Master’s in Data Analytics for Network Operation
Online master's programme of 1,900 hours in data analytics for electricity network operation, covering state estimation and loss and fraud detection. 12 modules covering SCADA, AMI and PMU data, CIM and IEC 61850 models, forecasting, quality of supply, anomaly detection, asset analytics and operational machine learning, with an assigned technical tutor.
Master’s in Data Analytics for Network Operation — studying from the United Kingdom
Energy engineering in the United Kingdom spans offshore wind, nuclear, gas, grid infrastructure and energy storage, with regional hubs in Aberdeen, Teesside, Humberside and the South West. The transition to net zero drives investment in transmission and distribution networks, renewable generation and digital grid management. Operators such as National Grid, SSE and Scottish Power employ engineers in planning, operations and asset management. The energy regulator Ofgem and the National Energy System Operator shape market rules and system planning.
Fees for the United Kingdom: £8,280 (Pound sterling), with instalments and employer sponsorship available.
Relevant bodies in the United Kingdom
Typical roles
Questions from the United Kingdom
Which UK bodies are relevant to energy and networks engineering qualifications?
In the UK, the Institution of Engineering and Technology (IET) and the Energy Institute are the main professional bodies for energy and networks, and the Engineering Council handles chartered registration. Regulation of the sector is by Ofgem and the National Energy System Operator. SEIUM's qualifications are private and not accredited by these bodies, so recognition is for the relevant body and employers to decide.
Do UK energy and network operators value private online qualifications?
UK energy utilities, network operators and grid contractors typically hire on the basis of competence, safety training, professional registration and project experience. A private qualification may support your profile, but its relevance is decided by each employer. Demonstrated practical work and progress towards professional registration usually matter most.
How does online study fit a UK working life, and what about funding?
Programmes are fully online, so you can study around your UK job and no student visa is needed to study from home. Teaching is in English, and an assigned technical tutor supports your progress. Fees are payable in instalments, and some employers sponsor job-related training, which varies by employer. Check the programme hours and funding options before enrolling.
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
Integrate SCADA, AMI and PMU data into analytical architectures.
Apply the CIM and IEC 61850 data models.
Develop demand and generation forecasting models.
Analyse quality of supply using regulatory indicators.
Detect anomalies, fraud and faults in network data.
Improve low-voltage visibility through analytics.
Deploy machine learning models in network operation.
Who it's for
- Electricity network data analysts looking to deepen their skills in operational analytics.
- Data scientists in utilities who want to apply their work to grid operation.
- Network operation engineers who need to make better use of SCADA, AMI and PMU data.
- Asset management engineers interested in condition-based and risk-based analytics.
- Energy data architects designing data platforms for utilities.
- Quality of supply engineers working with interruption and power-quality data.
- Engineering or science graduates seeking to enter electricity distribution.
