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Master’s in Visual Analytics and Technical BI Engineering

Online master's programme of 1,900 hours in visual analytics and technical BI engineering. 12 modules covering visual analytics, perception and visual design, complex data visualisation, data modelling for BI, BI tools, industrial BI, technical time-series visualisation, visual analytics for machine learning, visualisation programming, performance and BI governance, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Apply visual analytics principles to the analysis of technical data.

02

Design effective and accessible visualisations.

03

Visualise multivariate, temporal and network data.

04

Model data for BI solutions with semantic layers.

05

Develop technical dashboards with BI tools.

06

Integrate visual analytics with machine learning models.

07

Govern self-service BI platforms.

Profile

Who it's for

  • Technical BI analysts working with production, maintenance and energy data.
  • Dashboard developers building in Power BI, Tableau or Grafana.
  • Visual analytics engineers exploring complex multivariate and time-series data.
  • BI data engineers designing semantic models and fast platforms.
  • Operations analysts who rely on KPIs, OEE and SPC.
  • Business intelligence consultants delivering technical BI projects.
  • Engineering or science graduates aiming to enter industry.
Standards & tools

What you'll work with

  • ISO 22400-2 (KPIs for manufacturing operations management)
  • Regulation (EU) 2016/679 (GDPR)
  • W3C WCAG 2.2 (Web Content Accessibility Guidelines)
  • UNE-EN 301 549 (accessibility requirements for ICT products and services)
  • ISO 9241-11 (usability definitions and concepts)
  • ISO/IEC 25012 (data quality model)
  • ISO 7870-2 (Shewhart control charts)
  • Microsoft Power BI
  • Tableau
  • Grafana
  • Plotly Dash
  • Apache Superset
Syllabus

