analytics to optimize urban transportation, energy management, and resource planning.
- Evaluate the use of information and communication technologies (ICT) in the construction of smart cities, including the Internet of Things (IoT) and data platforms.
Develop skills in effectively communicating the results of data analysis to different audiences, including decision-makers and the general public.
2. Application of Data Analysis in the Development of Smart Cities
2. Application of Data Analysis in Smart City Development
- Identification and evaluation of relevant data sources for smart cities: IoT sensors, geospatial data, social networks, etc.
- Application of data cleaning and preprocessing techniques: handling missing values, outlier detection, data transformation.
- Implementation of descriptive analysis algorithms: statistics, data visualization to understand urban patterns and trends.
- Use of predictive models: regression, time series, machine learning to forecast events and optimize resources.
- Analysis of geospatial data: mapping, proximity analysis, and overlay for urban planning and land management.
- Application of data visualization techniques: creation of dashboards and interactive reports to communicate results effectively.
- Implementation of data-driven solutions for optimization of public transport, waste management, and energy efficiency.
Evaluation of the social and ethical impact of data use in the development of smart cities: privacy, security, and equity.
Design of strategies for integrating data into urban management and decision-making platforms.
Analysis of case studies of successful smart cities, highlighting the use of data in solving urban problems.
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. **Integration of Data Analysis into the Strategy and Operation of Smart Cities**
4. **Integration of Data Analysis into Smart City Strategy and Operations**
- Understand the fundamentals of data analysis and its application in the context of smart cities.
- Identify and evaluate relevant data sources for urban management: sensors, IoT devices, traffic data, etc.
- Learn data processing and cleaning techniques to ensure data quality and usability.
- Use data analysis tools (e.g., Python, R, data analysis platforms) to extract valuable information.
- Apply data visualization methods to communicate findings effectively.
- Analyze traffic and urban mobility to optimize vehicular flow and reduce congestion.
- Evaluate energy and resource use in the city to promote energy efficiency and sustainability.
- Monitor and manage air quality and the environment urban.
Use data to improve citizen security and emergency management.
Implement predictive models to anticipate problems and optimize decision-making.
Understand the legal and ethical framework related to data privacy and security in smart cities.
Design and implement data-driven strategies to improve the quality of life in cities.
5. **Implementation of Data Analysis in Innovation and Smart Urban Sustainability**
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You will master **data analysis** techniques applied to **smart urban planning**.
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You will learn to identify and evaluate **key performance indicators (KPIs)** in **urban sustainability**.
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You will become familiar with the use of **open data platforms** and **geospatial information sources**.
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You will develop skills in **data visualization** for strategic decision-making.
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You will explore the impact of **emerging technologies** (IoT, Big Data, AI) on urban management.
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You will understand the principles of the **smart city** and its relationship to **environmental sustainability**.
- You will analyze case studies of **urban innovation** and **best practices** at a global level.
- You will learn to apply predictive models for **resource management** and **service optimization**.
- You will develop strategies for **citizen participation** and **data governance** in urban environments.
- You will acquire knowledge about **environmental impact assessment** and **carbon footprint reduction** in cities.
6. **Discovering Data Analytics for the Evolution and Governance of Smart Cities**
6. **Discovering Data Analytics for the Evolution and Governance of Smart Cities**
Identifying key data sources for urban analysis: sensors, social networks, geospatial data, etc.
Understanding the concepts of “Smart City”: infrastructure, mobility, sustainability, security, and citizen participation.
Applying data analytics techniques to improve transportation: route optimization, traffic management, and demand forecasting.
Data analysis for the efficient management of resources: energy, water, waste, and their impact on sustainability.
Developing key performance indicators (KPIs) to evaluate the performance of a smart city.
Data visualization and dashboard creation to facilitate data-driven decision-making.
Study of successful smart city cases and analysis of their data-driven strategies.
Exploration of data analysis tools and platforms, such as Python, R, and software specialized in Smart Cities.
Consideration of ethical and privacy aspects in urban data analysis.
Implementation of solutions to improve citizen participation and transparency in government.
