Master’s in Data-Driven Design & Usage Telemetry
Online master's programme of 1,900 hours in data-driven design and usage telemetry: UX instrumentation, KPIs and experimentation. 12 modules covering evidence-based design, tracking plans, privacy and compliance, data pipelines, behavioural analysis, A/B testing, mixed-methods research, data visualisation, machine learning, telemetry in physical products and metrics-driven product management, with an assigned technical tutor.
Master’s in Data-Driven Design & Usage Telemetry — studying from the United Kingdom
Materials science and advanced manufacturing are longstanding strengths in the United Kingdom, with centres including the Manufacturing Technology Centre near Coventry, the Advanced Manufacturing Park in Sheffield and the High Value Manufacturing Catapult. Industries from aerospace to automotive rely on composites, metals and additive manufacturing expertise. Quality management and conformity to standards are critical to supply chains. The British Standards Institution and the UK Accreditation Service support certification and quality assurance across manufacturing.
Fees for the United Kingdom: £6,600 (Pound sterling), with instalments and employer sponsorship available.
Relevant bodies in the United Kingdom
Typical roles
Questions from the United Kingdom
Which UK professional bodies cover materials and quality engineering, and how is membership gained?
Relevant bodies include the Institute of Materials, Minerals and Mining, the Chartered Quality Institute and the Institution of Mechanical Engineers. Membership usually depends on qualifications accredited by or recognised by the body, plus professional experience. SEIUM's private qualifications are not accredited by these bodies, so they will decide independently whether any part of your training counts towards membership.
Will manufacturers and quality departments in the UK recognise a private online qualification?
Recognition varies by employer. Many UK manufacturers focus on demonstrable competence, industry experience and certifications such as Six Sigma or ISO-related training. A private qualification can support your profile, particularly when your final project shows practical materials or quality work, but it is not a state degree and its acceptance is at the employer's discretion.
Can I study this online from the UK while working shifts, and do I need a visa?
The programmes are fully online and taught in English, so you can study from home across UK time zones and fit sessions around shift patterns. No student visa is needed because you are studying from within the UK. Fees can be paid in instalments, and employer sponsorship is possible. Assigned technical tutors can help you manage the workload.
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
Define product metrics aligned with business and experience goals.
Design reliable, versioned instrumentation and telemetry plans.
Meet privacy and data protection requirements when collecting usage data.
Analyse user behaviour with funnels, cohorts and segmentation.
Plan and evaluate A/B experiments with statistical rigour.
Combine qualitative and quantitative research to make design decisions.
Communicate findings through effective visualisations and dashboards.
Who it's for
- Product designers who want to base their decisions on usage data.
- Product analysts working with funnels, cohorts and experiments.
- Telemetry engineers instrumenting software, vehicles or devices.
- Quantitative UX researchers.
- Growth designers running experiments on activation and retention.
- Product managers building evidence-based roadmaps.
- Engineering or science graduates aiming to enter software and apps.
