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Master'sDesign, HMI & Product Applications open

Master’s in Data-Driven Design & Usage Telemetry Engineering

Online master's programme of 1,900 hours in data-driven design and usage telemetry engineering: 12 modules covering instrumentation, data infrastructure with Kafka and dbt, usage analysis, experimentation, machine learning for design, visualisation and privacy under the GDPR and the Data Act, 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

Formulate measurable design questions using usage data.

02

Design instrumentation and telemetry plans.

03

Build data pipelines for product analytics.

04

Analyse usage behaviour and design experiments.

05

Apply machine learning to personalisation and segmentation.

06

Comply with the GDPR and the Data Act in telemetry systems.

07

Combine quantitative and qualitative data in design decisions.

Profile

Who it's for

  • Data-driven designers who want to base design decisions on real usage data.
  • Product analysts working with funnels, segments and experiments.
  • UX data scientists modelling user behaviour.
  • Telemetry engineers instrumenting connected products and vehicles.
  • Mixed-methods researchers combining telemetry with qualitative studies.
  • Product analytics leads building data-driven teams.
  • Engineering or science graduates aiming to move into the connected automotive sector.
Standards & tools

What you'll work with

  • Regulation (EU) 2016/679 (GDPR) (General Data Protection Regulation)
  • Regulation (EU) 2023/2854 (Data Act) (harmonised rules on fair access to and use of data)
  • Directive 2002/58/EC (ePrivacy) (privacy and electronic communications)
  • ISO/IEC 27701:2019 (privacy information management)
  • ISO/IEC 20889:2018 (privacy-enhancing data de-identification techniques)
  • ISO/IEC 25012:2008 (data quality model)
  • ISO 9241-210:2019 (human-centred design for interactive systems)
  • Amplitude
  • Apache Kafka
  • dbt
  • Tableau
  • Python with pandas
Syllabus

