Data Analytics Course for Network Operations
Online 300-hour course in Data Analytics for network operations. 6 modules covering IEC 61968, IEC 61970 and Python (pandas, scikit-learn). Assigned tutor and open enrolment.
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Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify the data sources and models of electricity network operation.
Apply analysis and visualisation tools to network time series.
Carry out descriptive analyses of load, voltage and interruptions.
Develop predictive models for demand and congestion.
Assess supply quality using regulatory indicators.
Plan the implementation of analytical capabilities in a distribution company.
Execute an analytics project applied to network operation.
Who it's for
- Electricity network data analyst looking to specialise in Data Analytics for network operations.
- Distribution operations engineer looking to specialise in Data Analytics for network operations.
- Data scientist in utilities looking to specialise in Data Analytics for network operations.
- Network planning engineer looking to specialise in Data Analytics for network operations.
- Supply quality specialist looking to specialise in Data Analytics for network operations.
- Energy digital transformation consultant looking to specialise in Data Analytics for network operations.
- Graduates in engineering or sciences seeking to enter electricity distribution.
What you'll work with
- IEC 61968
- IEC 61970
- IEC 62325
- Regulation (EU) 2016/679 (GDPR)
- IEC 62056 (DLMS/COSEM)
- Directive (EU) 2019/944 on the internal market for electricity
- Royal Decree 1955/2000
- ISO/IEC 27001
6 modules · 300 h
1.1Data sources: SCADA, AMI and PMU1.2DMS, OMS and GIS systems of the distribution company1.3Time series and sampling frequency1.4Data quality and outliers1.5CIM data models under IEC 61968 and 619701.6Data lake architectures in utilities1.7Data governance and ownership1.8Consumer data privacy1.9Network operation indicators1.10Workshop: cleaning smart meter data
2.1Python with pandas and NumPy2.2Time-series databases2.3SQL for operational queries2.4Distributed processing with Spark2.5Visualisation with Power BI and Grafana2.6Reproducible notebooks and version control2.7Geospatial analysis of network assets2.8Report automation2.9Dashboards for control centres2.10Laboratory: querying and visualising SCADA data
3.1Transformer load curves3.2Clustering of consumption profiles3.3Detection of overloads and imbalances3.4Supply quality analysis: TIEPI and NIEPI3.5Study of interruptions and causes3.6Low-voltage voltage analysis3.7Topology and phase identification3.8Correlation with meteorological variables3.9Analysis of distributed generation3.10Practice: clustering of load profiles
4.1Short-term demand forecasting4.2Forecasting of distributed renewable generation4.3State estimation in distribution networks4.4Prediction of local congestion4.5Optimisation of network reconfiguration4.6Flexibility management in distribution4.7Data-driven investment prioritisation4.8Electrification and electric vehicle scenarios4.9Model validation and deployment4.10Case study: load forecasting for a transformer substation
5.1Prioritised use cases in distribution companies5.2Data teams and required profiles5.3Integration with operational systems5.4Cybersecurity in data platforms5.5Regulation and reporting obligations5.6Value metrics for data projects5.7Change management in operations centres5.8Cloud and on-premise platforms5.9Experiences of European operators5.10Case study: analytics roadmap for a distribution company
6.1Selection of the use case6.2Data inventory and acquisition6.3Data preparation and cleaning6.4Exploratory analysis6.5Development of the analytical model6.6Operational dashboard6.7Assessment of operational value6.8Deployment plan6.9Report and presentation6.10Final review with the tutor
How you'll learn
Training 100 % online on the SEIUM campus, with downloadable material, recorded classes and assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, which makes it possible to combine with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas, scikit-learn), Apache Spark, Grafana, Power BI, InfluxDB, in free, academic or evaluation versions. Tests are studied using real procedures for clustering smart meter load profiles, transformer substation load forecasting and a supply quality dashboard. Assessment is continuous by module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify the data sources and models of electricity network operation.
Apply analysis and visualisation tools to network time series.
Carry out descriptive analyses of load, voltage and interruptions.
Develop predictive models for demand and congestion.
Assess supply quality using regulatory indicators.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow technical regulatory texts.
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, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with full-time work.
The programme uses Python (pandas, scikit-learn), Apache Spark, Grafana, Power BI, InfluxDB. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Check the credit transfer policy when you enrol.
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