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CourseCivil, Rail & Smart Cities Applications open

Data Analytics in Smart Cities Course

Online course of 300 hours in data analytics in smart cities: 6 modules covering ISO 37120, ISO 37122 and Regulation (EU) 2016/679 (GDPR) applied to real cases, with an assigned technical tutor.

Duration3 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Formulate analytical questions relevant to urban management.

02

Prepare and integrate heterogeneous urban data using quality criteria.

03

Apply statistical and spatial analysis techniques to urban phenomena.

04

Build and validate interpretable predictive models.

05

Communicate results through dashboards and data storytelling.

06

Take account of ethical requirements and the AI Act in public systems.

07

Develop a data analytics project applied to a city.

Profile

Who it's for

  • Urban data analysts.
  • Public-sector data scientists.
  • Urban observatory technicians.
  • Analytics consultants working with cities.
  • Public policy analysts.
  • Data visualisation specialists.
  • Engineering or science graduates looking to move into local government.
Standards & tools

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 (GDPR) (General Data Protection Regulation)
  • Regulation (EU) 2024/1689 (Artificial Intelligence Act) (harmonised rules on artificial intelligence)
  • UNE 178301 (open data for smart cities)
  • ISO/IEC 25012 (data quality model)
  • Directive (EU) 2019/1024 (open data and re-use of public sector information)
  • ISO/IEC 20546 (big data overview and vocabulary)
  • Python with pandas and scikit-learn
  • R
  • QGIS
  • Power BI
  • Apache Superset
Syllabus

6 modules

  1. 1.1The analytical cycle of city data
  2. 1.2Types of analysis: descriptive, predictive, prescriptive
  3. 1.3Municipal data sources
  4. 1.4Open, private and sensor data
  5. 1.5Analytical questions in public policy
  6. 1.6Municipal analytics teams and roles
  7. 1.7Ethics and bias in urban analysis
  8. 1.8ISO 37120 indicators as a baseline
  9. 1.9The urban analyst's toolkit
  10. 1.10Case study: formulating analytical questions for a local council
  1. 2.1Acquisition via APIs and ethical web scraping
  2. 2.2Cleaning and handling missing values
  3. 2.3Integration of heterogeneous sources
  4. 2.4Geocoding of addresses
  5. 2.5Statistical and visual exploration
  6. 2.6Outlier detection
  7. 2.7Temporal and spatial aggregation
  8. 2.8Data quality according to ISO/IEC 25012
  9. 2.9Reproducible documentation with notebooks
  10. 2.10Lab: cleaning citizen complaints data
  1. 3.1Spatial statistics and autocorrelation
  2. 3.2Moran's index and hot spots
  3. 3.3Interpolation and kriging
  4. 3.4Accessibility analysis to facilities
  5. 3.5Urban network analysis
  6. 3.6Segregation and territorial inequality
  7. 3.7Remote sensing for urban analysis
  8. 3.8Mobile phone data and mobility
  9. 3.9Effective cartographic visualisation
  10. 3.10Practical exercise: hot-spot analysis of incidents
  1. 4.1Regression and generalised linear models
  2. 4.2Decision trees, random forest and gradient boosting
  3. 4.3Time series with ARIMA and Prophet
  4. 4.4Incident classification
  5. 4.5Clustering of neighbourhoods
  6. 4.6Cross-validation and metrics
  7. 4.7Interpretability with SHAP
  8. 4.8The AI Act and public-sector systems
  9. 4.9Deploying models into production
  10. 4.10Workshop: demand forecasting for a municipal service
  1. 5.1Dashboard design
  2. 5.2Data storytelling
  3. 5.3Visualisation for citizens
  4. 5.4Public policy evaluation
  5. 5.5Causal inference and difference-in-differences
  6. 5.6Experiments and A/B testing in services
  7. 5.7Open data urban observatories
  8. 5.8Algorithmic transparency
  9. 5.9Managing uncertainty
  10. 5.10Case study: dashboard for a council member
  1. 6.1Definition of the business question
  2. 6.2Identification and acquisition of data
  3. 6.3Data preparation and quality
  4. 6.4Exploratory and spatial analysis
  5. 6.5Predictive or explanatory model
  6. 6.6Validation of results
  7. 6.7Final dashboard
  8. 6.8Public policy recommendations
  9. 6.9Report and presentation
  10. 6.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: you progress at your own pace within the intake, making it compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with pandas and scikit-learn, R, QGIS, Power BI and Apache Superset, in free, academic or evaluation versions. Real procedures are studied: hot-spot analysis of urban incidents, demand forecasting for municipal services and dashboard design for urban management. Assessment is continuous by module plus the final integrating project.

Standards covered: ISO 37120, ISO 37122, Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2024/1689 (Artificial Intelligence Act), UNE 178301, ISO/IEC 25012, Directive (EU) 2019/1024, ISO/IEC 20546.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 37120, ISO 37122 and the GDPR applied to real cases, not as a theoretical annex.
Industry software:Python with pandas and scikit-learn, R, QGIS, Power BI and Apache Superset, in free, academic or evaluation versions.
Final project:a data analytics project applied to a city, from business question to dashboard and policy recommendations, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Define the question
Define the business question and identify the data needed.
Prepare the data
Acquire, clean and assess the quality of the data.
Analyse and model
Carry out exploratory and spatial analysis and build a predictive or explanatory model.
Validate and visualise
Validate the results and build the final dashboard.
Final project
Present the analytics project with public policy recommendations.
Career opportunities

Where this takes you

Urban Data AnalystPublic-sector Data ScientistUrban Observatory TechnicianAnalytics Consultant for CitiesPublic Policy AnalystData Visualisation Specialist
Admissions & scholarships

Requirements

  • No prior qualification is required.
  • 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.

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FAQ

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 Python with pandas and scikit-learn, R, QGIS, Power BI and Apache Superset, 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 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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