12 modules · 1900 h

  1. 1.1Data versus intuition in design
  2. 1.2Combining quantitative and qualitative data
  3. 1.3The build, measure, learn cycle
  4. 1.4Telemetry in physical and digital products
  5. 1.5Measurable design questions
  6. 1.6Biases in usage data
  7. 1.7Ethics of data use
  8. 1.8Telemetry cases in the automotive industry
  9. 1.9Data culture in design teams
  10. 1.10Case study: a design decision backed by data
  1. 2.1Interaction instrumentation plan
  2. 2.2Event schemas and taxonomy
  3. 2.3Analytics SDKs in apps
  4. 2.4Telemetry on edge devices
  5. 2.5Connected vehicle data
  6. 2.6CAN signals and on-board logging
  7. 2.7Sampling and aggregation at source
  8. 2.8Data quality and validation
  9. 2.9Versioning of telemetry schemas
  10. 2.10Lab: instrumentation of an HMI prototype
  1. 3.1Telemetry ingestion pipelines
  2. 3.2Event streaming with Kafka
  3. 3.3Data lakes and the Parquet format
  4. 3.4Analytical data warehouses
  5. 3.5Data transformation with dbt
  6. 3.6Cloud data platforms
  7. 3.7Storage and compute costs
  8. 3.8Data governance and lineage
  9. 3.9Data catalogues and metadata
  10. 3.10Practical exercise: event pipeline with Kafka and Parquet
  1. 4.1Frequency and depth of use
  2. 4.2Navigation path analysis
  3. 4.3Task funnels
  4. 4.4Time on task and efficiency
  5. 4.5Errors and undo actions
  6. 4.6Feature abandonment
  7. 4.7Behavioural user segmentation
  8. 4.8Context of use: speed, time of day and weather
  9. 4.9Interaction heatmaps
  10. 4.10Workshop: use of infotainment menus
  1. 5.1Statistical inference for design
  2. 5.2A/B test design
  3. 5.3Power and sample size
  4. 5.4Bayesian approaches to experimentation
  5. 5.5Multiple comparisons and false positives
  6. 5.6Experiments on connected fleets
  7. 5.7Causal inference and confounders
  8. 5.8Quasi-experimental designs
  9. 5.9Heterogeneous effects by segment
  10. 5.10Lab: A/B test with Bayesian analysis
  1. 6.1Clustering of usage profiles
  2. 6.2Predicting user churn
  3. 6.3Recommender systems
  4. 6.4Detecting usage anomalies
  5. 6.5Interaction sequence models
  6. 6.6Adaptive interface personalisation
  7. 6.7Model explainability with SHAP
  8. 6.8Deploying models in the product
  9. 6.9Assessing model impact
  10. 6.10Practical exercise: driver segmentation with clustering
  1. 7.1Dashboards for design teams
  2. 7.2Storytelling with data
  3. 7.3Designing effective charts
  4. 7.4Tableau and Power BI tools
  5. 7.5Tailoring the message to audiences
  6. 7.6Periodic usage reports
  7. 7.7Automatic metric alerts
  8. 7.8Self-service analytics
  9. 7.9Common interpretation errors
  10. 7.10Case study: usage report for a product committee
  1. 8.1Legal bases under the GDPR
  2. 8.2Consent and preferences
  3. 8.3Anonymisation and pseudonymisation
  4. 8.4Personal data in connected vehicles
  5. 8.5Obligations under the Data Act
  6. 8.6ePrivacy Directive and cookies
  7. 8.7Data minimisation and retention
  8. 8.8Data protection impact assessments (DPIA)
  9. 8.9International data transfers
  10. 8.10Workshop: DPIA for a telemetry system
  1. 9.1Triangulating data and interviews
  2. 9.2Telemetry-guided interviews
  3. 9.3Diary studies with logging
  4. 9.4In-product contextual surveys
  5. 9.5User feedback analysis
  6. 9.6Usage session recordings
  7. 9.7Contextual research in the vehicle
  8. 9.8Mixed-methods synthesis
  9. 9.9Evidence-based prioritisation of improvements
  10. 9.10Practical exercise: mixed-methods study of an underused feature
  1. 10.1Connected home appliances and their use
  2. 10.2Vehicle usage data
  3. 10.3Wearables and health
  4. 10.4Monitored industrial machinery
  5. 10.5Usage and wear sensors
  6. 10.6Energy consumption and battery
  7. 10.7Connectivity constraints
  8. 10.8From field data to redesign
  9. 10.9Warranty and reliability data
  10. 10.10Lab: telemetry for a prototype with ESP32
  1. 11.1Roles of data analyst and data scientist
  2. 11.2Analysts embedded in teams
  3. 11.3Democratising data access
  4. 11.4Governance of shared metrics
  5. 11.5The product north star metric
  6. 11.6Data review rituals
  7. 11.7Data literacy in design
  8. 11.8Balancing data and creativity
  9. 11.9Selecting the analytics stack
  10. 11.10Case study: data-driven organisational model
  1. 12.1Design question and metrics
  2. 12.2Proposed instrumentation plan
  3. 12.3Data collection and preparation
  4. 12.4Exploratory usage analysis
  5. 12.5Design of a controlled experiment
  6. 12.6Complementary qualitative findings
  7. 12.7Data-driven redesign proposal
  8. 12.8Privacy and ethics assessment
  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 each 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 Amplitude, Apache Kafka, dbt, Tableau and Python with pandas, in free, academic or evaluation versions. Practice is based on real procedures for analysing real telemetry from connected products, A/B experiments on fleets and applications, and mixed-methods studies with users. Assessment is continuous for each module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2023/2854 (Data Act), Directive 2002/58/EC (ePrivacy), ISO/IEC 27701:2019, ISO/IEC 20889:2018, ISO/IEC 25012:2008, ISO 9241-210:2019.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the GDPR, the Data Act and the ePrivacy Directive applied to real cases, not as a theoretical annex.
Industry software:Amplitude, Apache Kafka, dbt, Tableau and Python with pandas, in free, academic or evaluation versions.
Master's Thesis with defence:A complete data-driven redesign, from design question and instrumentation plan to analysis, a controlled experiment and a privacy assessment, 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.

Design question and instrumentation
Define a measurable design question, its metrics and the instrumentation plan.
Data collection and analysis
Collect and prepare usage data and carry out an exploratory analysis.
Controlled experiment
Design a controlled experiment and complement it with qualitative findings.
Privacy and ethics assessment
Evaluate the privacy and ethical implications of the telemetry system.
Master's Thesis
Present a data-driven redesign proposal, defended before a panel.
Career opportunities

Where this takes you

Data-driven designerProduct analystUX data scientistTelemetry engineerMixed-methods researcherProduct analytics lead
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 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.

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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 Amplitude, Apache Kafka, dbt, Tableau and Python with pandas, 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 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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