Who this program is for:
Data Analytics in Smart Cities Course
- Professionals and students in fields related to data management and analysis, with an interest in the development of smart cities.
- Data analysts, data scientists, engineers, and other professionals who wish to apply their skills to the urban context.
- Individuals seeking to understand and use data analysis technologies for decision-making in urban environments.
- Public officials and public sector professionals interested in improving the efficiency and sustainability of 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 Data Analysis: Introduction to key concepts.
1.2 Data Collection and Management: Sources and methods for smart cities.
1.3 Data Cleaning and Preprocessing: Essential techniques.
1.4 Data Visualization: Tools and strategies.
1.5 Introduction to Smart Cities: Concepts and examples.
1.6 The Role of Data Analysis: In smart urban planning.
1.7 Data Analysis Tools: Introduction to platforms and software.
1.8 Data Ethics and Privacy: Important considerations.
1.9 Case Studies: Initial applications in smart cities.
1.10 Future Trends: The future of data analysis in the urban context.
2.2 Collection and Cleaning of Urban Data: Sources and Methods
2.2 Data Exploration and Visualization for Smart Cities
2.3 Descriptive Analysis and Diagnosis of Urban Indicators
2.4 Predictive Modeling for Urban Behavior
2.5 Network and Flow Analysis in Urban Environments
2.6 Implementation of Dashboards and Urban Data Reports
2.7 Evaluating the Impact of Urban Policies Using Data
2.8 Tools and Technologies for Urban Data Analysis
2.9 Ethics and Privacy in Data Analysis for Cities
2.20 Case Studies: Applications of Data Analysis in Urban Development
3.3 Fundamentals of Data Analysis in Smart City Transformation
3.2 Data Collection and Management in Smart City Environments
3.3 Data Analysis Tools and Techniques for Urban Transformation
3.4 Data Visualization and Presentation for Decision-Making
3.5 Application of Predictive Analytics in Smart City Management
3.6 Data Analysis for Optimizing Urban Services
3.7 Modeling and Simulation of Smart City Scenarios
3.8 Ethics and Privacy in Smart City Data Analysis
3.9 Case Studies: Examples of Smart City Transformation
3.30 Implementation of Data Analysis-Based Projects
4.4 Data Analysis for Defining Smart City Strategy
4.2 Designing the Data Infrastructure for Smart City Operations
4.3 Selecting and Evaluating Data Sources for Urban Planning
4.4 Modeling and Simulating Smart City Scenarios
4.5 Implementing Data-Driven Monitoring and Control Systems
4.6 Data Analysis for Traffic Management and Urban Mobility
4.7 Using Data to Optimize Public Services and Citizen Experience
4.8 Integrating Data for Real-Time Decision Making
4.9 Key Performance Indicators (KPIs) and Metrics for Evaluating Smart City Performance
4.40 Designing and Implementing a Smart City Dashboard
5.5 Definition of Smart City Innovation: Key Concepts
5.5 Urban Data Sources: Types and Collection
5.3 Descriptive Data Analysis: Visualization and Summarization
5.4 Diagnostic Data Analysis: Problem Identification
5.5 Predictive Data Analysis: Modeling and Forecasting
5.6 Prescriptive Data Analysis: Recommendations and Optimization
5.7 Implementation of Data-Driven Solutions: Case Studies
5.8 Emerging Technologies: IoT, AI, Blockchain in Smart Cities
5.9 Urban Sustainability: Data-Driven Indicators and Metrics
5.50 Social and Economic Impact of Smart City Innovation
6.6 Fundamentals of Data Analysis for Urban Governance
6.2 Data Collection and Management in the Urban Environment
6.3 Descriptive and Exploratory Analysis of Urban Data
6.4 Predictive Modeling for Decision-Making in Governance
6.5 Data Visualization and Communication of Results
6.6 Applications of Data Analysis in Public Service Management
6.7 Data Analysis for Urban Planning and Sustainable Development
6.8 Ethics and Privacy in Data Analysis for Governance
6.9 Tools and Technologies for Data Analysis in Local Governments
6.60 Case Studies: Implementing Data Analysis in Smart 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”).