Smart Urbanism Course with Data Analytics
Online course of 300 hours in smart urbanism with data analytics: 6 modules covering the city as a data system, urban spatial analysis, programming for urban data science, predictive and mobility models and data-driven decision-making, with an assigned technical tutor.
Smart Urbanism Course with Data Analytics — studying from South Africa
Civil engineering is central to South Africa's infrastructure, from roads and water systems to rail and urban development. Transnet's freight rail network, the Gautrain system in Gauteng and metropolitan projects in Cape Town, Durban and Johannesburg create steady demand for engineers. Large contractors and consultancies, along with public agencies such as the South African National Roads Agency Limited, employ specialists in structures, transport and urban systems. Smart city initiatives increasingly combine civil works with digital monitoring and sustainable design.
Fees for South Africa: 12,700 R (South African rand), with instalments and employer sponsorship available.
Relevant bodies in South Africa
Typical roles
Questions from South Africa
Can I pay my fees in rand?
Yes. Fees for South African learners are shown in South African rand (ZAR), and instalment plans are available. Some learners also arrange employer sponsorship.
How is the final project chosen?
You complete a capstone project that can be linked to a real problem in your current or intended field. Your technical tutor guides you through scope, methodology and delivery.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify urban data sources and assess their quality and representativeness.
Apply spatial analysis techniques to urban planning problems.
Programme analysis workflows with Python, GeoPandas and PostGIS.
Build predictive models of mobility and land use.
Evaluate urban interventions with quasi-experimental methods.
Design dashboards that are useful for decision-making.
Ensure compliance with data protection regulations and algorithmic ethics.
Who it's for
- Urban data analysts
- Geospatial data scientists
- Smart city technicians
- Urban planning consultants
- Municipal open data managers
- Urban digital twin specialists
- Engineering or science graduates looking to move into local government
What you'll work with
- ISO 37120 (indicators for city services and quality of life)
- ISO 37122 (indicators for smart cities)
- Regulation (EU) 2016/679 (General Data Protection Regulation)
- Directive (EU) 2019/1024 (open data and re-use of public sector information)
- UNE 178301 (smart cities: open data)
- Directive 2007/2/EC INSPIRE (European spatial data infrastructure)
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- QGIS
- Python with GeoPandas and OSMnx
- PostgreSQL with PostGIS
- Kepler.gl
- FIWARE
6 modules
1.1Evidence-based urban planning1.2Urban data sources: sensors, censuses and registers1.3Municipal open data and DCAT-AP portals1.4Data governance and city platforms1.5Interoperability with NGSI-LD and FIWARE1.6Data quality, bias and representativeness1.7Privacy and anonymisation in urban data1.8City indicators under ISO 371201.9Data life cycle in municipal management1.10Workshop: inventory of a municipality's data sources
2.1Vector and raster data models2.2Reference systems and projections2.3Geoprocessing and map algebra2.4Spatial autocorrelation: Moran's index2.5Cluster detection with Getis-Ord2.6Network analysis and accessibility2.7Interpolation and continuous surfaces2.8H3 hexagonal grids for aggregation2.9Small-area statistics2.10Lab: accessibility analysis of facilities with QGIS
3.1Python and Jupyter environments for urban planning3.2Data manipulation with pandas3.3GeoPandas and spatial operations3.4Downloading OpenStreetMap with OSMnx3.5Spatial queries in PostGIS3.6Cleaning mobility data3.7IoT sensor time series3.8Visualisation with Kepler.gl and Folium3.9Automating data workflows3.10Practical exercise: road network analysis notebook with OSMnx
4.1Spatial regression and geographically weighted regression4.2Land-use classification with machine learning4.3Remote sensing: Sentinel-2 and spectral indices4.4Mobile phone data and origin-destination matrices4.5GTFS data and public transport analysis4.6Demand forecasting with time-series models4.7Urban simulation with agent-based models4.8Urban digital twins4.9Algorithmic ethics and model explainability4.10Case study: forecasting bike-sharing demand
5.1Urban dashboards with key indicators5.2Designing visualisations for citizen participation5.3Data storytelling for policy-makers5.4Policy evaluation with before-and-after data5.5Difference-in-differences applied to urban interventions5.6Data-supported cost-benefit analysis5.7Smart city frameworks: ISO 37122 and UNE 1782015.8Public procurement of data platforms5.9Surveillance risks and democratic legitimacy5.10Workshop: air quality and traffic dashboard
6.1Definition of the urban challenge and analytical questions6.2Dataset capture and cleaning6.3Exploratory spatial analysis6.4Analytical or predictive model6.5Validation and sensitivity analysis6.6Results dashboard6.7Recommendations for urban intervention6.8Data governance plan6.9Report and presentation6.10Final project: final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so the course is compatible with full-time work.
Each module combines theory, guided reading of the original standards and applied exercises. The working software is QGIS, Python with GeoPandas and OSMnx, PostgreSQL with PostGIS, Kepler.gl and FIWARE, in free, academic or evaluation versions. Real procedures are studied, including accessibility analysis of neighbourhood facilities, exploitation of mobility and public transport data and the construction of an urban dashboard. Assessment is continuous by module, plus the final integrating project.
Standards covered: ISO 37120; ISO 37122; Regulation (EU) 2016/679 (GDPR); Directive (EU) 2019/1024; UNE 178301; Directive 2007/2/EC INSPIRE; Regulation (EU) 2024/1689 (Artificial Intelligence Act).
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- No prior qualification is required.
- An internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with QGIS, Python with GeoPandas and OSMnx, PostgreSQL with PostGIS, Kepler.gl and FIWARE, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
No prior qualification is required: you need an internet connection, a computer able to run the programme software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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