Diploma in Wearables and Performance Data in Actuarial Science
Online postgraduate diploma of 800 hours in wearables and performance data in actuarial science. 8 modules covering wearable devices and health data, data processing, physical activity and risk, actuarial modelling, interactive insurance products, legal and ethical framework and programme governance, with an assigned technical tutor.
Diploma in Wearables and Performance Data in Actuarial Science — studying from Singapore
Singapore's off-road and special vehicle sector is smaller than in larger industrial economies but serves port operations, construction, airport ground support and marine logistics. Equipment such as cranes, forklifts and specialised port machinery is maintained and developed locally. The Ministry of Manpower and the Land Transport Authority set safety and operating standards. Employers include port operators, construction groups and heavy equipment dealers.
Fees for Singapore: S$1,280 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
Will this qualification be recognised by professional bodies in Singapore?
SEIUM's qualifications are private, not official state degrees, and are not accredited by any national body in Singapore. Engineers may seek Professional Engineer registration through the Professional Engineers Board, and machinery work may also involve Workplace Safety and Health requirements set by the Ministry of Manpower. Recognition of a private qualification is decided by employers or the relevant body.
How might this training be used by employers in Singapore?
Singapore's construction, port, logistics, marine and industrial sectors use heavy machinery and special vehicles, so these skills may be relevant to maintenance, design and equipment engineering roles. The programme does not guarantee employment. Employers usually weigh practical experience, safety knowledge and project work alongside any qualification.
Can I study this alongside a full-time job, and can my employer help fund it?
Study is fully online and in English, with an assigned technical tutor, so you can fit it around work from home in Singapore. Workload ranges from 300 to 1,900 hours depending on the programme. Fees can be paid in instalments, and employer sponsorship is possible; many Singapore employers fund job-relevant training, so ask your management.
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
Know wearable devices and the data they generate.
Process device data with quality and security.
Relate physical activity to biometric risk.
Incorporate activity data into actuarial models.
Design interactive insurance products.
Comply with the legal and ethical framework for using health data.
Evaluate and govern wearable-based programmes.
Who it's for
- Life and health actuaries.
- Insurance data scientists.
- Insurance product designers.
- Data protection officers.
- Digital health analysts.
- Wellness programme managers.
- Engineering or science graduates seeking to enter life insurance.
What you'll work with
- Regulation (EU) 2016/679 (GDPR) (General Data Protection Regulation)
- Directive 2009/138/EC (Solvency II) (EU insurance solvency framework)
- Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
- Regulation (EU) 2017/745 (medical devices)
- Directive (EU) 2016/97 (IDD) (Insurance Distribution Directive)
- ISO/IEC 27001 (information security management)
- Law 50/1980 (Spanish Insurance Contract Act)
- R
- Python with pandas
- Apple HealthKit
- Google Health Connect
- Microsoft Power BI
8 modules
1.1Device types: bands, watches, patches1.2Sensors: accelerometer, PPG, ECG1.3Variables: steps, heart rate, sleep1.4Heart rate variability1.5Device accuracy and validation1.6Medical devices versus consumer devices1.7Regulation (EU) 2017/7451.8Data platforms and APIs1.9Digital health ecosystems1.10Workshop: comparing devices and variables
2.1Ingesting device data2.2Cleaning and missing values2.3Time synchronisation2.4Daily and weekly aggregation2.5Detection of manipulated data2.6Data quality and bias2.7Secure storage2.8Cloud architectures2.9Data governance2.10Practical exercise: cleaning activity data in Python
3.1Epidemiological evidence on activity3.2Sleep and mortality3.3Fitness indicators3.4Sedentary behaviour and morbidity3.5Risk models with lifestyle variables3.6Causation versus correlation3.7Selection and self-selection3.8Effect of the programme on behaviour3.9Impact evaluation3.10Case study: relationship between activity and sick leave
4.1Generalised linear models with activity data4.2Survival models4.3Credibility and experience4.4Machine learning in pricing4.5Validation and stability4.6Model explainability4.7Dynamic pricing4.8Reserves and solvency4.9Stress testing4.10Lab: mortality model with activity variables in R
5.1Life insurance with wellness programmes5.2Health insurance with incentives5.3Group insurance for companies5.4Rewards and discounts5.5Design of behavioural incentives5.6Gamification5.7Customer retention5.8User experience5.9Market cases5.10Practical exercise: designing an interactive life product
6.1GDPR and health data6.2Explicit consent6.3Data minimisation and purpose limitation6.4Regulation (EU) 2024/1689 on artificial intelligence6.5Non-discrimination in insurance6.6Transparency towards the customer6.7IDD Directive and product governance6.8Device cyber security6.9Ethics committees6.10Workshop: data protection impact assessment
7.1Programme success indicators7.2Experiments and control groups7.3Cost-benefit analysis7.4Model monitoring7.5Product governance7.6Reports to management7.7Relationship with device providers7.8Operational risk management7.9Continuous improvement7.10Case study: evaluation of a pilot programme
8.1Definition of the insured group8.2Selection of data and devices8.3Data processing8.4Risk model8.5Product design8.6Legal and ethical assessment8.7Evaluation plan8.8Conclusions8.9Report and presentation8.10Final project: final review with the tutor
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 work.
Each module combines theory, guided reading of the original regulation and applied exercises. The working software is R, Python with pandas, Apple HealthKit, Google Health Connect and Microsoft Power BI, in free, academic or evaluation versions. Procedures are studied on real cases of risk modelling with activity data, design of interactive insurance products and evaluation of wellness programmes with control groups. Assessment is continuous by module plus the final project.
Standards covered: Regulation (EU) 2016/679 (GDPR), Directive 2009/138/EC (Solvency II), Regulation (EU) 2024/1689, Regulation (EU) 2017/745, Directive (EU) 2016/97 (IDD), ISO/IEC 27001, Law 50/1980.
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
- Prior technical training or professional experience in the sector. A university degree is not required.
- 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 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with R, Python with pandas, Apple HealthKit, Google Health Connect, Microsoft Power BI, 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), 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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