What you'll work with
- IEC 61970-301 (CIM) (common information model for energy management systems)
- IEC 61968-11 (CIM extensions for distribution)
- IEC 61850-7-4 (logical nodes and data classes for substation automation)
- IEEE C37.118.1 (synchrophasor measurements for power systems)
- IEEE 1366 (distribution reliability indices)
- CNMC Circular on quality of supply (Spanish regulatory framework for continuity of supply)
- IEEE C37.111 (COMTRADE) (common format for transient data exchange)
- Regulation (EU) 2024/1689 (AI Act) (EU rules on artificial intelligence)
- Python with pandas and scikit-learn
- Apache Spark
- InfluxDB
- Power BI
- pandapower
12 modules
1.1SCADA and EMS systems1.2Smart meters and AMI1.3Phasor measurement units (PMU)1.4Protection records and oscillography1.5Weather and generation data1.6Network GIS systems1.7Data quality in operation1.8Integration of heterogeneous sources1.9Latency requirements by use case1.10Case study: data inventory of a distribution company
2.1CIM model IEC 61970 and 619682.2CGMES profiles for data exchange2.3IEC 61850 in substations2.4COMTRADE and PQDIF formats2.5Network topology models2.6Integration with ERP and asset management2.7Interoperability between systems2.8Semantics and ontologies2.9Network data governance2.10Lab: CIM model of a medium-voltage network
3.1Big data architectures3.2Time-series databases3.3Stream processing3.4Cloud and on-premise platforms3.5Data lakes for utilities3.6Infrastructure security3.7Scaling AMI data volumes3.8Catalogue and lineage3.9Costs and sizing3.10Practical exercise: PMU data pipeline
4.1Exploratory analysis of loads4.2Distributions and extreme values4.3Correlation of network variables4.4Time series and seasonality4.5ARIMA models and smoothing4.6Principal component analysis4.7Clustering of consumption profiles4.8Statistical hypothesis testing4.9Network data visualisation4.10Lab: clustering of load curves
5.1Short-term demand forecasting5.2Photovoltaic and wind forecasting5.3Gradient boosting models5.4LSTM networks and transformers5.5Probabilistic quantile forecasting5.6Use of weather variables5.7Evaluation of forecasting errors5.8Hierarchical forecasting by network level5.9Deployment into production5.10Practical exercise: 24-hour demand forecast
6.1TIEPI and NIEPI indices6.2SAIDI and SAIFI6.3Voltage dips and their analysis6.4Harmonic analysis6.5Unbalance and flicker6.6Event correlation6.7Data-driven fault location6.8Preparation of regulatory reports6.9Improvement plans6.10Case study: analysis of one year of interruptions
7.1Technical and non-technical losses7.2Fraud detection in AMI7.3Anomalies in time series7.4Isolation Forest and autoencoders7.5Meter fault detection7.6VEE reading validation7.7Cyberattacks detectable in data7.8Prioritisation of inspections7.9Measuring effectiveness7.10Lab: fraud detector with AMI data
8.1Maintenance and condition data8.2Transformer health indices8.3Dissolved gas analysis8.4Ageing models8.5Probability of failure8.6Risk-based maintenance8.7Investment planning8.8Analysis of underground cables8.9Data-driven vegetation management8.10Practical exercise: health index for a transformer fleet
9.1Low-voltage observability9.2Phase and topology identification9.3State estimation with AMI data9.4Pseudo-measurements and their generation9.5Hosting capacity9.6Congestion detection9.7Self-consumption monitoring9.8Data-driven network twins9.9Validation against measurements9.10Lab: phase identification using correlation
10.1Supervised models in operation10.2Explainability for operators10.3Reinforcement learning in networks10.4Graphs and graph neural networks10.5MLOps in utilities10.6Drift monitoring10.7Validation of critical models10.8AI Act and high-risk systems10.9Ethics and bias10.10Workshop: deployment of a forecasting model
11.1Dashboards for control centres11.2Geospatial network visualisation11.3Intelligent alarm management11.4Reports for the regulator11.5Real-time decision support11.6Operator experience11.7Integration with ADMS11.8Communicating uncertainty11.9Business indicators11.10Case study: operations dashboard
12.1Selection of the operational problem12.2Data collection and preparation12.3Exploratory data analysis12.4Model development12.5Validation and comparison12.6Integration into the operational workflow12.7Visualisation of results12.8Impact assessment12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
The programme is delivered 100% online 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 each intake, so it can be combined with full-time work.
Each module combines theory, guided reading of the original standards and applied exercises. The working software is Python with pandas and scikit-learn, Apache Spark, InfluxDB, Power BI and pandapower, in free, academic or evaluation versions. Practical work is based on real procedures for validating forecasting models, analysing smart meter data and detecting anomalies in historical series. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: IEC 61970-301 (CIM), IEC 61968-11, IEC 61850-7-4, IEEE C37.118.1, IEEE 1366, CNMC Circular on quality of supply, IEEE C37.111 (COMTRADE), Regulation (EU) 2024/1689 (AI 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
- A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- 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 1,900 hours over 12–14 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, Apache Spark, InfluxDB, Power BI and pandapower, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. 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 in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the 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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