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Master’s in Data Products and Visualisation Engineering

Online master's programme of 1,900 hours in data product and visualisation engineering. 12 modules covering data as a product, data product design and engineering, visual perception, the grammar of graphics, interactive web visualisation, dashboards, data storytelling, engineering data visualisation, product management and 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

Conceive data products centred on users' needs.

02

Build the data engineering that underpins a product.

03

Apply principles of visual perception to chart design.

04

Develop declarative and interactive visualisations.

05

Design effective dashboards with BI tools.

06

Communicate results through data storytelling.

07

Manage the life cycle and governance of data products.

Profile

Who it's for

  • Data product engineers turning analytics into products people actually use.
  • Analytics engineers building semantic layers and metrics with dbt.
  • Visualisation developers working with D3.js, Vega-Lite and web apps.
  • BI analysts designing dashboards in Power BI or Tableau.
  • Data product managers responsible for roadmaps and adoption.
  • Information designers focused on perception, accessibility and storytelling.
  • Engineering or science graduates aiming to enter industry.
Standards & tools

What you'll work with

  • Regulation (EU) 2016/679 (GDPR)
  • UNE-EN 301 549 (accessibility requirements for ICT products and services)
  • ISO 8000-8 (data quality — information and data quality concepts and measuring)
  • W3C WCAG 2.2 (Web Content Accessibility Guidelines)
  • Regulation (EU) 2023/2854 (Data Act)
  • ISO 9241-210 (human-centred design for interactive systems)
  • ISO/IEC 25012 (data quality model)
  • Microsoft Power BI
  • Tableau
  • dbt
  • Streamlit
  • D3.js
Syllabus

