Postgraduate Diploma in Time-Series and Data Pipelines
Online Postgraduate Diploma in Time-Series and Data Pipelines, 800 h. 8 modules covering IEC 62541, ISA-95 (IEC 62264) and InfluxDB. Assigned tutor and open enrolment.
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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 the time-series data generated on the plant floor.
Select and model time-series databases suited to the use case.
Build reliable ingestion flows from industrial protocols.
Transform and cleanse data through stream and batch processing.
Apply analytics and anomaly detection techniques to process signals.
Design dashboards that are useful for operations and management.
Govern and protect the data pipeline at multi-plant scale.
Who it's for
- Industrial data engineer who wants to specialise in Time-Series and Data Pipelines.
- Process data analyst who wants to specialise in Time-Series and Data Pipelines.
- IIoT platform architect who wants to specialise in Time-Series and Data Pipelines.
- MES systems engineer who wants to specialise in Time-Series and Data Pipelines.
- Predictive maintenance specialist who wants to specialise in Time-Series and Data Pipelines.
- Data pipeline developer who wants to specialise in Time-Series and Data Pipelines.
- Engineering or science graduates seeking to enter discrete manufacturing.
What you'll work with
- IEC 62541
- ISA-95 (IEC 62264)
- ISO/IEC 20922
- ISO 8000
- IEC 62443-3-3
- ISO/IEC 25012
- Regulation (EU) 2023/2854 on Data
8 modules · 800 h
1.1Characteristics of process data1.2Sampling, resolution and acquisition frequency1.3Industrial historians and their evolution1.4Tags, metadata and asset hierarchies1.5Data quality and OPC status codes1.6Volume, retention and cardinality1.7Event data versus continuous data1.8ISA-95 model for contextualising data1.9Latency requirements of use cases1.10Laboratory: exploring a historian with real data
2.1InfluxDB architecture and its data model2.2TimescaleDB on PostgreSQL2.3Comparison with QuestDB and other engines2.4Schemas, indexing and time partitioning2.5Compression and retention policies2.6Downsampling and continuous aggregates2.7Queries with Flux, InfluxQL and SQL2.8High availability and replicas2.9Storage sizing2.10Practical: modelling a schema for a production line
3.1Push and pull patterns in data acquisition3.2MQTT and Sparkplug B as a transport layer3.3Apache Kafka for high-volume streams3.4Telegraf and collection agents3.5OPC UA connectors to time-series databases3.6Delivery guarantees and message ordering3.7Buffers and backpressure3.8Serialisation with JSON, Avro and Protobuf3.9Unified Namespace as an integration pattern3.10Workshop: ingesting from OPC UA into InfluxDB with Telegraf
4.1ETL and ELT architectures for plant data4.2Stream processing with Kafka Streams4.3Time windows and aggregations4.4Interpolation and gap filling4.5Noise filtering and outlier detection4.6Aligning signals with different frequencies4.7Enrichment with batch and order context4.8Workflow orchestration with Apache Airflow4.9Automated data quality testing4.10Practical: cleansing and shift-based aggregation pipeline
5.1Decomposition into trend and seasonality5.2Anomaly detection in process signals5.3Correlation between machine variables5.4ARIMA models and exponential smoothing5.5Introduction to machine learning models5.6Vibration-based predictive maintenance5.7Calculating performance indicators5.8Python with pandas for time-series analysis5.9Model validation with historical data5.10Case study: anomaly detection in compressor consumption
6.1Operational dashboards with Grafana6.2Alarms and thresholds on series6.3Shift and production reports6.4Designing plant dashboards6.5Role-based and multi-tenant access6.6Integration with MES and ERP6.7REST APIs for data consumption6.8Export to BI tools6.9Best practices in time-series visualisation6.10Workshop: energy dashboard by line
7.1Edge, fog and cloud architecture7.2Replication between plant and data centre7.3Data governance and catalogues7.4Lineage and traceability of transformations7.5Flow security and encryption in transit7.6Network segmentation for the pipeline7.7Storage and compute costs7.8Backup and recovery7.9Monitoring the pipeline itself7.10Case study: scaling five plants to a central platform
8.1Defining use cases and metrics8.2Inventory of sources and tags8.3Designing the data architecture8.4Implementing ingestion8.5Transformations and quality control8.6Analytics and visualisation of results8.7Security and governance plan8.8Evaluating pipeline performance8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is InfluxDB, TimescaleDB, Apache Kafka, Grafana, Telegraf, in free, academic or evaluation versions. Practicals are based on real procedures: ingesting data from a line from OPC UA into a time-series database, anomaly detection in energy consumption series, and designing dashboards for shift supervision. 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 the time-series data generated on the plant floor.
Select and model time-series databases suited to the use case.
Build reliable ingestion flows from industrial protocols.
Transform and cleanse data through stream and batch processing.
Apply analytics and anomaly detection techniques to process signals.
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.
- Sufficient command of Spanish to follow technical standards documentation.
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 is an average of 10 to 15 hours per week, compatible with a full-time job.
You will work with InfluxDB, TimescaleDB, Apache Kafka, Grafana, Telegraf. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
An SEIUM Institute 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 recognition policy when you enrol.
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