12 modules · 1900 h

  1. 1.1Definition and process of visual analytics
  2. 1.2Keim's model and the sensemaking loop
  3. 1.3Exploration versus confirmation
  4. 1.4Low-level and high-level analytical tasks
  5. 1.5Data typologies
  6. 1.6Visual scalability for large volumes
  7. 1.7Interaction and reasoning
  8. 1.8Visual analytics in engineering
  9. 1.9Current research in visual analytics
  10. 1.10Case study: analysing a technical investigation with visual analytics
  1. 2.1Visual channels and effectiveness
  2. 2.2Colour for sequential and diverging data
  3. 2.3Accessible colour palettes
  4. 2.4Information density
  5. 2.5Reference charts by Tufte and Few
  6. 2.6Logarithmic scales and axes
  7. 2.7Visualising uncertainty
  8. 2.8Common mistakes in technical BI
  9. 2.9Corporate style guides
  10. 2.10Practical exercise: style guide for technical dashboards
  1. 3.1Parallel coordinate plots
  2. 3.2Scatterplot matrices
  3. 3.3Dimensionality reduction with t-SNE and UMAP
  4. 3.4Heat maps and clustering
  5. 3.5Visualising hierarchies
  6. 3.6Visualising networks
  7. 3.7Temporal data and calendars
  8. 3.8Visualising geospatial data
  9. 3.9Visualising textual data
  10. 3.10Lab: multivariate exploration of process data
  1. 4.1Star and snowflake schemas
  2. 4.2Time and asset dimensions
  3. 4.3Fact tables for sensors and events
  4. 4.4Granularity and aggregations
  5. 4.5Semantic metrics layer
  6. 4.6Measures with the DAX language
  7. 4.7Composite models and DirectQuery
  8. 4.8Model performance
  9. 4.9Row-level security
  10. 4.10Workshop: semantic model of maintenance data
  1. 5.1Power BI in depth
  2. 5.2Tableau and its query engine
  3. 5.3Open-source Apache Superset
  4. 5.4Grafana for technical data
  5. 5.5Qlik and the associative model
  6. 5.6Tool comparison
  7. 5.7Licensing and costs
  8. 5.8Embedded BI in applications
  9. 5.9Integration with industrial sources
  10. 5.10Practical exercise: Power BI versus Grafana comparison
  1. 6.1Production KPIs according to ISO 22400
  2. 6.2OEE dashboards
  3. 6.3SPC visualisation
  4. 6.4Maintenance indicators
  5. 6.5Plant energy indicators
  6. 6.6Quality and traceability
  7. 6.7Projects and engineering
  8. 6.8Occupational safety indicators
  9. 6.9Integration with MES and SCADA
  10. 6.10Lab: production dashboard with MES data
  1. 7.1Large volumes of time series
  2. 7.2Shape-preserving downsampling
  3. 7.3Visualising alarms and events
  4. 7.4Horizon charts and small multiples
  5. 7.5Batch comparison
  6. 7.6Visualising spectra
  7. 7.7Correlation between signals
  8. 7.8Visual anomaly detection
  9. 7.9Specialised time-series tools
  10. 7.10Workshop: visual analysis of sensor series
  1. 8.1Visualisation for model exploration
  2. 8.2Interactive confusion matrices
  3. 8.3Visualising feature importance
  4. 8.4Visual local explanations
  5. 8.5Interactive data labelling
  6. 8.6Visual analytics of embeddings
  7. 8.7Experiment tracking
  8. 8.8Visualising drift
  9. 8.9Human in the loop
  10. 8.10Practical exercise: visual tool for model error analysis
  1. 9.1Python with Matplotlib and Seaborn
  2. 9.2Plotly and Dash
  3. 9.3Bokeh and HoloViews
  4. 9.4Datashader for large volumes
  5. 9.5Custom visualisations with D3.js
  6. 9.6Vega-Lite and Altair
  7. 9.7R and Shiny
  8. 9.8Visualisation in notebooks
  9. 9.9Application deployment
  10. 9.10Lab: visual analytics app with Dash
  1. 10.1OLAP engines and cubes
  2. 10.2Columnar databases for BI
  3. 10.3DuckDB and local analytics
  4. 10.4Caching and pre-computed aggregations
  5. 10.5Incremental data refreshes
  6. 10.6Real-time BI
  7. 10.7User scalability
  8. 10.8Performance monitoring
  9. 10.9Platform cost
  10. 10.10Practical exercise: optimising a slow model
  1. 11.1BI strategy and self-service
  2. 11.2Centre of excellence
  3. 11.3Dataset certification
  4. 11.4Metrics catalogue
  5. 11.5Report version control
  6. 11.6Security and GDPR compliance
  7. 11.7Report accessibility
  8. 11.8User training
  9. 11.9Usage measurement
  10. 11.10Case study: BI governance model for an engineering firm
  1. 12.1Selection of the technical domain
  2. 12.2User and task analysis
  3. 12.3Data modelling
  4. 12.4Visualisation design
  5. 12.5Dashboard implementation
  6. 12.6User evaluation
  7. 12.7Optimisation and governance
  8. 12.8Conclusions and usage recommendations
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

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 you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Microsoft Power BI, Tableau, Grafana, Plotly Dash and Apache Superset, in free, academic or evaluation versions. Practical work is based on real procedures: production and OEE dashboards, visual exploration of multivariate process data and visual error analysis of models. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 22400-2, Regulation (EU) 2016/679, W3C WCAG 2.2, UNE-EN 301 549, ISO 9241-11, ISO/IEC 25012, ISO 7870-2.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 22400-2, the GDPR (Regulation (EU) 2016/679) and W3C WCAG 2.2 applied to real cases, not as a theoretical annex.
Industry software:Microsoft Power BI, Tableau, Grafana, Plotly Dash and Apache Superset, in free, academic or evaluation versions.
Master's Thesis with defence:A technical dashboard for a real engineering domain, from user and task analysis and data modelling to user evaluation and governance, defended before a panel.
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.

Analyse users and tasks
Select the technical domain and analyse its users and analytical tasks.
Model the data
Build a semantic model with fact tables, dimensions and DAX measures.
Design the visuals
Design accessible visualisations for multivariate and time-series data.
Implement and optimise
Implement the dashboard, evaluate it with users and optimise performance.
Master's Thesis
A governed technical dashboard evaluated with users, defended before a panel.
Career opportunities

Where this takes you

Technical BI analystDashboard developerVisual analytics engineerBI data engineerOperations analystBusiness intelligence consultant
Admissions & scholarships

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's 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.

Apply now Scholarships
FAQ

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 Microsoft Power BI, Tableau, Grafana, Plotly Dash and Apache Superset, 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 university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme's 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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