12 modules · 1900 h

  1. 1.1From data project to product
  2. 1.2Types of data products
  3. 1.3Users, needs and value proposition
  4. 1.4Data mesh and domain-oriented products
  5. 1.5Life cycle of a data product
  6. 1.6Multidisciplinary product teams
  7. 1.7Adoption and value indicators
  8. 1.8Examples in engineering and industry
  9. 1.9Common mistakes in data products
  10. 1.10Case study: defining a maintenance data product
  1. 2.1User research
  2. 2.2Experience and task maps
  3. 2.3Rapid prototyping of visualisations
  4. 2.4Metric specification
  5. 2.5Data contracts
  6. 2.6Data API design
  7. 2.7Non-functional requirements
  8. 2.8Accessibility of data interfaces
  9. 2.9User validation
  10. 2.10Workshop: prototyping a data product with users
  1. 3.1Data architecture of the product
  2. 3.2Pipelines and transformations with dbt
  3. 3.3Semantic layer and metrics
  4. 3.4Data quality and testing
  5. 3.5Versioning and deployment
  6. 3.6Performance and caching
  7. 3.7Product observability
  8. 3.8Documentation and catalogues
  9. 3.9Security and permissions
  10. 3.10Lab: semantic metrics layer with dbt
  1. 4.1Pre-attentive perception of visual attributes
  2. 4.2Visual encodings and their accuracy
  3. 4.3Colour, contrast and colour blindness
  4. 4.4Gestalt principles
  5. 4.5Data-ink ratio
  6. 4.6Visual hierarchy of information
  7. 4.7Typography in visualisation
  8. 4.8Visual errors and deception
  9. 4.9Accessible design for everyone
  10. 4.10Practical exercise: redesigning poor charts
  1. 5.1Wilkinson's grammar of graphics
  2. 5.2ggplot2 and the layered grammar
  3. 5.3Vega-Lite and declarative specifications
  4. 5.4Scales, axes and legends
  5. 5.5Facets and small multiples
  6. 5.6Statistical transformations in charts
  7. 5.7Charts for distributions
  8. 5.8Charts for relationships and series
  9. 5.9Charts for parts of a whole
  10. 5.10Lab: declarative visualisations with Vega-Lite
  1. 6.1Interaction: filter, link and drill down
  2. 6.2Coordination of multiple views
  3. 6.3D3.js and the data-join model
  4. 6.4Observable and notebooks
  5. 6.5Plotly and Dash
  6. 6.6Streamlit for data apps
  7. 6.7Performance with large volumes
  8. 6.8Visualisation on mobile devices
  9. 6.9Usability testing
  10. 6.10Practical exercise: data app with Streamlit
  1. 7.1Types: strategic, tactical and operational
  2. 7.2Indicator selection
  3. 7.3Layout design
  4. 7.4Narrative and context
  5. 7.5Alerts and thresholds
  6. 7.6BI tools: Power BI, Tableau and Superset
  7. 7.7Modelling for BI
  8. 7.8Dashboard performance
  9. 7.9Maintenance and governance
  10. 7.10Workshop: operational dashboard in Power BI
  1. 8.1Narrative structure of a story
  2. 8.2From analysis to story
  3. 8.3Annotations and emphasis
  4. 8.4Explanatory versus exploratory visualisation
  5. 8.5Presentations to management
  6. 8.6Technical reports with visualisations
  7. 8.7Interactive storytelling with scrollytelling
  8. 8.8Communicating uncertainty
  9. 8.9Ethics in data communication
  10. 8.10Practical exercise: data story on an engineering problem
  1. 9.1Sensor time series
  2. 9.2Spatial data and maps
  3. 9.33D data and simulation fields
  4. 9.4Networks and graphs
  5. 9.5High-dimensional data
  6. 9.6Quality data and SPC
  7. 9.7Visualising AI models
  8. 9.8Digital twins and visualisation
  9. 9.9Real-time visualisation
  10. 9.10Lab: visualising simulation results with ParaView
  1. 10.1Product roadmap
  2. 10.2Prioritisation and backlog
  3. 10.3Product metrics
  4. 10.4Experimentation and improvement
  5. 10.5Stakeholder communication
  6. 10.6Pricing and monetisation
  7. 10.7Support and adoption
  8. 10.8Scaling the product
  9. 10.9Product retirement
  10. 10.10Case study: roadmap for an industrial data product
  1. 11.1Data governance and ownership
  2. 11.2Quality according to ISO 8000
  3. 11.3Privacy and the GDPR
  4. 11.4The Data Act and data sharing
  5. 11.5Digital accessibility and standards
  6. 11.6Bias in metrics and visualisations
  7. 11.7Algorithmic transparency
  8. 11.8Data licensing
  9. 11.9Auditing data products
  10. 11.10Practical exercise: compliance review of a dashboard
  1. 12.1Identification of the problem and users
  2. 12.2Product design
  3. 12.3Data architecture and engineering
  4. 12.4Visualisations and dashboard
  5. 12.5User testing
  6. 12.6Product deployment
  7. 12.7Adoption and value metrics
  8. 12.8Conclusions and product evolution
  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, dbt, Streamlit and D3.js, in free, academic or evaluation versions. Practical work is based on real procedures: design of operational dashboards, development of interactive data apps and data storytelling for management. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2016/679, UNE-EN 301 549, ISO 8000-8, W3C WCAG 2.2, Regulation (EU) 2023/2854, ISO 9241-210, ISO/IEC 25012.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the GDPR (Regulation (EU) 2016/679), UNE-EN 301 549 and ISO 8000-8 applied to real cases, not as a theoretical annex.
Industry software:Microsoft Power BI, Tableau, dbt, Streamlit and D3.js, in free, academic or evaluation versions.
Master's Thesis with defence:A complete data product, from user research and data engineering to dashboard, user testing and deployment, 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.

Define the product
Identify the problem, users and value proposition of a data product.
Prototype with users
Prototype visualisations and validate them with real users.
Engineer the data
Build pipelines and a semantic metrics layer with dbt.
Ship the dashboard
Deliver an operational dashboard and an interactive data app.
Master's Thesis
A deployed data product with adoption and value metrics, defended before a panel.
Career opportunities

Where this takes you

Data product engineerAnalytics engineerVisualisation developerBI analystData product managerInformation designer
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, dbt, Streamlit and D3.js, 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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