Master’s in Visual Analytics and Technical BI
Online master's programme of 1,900 hours in visual analytics and technical business intelligence: dashboard design, data storytelling and operational decision-making. 12 modules covering BI fundamentals, visual perception, data modelling, data integration, advanced Power BI, other platforms, exploratory and advanced analytics, technical BI in engineering sectors, data governance and implementation, with an assigned technical tutor.
Master’s in Visual Analytics and Technical BI — studying from the United States
Data science, artificial intelligence and simulation are central to American industry, with major concentrations in the San Francisco Bay Area, Seattle, New York and Boston. Engineering organizations increasingly rely on machine learning, digital twins and high-performance computing to accelerate design, testing and operations. Employers range from large technology companies to aerospace, automotive and energy firms that apply analytics to complex systems. Demand is strong for engineers who combine domain knowledge with modeling, statistics and software skills.
Fees for the United States: 6,610 US$ (US Dollars), with instalments and employer sponsorship available.
Relevant bodies in the United States
Typical roles
Questions from the United States
Which US bodies relate to data science, AI and simulation engineering?
There is no single licensing body for data science or AI in the US. Relevant professional organizations include the IEEE Computer Society, the Association for Computing Machinery (ACM), and the Society for Industrial and Applied Mathematics (SIAM). Federal guidance such as the NIST AI Risk Management Framework shapes practice. SEIUM's qualifications are private and not accredited by a national body, so recognition is decided by employers.
How do US employers in data and AI regard private online postgraduate qualifications?
US tech firms, financial institutions and research labs generally hire on the basis of portfolios, technical interviews, relevant experience and sometimes advanced degrees. A private online qualification can show structured study, but it is not a standard hiring requirement and each employer weighs it differently. Public projects, code repositories and measurable results tend to matter most.
Is it realistic to study data and AI from the US around a full-time job?
Yes, the programmes are fully online and taught in English, so you can study from home without a student visa. The workload is large: about 1,900 hours for a master's, 800 hours for a postgraduate diploma, and 300 hours for a specialist course. An assigned technical tutor supports you. Fees may be paid in instalments, and employer sponsorship is sometimes available, which you should confirm with your employer.
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
Design business intelligence architectures.
Apply perception principles to visual design.
Build dimensional models of technical data.
Develop advanced solutions in Power BI and other platforms.
Explore complex data through visual analytics.
Integrate predictive models into dashboards.
Govern and implement BI solutions in organisations.
Who it's for
- Business intelligence analysts working with technical and operational data.
- Data engineers who want to master the visualisation layer.
- Dashboard designers looking to strengthen their modelling and perception skills.
- Industrial operations analysts.
- Data governance managers.
- Visual analytics consultants.
- Engineering or science graduates looking to enter manufacturing industry.
What you'll work with
- ISO/IEC 25012 (data quality model)
- ISO 8000-8 (information and data quality measurement)
- ISO 22400-2 (KPIs for manufacturing operations management)
- ISO 9241-210 (human-centred design for interactive systems)
- WCAG 2.2 (web content accessibility guidelines)
- ISO/IEC 27001 (information security management)
- Regulation (EU) 2016/679 (General Data Protection Regulation)
- Power BI
- Tableau
- Grafana
- Apache Superset
- Python with Plotly and Dash
12 modules
1.1Data life cycle1.2BI architectures1.3Technical data versus business data1.4Key performance indicators1.5Analytics maturity models1.6Descriptive, diagnostic and predictive analytics1.7Roles in a data team1.8BI cases in engineering1.9Data literacy1.10Case study: maturity assessment of a plant
2.1Pre-attentive perception2.2Gestalt principles2.3Visual encoding of variables2.4Use of colour and accessibility2.5Visual hierarchy2.6Choosing the chart type2.7Visual errors and misleading charts2.8Dashboard design2.9Data storytelling2.10Practical exercise: redesign of a technical dashboard
3.1Relational models3.2Dimensional modelling3.3Facts and dimensions3.4Star and snowflake schemas3.5Slowly changing dimensions3.6Calendars and hierarchies3.7Semantic models3.8Granularity and aggregation3.9Performance best practices3.10Lab: dimensional model for maintenance
4.1Technical data sources4.2ETL and ELT processes4.3Cleansing and transformation4.4Sensor data and time series4.5Industrial connectors4.6Data warehouses and data lakes4.7Lakehouse architecture4.8Workflow orchestration4.9Data quality4.10Practical exercise: ETL flow with Power Query
5.1Modelling in Power BI5.2DAX language5.3Time intelligence5.4Measures and calculated columns5.5Row-level security5.6Custom visuals5.7Paginated reports5.8Publishing and workspaces5.9Performance optimisation5.10Lab: production dashboard in Power BI
6.1Tableau and its data model6.2Grafana for time series6.3Apache Superset6.4Visualisation with Python6.5Plotly and Dash6.6Web visualisation with D36.7Notebooks and reproducibility6.8Tool comparison6.9Selection by use case6.10Practical exercise: monitoring panel in Grafana
7.1Interactive exploration7.2Filters, linking and focus plus context7.3Multivariate visualisation7.4Visual dimensionality reduction7.5Geospatial data7.6Networks and graphs7.7Visual anomaly detection7.8Time series analysis7.9Visualising uncertainty7.10Lab: visual exploration of sensor data
8.1Statistics for engineers8.2Regression and correlation8.3Time series forecasting8.4Anomaly detection8.5Classification and segmentation8.6Integrating models into dashboards8.7Augmented analytics8.8Explainability of results8.9Model validation8.10Practical exercise: consumption forecast integrated into a report
9.1OEE manufacturing indicators9.2Maintenance management9.3Energy and sustainability9.4Quality and statistical control9.5Construction projects9.6Infrastructure networks9.7Logistics and supply chain9.8Laboratories and testing9.9Occupational health and safety9.10Case study: OEE dashboard
10.1Data governance10.2Catalogue and lineage10.3Data quality10.4Metadata management10.5Security and access control10.6Personal data protection10.7Report life cycle10.8Governed self-service10.9Adoption indicators10.10Workshop: governance model for technical BI
11.1Data strategy11.2Gathering user requirements11.3Iterative prototyping11.4Agile methodologies in BI11.5Training and communities of practice11.6User adoption11.7Measuring value11.8Costs and licensing11.9Solution maintenance11.10Case study: BI roll-out in an engineering firm
12.1Definition of the analytical problem12.2State of the art12.3Data sources and preparation12.4Data model12.5Visual design12.6Advanced analytics12.7Evaluation with users12.8Conclusions12.9Thesis 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 fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Power BI, Tableau, Grafana, Apache Superset and Python with Plotly and Dash, in free, academic or evaluation versions. Exercises are based on real procedures: dashboard design with users, dimensional modelling of technical data and visual exploration of time series. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO/IEC 25012, ISO 8000-8, ISO 22400-2, ISO 9241-210, WCAG 2.2, ISO/IEC 27001, Regulation (EU) 2016/679.
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
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- 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 Power BI, Tableau, Grafana, Apache Superset and Python with Plotly and Dash, 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 suitable computer 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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