What you'll work with
- Regulation (EU) 2016/679, GDPR (protection of personal data)
- Regulation (EU) 2023/2854, Data Act (access to and use of data from connected products)
- Directive 2002/58/EC, ePrivacy (privacy in electronic communications and cookies)
- ISO 9241-210 (human-centred design for interactive systems)
- ISO/IEC 27001 (information security management systems)
- ISO/IEC 27701 (privacy information management)
- ISO/IEC 25010 (software product quality model)
- Amplitude
- Mixpanel
- Google BigQuery
- Apache Kafka
- Tableau
12 modules
1.1From intuitive to evidence-based design1.2Adoption, retention and engagement metrics1.3The HEART framework for experience metrics1.4North Star metric and metric trees1.5Qualitative versus quantitative data1.6Biases in data interpretation1.7Ethics and transparency with users1.8Experimentation culture in teams1.9Roles: designer, analyst and data engineer1.10Case study: metric tree for an app
2.1Event and property taxonomy2.2Tracking plan and data dictionary2.3Analytics SDKs for web and mobile2.4Embedded telemetry in IoT devices2.5Connected vehicle data and the CAN bus2.6Sampling, batching and energy consumption2.7Event schema versioning2.8Data quality and validation2.9User identity and sessions2.10Workshop: tracking plan for a product
3.1GDPR applied to usage data3.2Consent and CMP platforms3.3Anonymisation and pseudonymisation3.4Differential privacy in aggregated metrics3.5Data minimisation and retention periods3.6ePrivacy Directive and cookies3.7The Data Act and connected products3.8Data protection impact assessments (DPIA)3.9International data transfers3.10Case study: DPIA for vehicle telemetry
4.1Streaming event ingestion4.2Apache Kafka and message queues4.3Data warehouses: BigQuery and Snowflake4.4Dimensional modelling with dbt4.5Data lakes and the Parquet format4.6Edge preprocessing4.7Orchestration with Airflow4.8Cloud costs and scalability4.9Data governance and catalogues4.10Lab: event pipeline with Kafka
5.1Conversion funnels5.2Cohorts and retention curves5.3User paths and flows5.4Behavioural segmentation5.5Survival and churn analysis5.6Feature adoption and discoverability5.7Heatmaps and session replay5.8Perceived performance and latency5.9Analytical SQL with window functions5.10Practical: cohort analysis in SQL
6.1Design of controlled experiments6.2Hypotheses, power and sample size6.3Frequentist statistical tests6.4Bayesian approach to experimentation6.5Multivariate tests and multi-armed bandits6.6Feature flags and progressive rollout6.7Common pitfalls: peeking and SRM6.8Guardrail metrics and side effects6.9Firmware experiments via OTA6.10Case study: A/B test of an onboarding flow
7.1Triangulation of sources7.2Interviews guided by usage data7.3In-product surveys and NPS7.4Diary studies combined with telemetry7.5Unmoderated remote usability testing7.6Analysis of text feedback with NLP7.7Data-driven personas7.8Applied jobs-to-be-done framework7.9Synthesis and prioritisation of findings7.10Workshop: mixed-methods study of a feature
8.1Principles of visual perception in charts8.2Product dashboards in Looker and Tableau8.3Storytelling with data8.4Choosing the chart type8.5Metrics for executive committees8.6Alerts and anomaly detection8.7Automated and scheduled reports8.8Data literacy for designers8.9Common visualisation mistakes8.10Practical: product dashboard
9.1Clustering of usage profiles9.2Predictive churn models9.3Recommender systems9.4Adaptive interface personalisation9.5Sequential pattern detection9.6Sensor time series9.7Model explainability9.8MLOps for product teams9.9Algorithmic bias and fairness9.10Lab: clustering of usage patterns
10.1Usage sensors in home appliances10.2HMI data in automotive10.3Wearables and health devices10.4Usage-informed predictive maintenance10.5Digital twins of real-world use10.6Sensor drift and signal quality10.7Hardware design cycle with field data10.8New product generations based on real use10.9Warranty and claims data10.10Case study: redesigning a control from telemetry
11.1OKRs and product metrics11.2Prioritisation with RICE and ICE11.3Evidence-based roadmaps11.4Continuous product discovery11.5Organising growth teams11.6Amplitude and Mixpanel tools11.7Feature business cases11.8Ethics of persuasive design11.9Analytics maturity models11.10Workshop: quarterly roadmap with OKRs
12.1Product selection and design question12.2Instrumentation plan12.3Privacy assessment12.4Data collection and cleaning12.5Exploratory data analysis12.6Experiment design12.7Redesign proposal12.8Impact measurement12.9Master's Thesis report12.10Master's Thesis: defence before a panel
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, making it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Amplitude, Mixpanel, Google BigQuery, Apache Kafka and Tableau, in free, academic or evaluation versions. Cases are studied on real procedures: A/B tests on features in production, cohort analysis with telemetry data and mixed studies combining interviews with usage metrics. Assessment is continuous by 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 9241-210, ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 25010.
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 Amplitude, Mixpanel, Google BigQuery, Apache Kafka and Tableau, 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 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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