Postgraduate Diploma in Time-Series, Pipelines and Streaming
Online 800-hour Postgraduate Diploma in Time-Series, Pipelines and Streaming. 8 modules covering ISO/IEC 20547-3, ISO 8000-8 and Apache Kafka. Assigned tutor and open enrolment.
Postgraduate Diploma in Time-Series, Pipelines and Streaming — studying from Singapore
Singapore is one of Asia's leading electronics manufacturing centres, with semiconductor fabrication, back-end assembly and test, and precision electronics concentrated in areas such as Tampines and Jurong. The sector also includes embedded systems for industrial and automotive use and a strong telecommunications base led by the Infocomm Media Development Authority. Major employers include global semiconductor firms and electronics manufacturers. Engineers work on hardware design, firmware, communications systems and test engineering.
Fees for Singapore: S$1,840 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
How are electronics and telecom engineers recognised in Singapore, and does SEIUM count?
Telecom and electronics engineering is regulated by the Infocomm Media Development Authority (IMDA), and engineers may register with the Professional Engineers Board (PEB). SEIUM's qualifications are private, not state degrees, and not accredited by any national body. Whether they are recognised is decided by employers or the relevant body, so confirm this before enrolling.
Which Singapore employers hire embedded systems and telecom engineers?
Singapore has demand from telecom operators, semiconductor and electronics manufacturers, defence and aerospace firms, and industrial automation companies. Employers generally assess hands-on skills in firmware, hardware design and testing. Your final project can evidence embedded or telecom competence, but its value is for each employer to judge.
Can I follow this from Singapore while working, and does it need a visa?
Yes, the programmes are 100% online and taught in English, so no student visa is needed to study from home in Singapore. Expect a heavy workload: 1,900 hours for a master's, 800 for a postgraduate diploma and 300 for a specialist course. Fees can be paid in instalments, and employer sponsorship is possible.
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
Characterise industrial-origin time-series data.
Select and model time-series databases.
Implement data ingestion with Apache Kafka.
Develop stream processing with windows and state.
Design reliable and reprocessable data pipelines.
Build dashboards and alerts on real-time data.
Apply governance, security and cost control practices.
Who it's for
- Data engineer looking to specialise in Time-Series, Pipelines and Streaming.
- Streaming engineer looking to specialise in Time-Series, Pipelines and Streaming.
- Industrial data architect looking to specialise in Time-Series, Pipelines and Streaming.
- Data scientist looking to specialise in Time-Series, Pipelines and Streaming.
- Platform engineer looking to specialise in Time-Series, Pipelines and Streaming.
- Operations data analyst looking to specialise in Time-Series, Pipelines and Streaming.
- Graduates in engineering or sciences seeking to enter industry.
What you'll work with
- ISO/IEC 20547-3
- ISO 8000-8
- ISO/IEC 20546
- ISO/IEC 27001
- Regulation (EU) 2016/679
- Regulation (EU) 2023/2854
8 modules · 800 h
1.1Characteristics of sensor time-series1.2Sampling frequencies and resolution1.3Timestamps, time zones and clocks1.4Irregular data and gaps1.5Labels, dimensions and cardinality1.6Aggregations and time windows1.7Time-series compression1.8Data models for assets1.9Volume, velocity and retention1.10Practical: profiling a sensor dataset
2.1InfluxDB and its query language2.2TimescaleDB and hypertables2.3QuestDB and columnar storage2.4Time and tag indexing2.5Downsampling and retention policies2.6Analytical and continuous queries2.7Partitioning and horizontal scaling2.8Backups and high availability2.9Performance comparison2.10Laboratory: data model in TimescaleDB
3.1Topics, partitions and offsets3.2Producers, consumers and groups3.3Delivery guarantees and exactly-once3.4Schemas with Schema Registry and Avro3.5Kafka Connect and industrial connectors3.6Record retention and compaction3.7Replication and fault tolerance3.8Security with SASL and TLS3.9Cluster monitoring3.10Workshop: ingesting MQTT into Kafka
4.1Event time and processing time4.2Sliding, tumbling and session windows4.3Watermarks and late data4.4State and checkpoints in Apache Flink4.5Kafka Streams and ksqlDB4.6Joins between streams and tables4.7Complex pattern detection (CEP)4.8Real-time KPI calculation4.9Scaling and task rebalancing4.10Laboratory: aggregation window in Flink
5.1Lambda and Kappa architectures5.2ETL versus ELT5.3Orchestration with Apache Airflow5.4Medallion architecture in data lakes5.5Parquet, Delta Lake and Apache Iceberg formats5.6Partitioning and file optimisation5.7Idempotency and reprocessing5.8Schema management and evolution5.9Automated pipeline testing5.10Practical: combined batch and streaming pipeline
6.1Real-time dashboards with Grafana6.2Threshold- and trend-based alerts6.3Anomaly detection in time-series6.4Forecasting with ARIMA and Prophet models6.5Features for machine learning6.6Feature stores and model serving6.7Integration with BI tools6.8Ad hoc queries on historical data6.9Explainability of indicators6.10Case study: vibration anomaly detection
7.1Pipeline observability7.2Data quality in streams7.3Data lineage and catalogue7.4Data security in transit and at rest7.5Role-based access control7.6GDPR and Data Act compliance7.7Storage and compute costs7.8Disaster recovery plans7.9Documentation and data contracts7.10Workshop: quality alerts in a pipeline
8.1Use case and data sources8.2Streaming architecture design8.3Time-series storage selection8.4Ingestion implementation8.5Processing and indicator calculation8.6Visualisation and alerts8.7Load and reliability testing8.8Cost assessment8.9Report and presentation8.10Final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and a dedicated technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it easy to combine with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Kafka, Apache Flink, InfluxDB, TimescaleDB, Grafana, in free, academic or evaluation versions. Tests are studied using real procedures for ingestion load testing, reprocessing of late data and real-time anomaly detection. Assessment is continuous per module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Characterise industrial-origin time-series data.
Select and model time-series databases.
Implement data ingestion with Apache Kafka.
Develop stream processing with windows and state.
Design reliable and reprocessable data pipelines.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- A level of Spanish sufficient to follow technical regulatory texts.
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, spread over 6 to 8 months. That works out to an average of 10 to 15 hours per week, which is compatible with full-time work.
You will work with Apache Kafka, Apache Flink, InfluxDB, TimescaleDB and Grafana. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the higher programme. Check the credit recognition policy when you enrol.
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