The course introduces students to the main emerging technologies applied to the field of Smart Cities and the intelligent management of data in urban and business environments. Throughout the course, the processes of data collection, processing, and visualization are analyzed, along with the technologies and tools used to develop intelligent systems capable of generating predictive models and supporting decision-making.
Through a practical, project-based methodology, students will work with data capture technologies, algorithms, visualization tools, and predictive models applied to real cases related to smart cities. The course combines theoretical sessions with practical exercises and the development of a group project aimed at simulating the launch of an innovative product or service linked to the Smart Cities ecosystem.
Titular Professors
Professors
Introduction: This session will provide an overview of the course structure, objectives, and key topics that will be covered throughout the semester.
Topic 1: Smart Cities. What are Smart Cities? Project setup (Group configuration).
Topic 2: Data Types. Understanding different types of data and their potential uses.
Topic 3: Data Acquisition Technologies. Understanding the different types of technologies for data acquisition.
Topic 4: Hardware + Software. Implementation of data collection systems.
Topic 5: Data Visualization. Key frameworks for data visualization available on the market.
Topic 6: Algorithms. Hands-on work in algorithm configuration to understand how they function.
Topic 7: Predictive Models. Training a model to make predictions and reduce risks.
Topic 8: Smart Cities Product Launch. Simulating a product launch to the market. Key variables.
The classes will follow a practical, project-oriented methodology based on the development of cases and products as close as possible to real-world environments. The course will combine theoretical explanations with practical exercises on data acquisition and processing, algorithms, and predictive models applied to emerging technologies and Smart Cities. Students will work in groups throughout the semester on the progressive development of a project linked to an innovative product or service, integrating the knowledge acquired in each topic. The use of a laptop in class will be necessary to carry out the practical activities. The main methodologies used in this course are:
- MD.0 Lecture-based teaching
- MD.6 Project-based learning
This course includes the following assessment activities:
Class participation and practical exercises (significant assessment activity) / 50% of the final grade. The maximum percentage of the grade is achieved when the student attends all or almost all scheduled sessions. In addition to attendance, punctuality and participation in class will be considered, whether by answering questions posed by the instructor, asking relevant questions, or engaging in discussions with classmates when the session allows it. Finally, the grades obtained in the different practical exercises completed during class will also be taken into account.
Main Project (significant assessment activity) / 50% of the final grade. Students will work on a semester-long project to be completed in groups and submitted in two deliverables: a first submission at the midterm evaluation and a second submission at the final evaluation.
- Understanding and correct identification of different data collection methods, as well as their application in various contexts, especially in environments related to Smart Cities and data-driven business management.
- Knowledge of the basic concepts of technologies used for data acquisition and their integration into real systems, including the ability to select the most appropriate tools depending on the problem addressed.
- The ability to use basic data processing tools with rigor and consistency, as well as to correctly interpret the results obtained from different analytical techniques.
- Understanding how predictive models work and their application using real data, including the ability to analyze their results and their impact on decision-making in uncertain environments.
- The ability to represent and communicate data and results using visualization tools, ensuring clarity, consistency, and suitability to the application context.
- The ability to integrate the different concepts covered throughout the course in the development of an applied project, including data collection, processing, model building, and interpretation of results in a real case.
- The quality of work developed in practical activities and projects, as well as the level of active participation, rigor, and contribution in class dynamics.
- Clarity, structure, and precision in both written and oral presentation of the results obtained and the decisions made throughout the analysis and project development process.
Agrawal, A., Gans, J., & Goldfarb, A. Prediction Machines: The Simple Economics of Artificial Intelligence.
Allam, Z. Rethinking Smart Cities: ICT for New-Type Urbanization and Urban Prosperity.
Gassmann, O., Böhm, J., & Palmié, M. Smart Cities: Introducing Digital Innovation to Cities.
Gilthorpe, O. Smart Cities: A Spatial Perspective.
Green, B. The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future.
Isson, J. P. (2018). Unstructured Data Analytics: How to Improve Customer Acquisition, Customer Retention, and Fraud Detection and Prevention.
Kirwan, C. G., & Oliveira, R. G. Smart Cities and Artificial Intelligence: Convergent Systems for Planning, Design, and Operations.
Kitchin, R., Lauriault, T. P., & McArdle, G. Data and the City.
Landeta Echeberria, A. Artificial Intelligence for Business: Innovation, Tools and Practices.
Panda, S. K., Mishra, V., Balamurali, R., & Elngar, A. A. Artificial Intelligence and Machine Learning in Business Management.
Ratti, C., & Claudel, M. The City of Tomorrow: Sensors, Networks, Hackers, and the Future of Urban Life.
Reichental, J. Making Sense of Smart Cities: A Human-Driven Approach.
Song, H., Srinivasan, R., Sookoor, T., & Jeschke, S. Smart Cities: Foundations, Principles, and Applications.
Teoh, T. T., & Goh, Y. J. Artificial Intelligence in Business Management.
Townsend, A. M. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia.
Agrawal, A., Gans, J., & Goldfarb, A. Prediction Machines: The Simple Economics of Artificial Intelligence.
Allam, Z. Rethinking Smart Cities: ICT for New-Type Urbanization and Urban Prosperity.
Gassmann, O., Böhm, J., & Palmié, M. Smart Cities: Introducing Digital Innovation to Cities.
Gilthorpe, O. Smart Cities: A Spatial Perspective.
Green, B. The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future.
Isson, J. P. (2018). Unstructured Data Analytics: How to Improve Customer Acquisition, Customer Retention, and Fraud Detection and Prevention.
Kirwan, C. G., & Oliveira, R. G. Smart Cities and Artificial Intelligence: Convergent Systems for Planning, Design, and Operations.
Kitchin, R., Lauriault, T. P., & McArdle, G. Data and the City.
Landeta Echeberria, A. Artificial Intelligence for Business: Innovation, Tools and Practices.
Panda, S. K., Mishra, V., Balamurali, R., & Elngar, A. A. Artificial Intelligence and Machine Learning in Business Management.
Ratti, C., & Claudel, M. The City of Tomorrow: Sensors, Networks, Hackers, and the Future of Urban Life.
Reichental, J. Making Sense of Smart Cities: A Human-Driven Approach.
Song, H., Srinivasan, R., Sookoor, T., & Jeschke, S. Smart Cities: Foundations, Principles, and Applications.
Teoh, T. T., & Goh, Y. J. Artificial Intelligence in Business Management.
Townsend, A. M. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia.