Automated Data Collection Course
This course gives you the skills to streamline how information is captured and managed in the digital era. You will learn to design and implement automated solutions that collect data from sensors, systems, APIs and other sources, saving time and resources, and to build efficient, scalable ETL (extract, transform, load) workflows that turn raw data into reliable information for maritime, port and industrial operations.
Prior knowledge check
These are the skills this program gives you. Tick the ones you already master and we will tell you which part of the syllabus is for you.
You are starting from scratch in this subject. The program is designed precisely for that.
What is the Automated Data Collection Course?
This short course prepares you to design end-to-end automated data collection systems. You will learn the key concepts of automated acquisition and the edge, gateway, cloud and hybrid architectures used in maritime, port and industrial operations, how to select sensors, acquisition electronics, interfaces and field protocols such as Modbus, NMEA, OPC UA and MQTT, how to automate ingestion, ETL and ELT with real-time validation and error handling, how to store, model and govern time series and georeferenced data, and how to secure, harden and keep the whole system running reliably in the field.
What sets this course apart: a complete view of the data flow from the sensor to analytical use; practical work with real data and up-to-date tools; automated ETL pipelines that load data into databases and data lakes; attention to data quality, validation and consistency at every stage; and a final focus on cybersecurity, environmental robustness and service continuity for systems deployed in demanding conditions.
Program highlights
- End-to-end design: from the source and acquisition to storage and analytical use.
- Instrumentation: sensors, signal conditioning, interfaces and field protocols.
- Automated ETL: real-time ingestion, transformation and validation pipelines.
- Data governance: time series, metadata, lineage, retention and access control.
- Cybersecurity and robustness: hardening, encryption, redundancy and contingency plans.
- Real cases: practical exercises with real data and operational challenges.
Syllabus of the Automated Data Collection Course
5 modules. Select a module to see its content.
Fundamentals of automated data capture, acquisition architectures and end-to-end flow design
- 1.1. Key concepts of automated acquisition: sampling, frequency, resolution, latency and operational continuity
- 1.2. Typical architectures: edge, gateway, cloud and hybrid models for maritime, port and industrial operations
- 1.3. Data flow design: source, acquisition, validation, storage and analytical use
- 1.4. Requirements definition: variables, tolerances, availability SLAs, environmental conditions and criticality
- 1.5. Normalization and standardization: naming conventions, signal catalogs, units and metadata conventions
Instrumentation, sensors, interfaces and protocols for automated collection
- 2.1. Sensor selection by variable: pressure, temperature, level, currents, turbidity, energy and vibration
- 2.2. Acquisition electronics: ADC and DAC, signal conditioning, filters, isolation and noise suppression
- 2.3. Industrial and embedded interfaces: UART, I2C, SPI, CAN, RS-485 and cabling considerations
- 2.4. Field protocols: Modbus, NMEA, OPC UA, MQTT and compatibility and integration criteria
- 2.5. Time synchronization: NTP and PTP, time-stamping, drift and consistency across multi-sensor series
Ingestion automation, ETL and ELT and real-time data validation
- 3.1. Continuous ingestion: queues, brokers, buffering, store-and-forward and tolerance to loss of connectivity
- 3.2. Operational ETL and ELT: parsing, transformation, enrichment and harmonization of units and scales
- 3.3. Automatic validation: rules, physical limits, cross-consistency checks and outlier detection
- 3.4. Error handling: retries, record quarantine, alerts and controlled service degradation
- 3.5. Format normalization: JSON, CSV and Parquet, schemas, data contracts and versioning
Storage, modeling and data governance for time series and georeferenced data
- 4.1. Data models: entities, events, signals, time series and sizing for high volumes
- 4.2. Databases and repositories: time-series, relational and data lake options according to query patterns
- 4.3. Metadata and lineage: catalog, provenance, quality, changes and audit of transformations
- 4.4. Retention and archiving: policies, compression, partitioning, cold storage and verifiable recovery
- 4.5. Access control: roles, permissions, segregation by project or client and traceability of reads and writes
Cybersecurity, field robustness and applied automated data collection project
- 5.1. Data attack surface: endpoints, gateways, credentials, APIs and basic hardening
- 5.2. Communications security: encryption, authentication, key rotation and network segmentation
- 5.3. Environmental robustness: corrosion, watertightness, electromagnetic interference and preventive maintenance
- 5.4. Service continuity: redundancy, failover, backups, recovery tests and contingency plans
- 5.5. Compliance and evidence: logs, internal audits, data policies and operational traceability
Duration and format of the Automated Data Collection Course
It is 300 hours. Tell us how many you can study each week and we will tell you when you will really finish.
This is the pace at which most students finish. It fits well with the installment plan.
Academic leadership and faculty
The Navalis teaching team: working professionals who teach and supervise the programs.
Dr. Marina AlcoverAcademic Director and Head of Quality Assurance
Eng. Diego CifuentesSenior Lecturer in Naval Welding and Non-Destructive Testing; Head of QA/QC for Welded Joints
Capt. Javier Abaroa (MCA)Senior Lecturer in Ship Handling, Electronic Navigation (ECDIS/Radar/ARPA) and GMDSS; STCW Compliance and Bridge Resource Management Coordinator
Dr. Pau FerrerSenior Lecturer in Naval Architecture and Simulation; Director of the Hydrodynamics and CFD Laboratory
Dr. Nuria LlobregatSenior Lecturer in Marine Meteorology, SAR and Operational Safety; Navigation Risk Management Coordinator
Eng. Javier BañulsSenior Lecturer in Electrical Engineering, Automation and ETO Engineering; Electronic Integration and Engine Alarms Coordinator
Eng. Sofía MarquinaSenior Lecturer in Materials, Composites and Corrosion Protection; Coordinator of the Advanced Paints and Coatings Line
Eng. Tomás RieraSenior Lecturer in Propulsion, Alternative Energy and Hybrid Systems; Energy Performance and Vibration CoordinatorObjectives of the Automated Data Collection Course
Career opportunities after the Automated Data Collection Course
Questions about the Automated Data Collection Course
Using technology to collect data with no or minimal human intervention, which improves efficiency, accuracy and coverage.
Reading sensors and equipment, extracting data from systems, databases, documents and web sources, and validating and loading it into storage.
Basic programming knowledge in Python, R or a similar language is recommended, together with familiarity with databases and APIs.
Yes. One module deals with sensor selection, acquisition electronics, industrial interfaces and protocols such as Modbus, NMEA, OPC UA and MQTT.
Yes. The final module covers the data attack surface, encryption, authentication, network segmentation and service continuity.
Fully online, through a virtual campus available 24/7, with live sessions that are recorded and tutorials with the teaching staff.
The course comprises 300 hours of study across 5 modules of about 60 hours each. You can progress at your own pace within the schedule set when you enroll.
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Admission process
No waiting for a call back. If your documents are in order, you can enroll in the same session.
- Profile: aimed at data analysts and data scientists, software engineers and developers, digital marketing professionals, researchers, entrepreneurs and technical staff in maritime, port and industrial operations who want to automate data collection; basic programming skills in Python, R or another language, familiarity with databases, APIs and data structures such as JSON or XML, and basic use of the command line are recommended.
- Language: sufficient command of English to follow the classes and the technical documentation.
- Technical resources: a computer able to run programming software, a stable internet connection, an up-to-date browser and a camera for live sessions and project presentations.
- Admission: online application, review of your profile and a short interview with the academic team.
- 01ApplicationYour details, background and documents.
- 02Instant reviewWe check that everything is complete and in order.
- 03EnrollmentChoose full payment or instalments and reserve your place.
Admission is provisional until the committee validates the documents provided. If you do not meet the requirements, the full amount is refunded.
Ask us before you enroll
Leave your details and admissions will write to you to answer any questions. If you are already sure, you can apply and pay in the same session.