Automated Data Collection Course
Why this course?
The Automated Data Collection course
This course will provide you with the necessary skills to optimize information extraction and management in the digital age. You will learn to design and implement automated solutions for collecting data from diverse sources, saving time and resources. This program will train you in the use of web scraping, API, and ETL (Extract, Transform, Load) tools and techniques to build efficient and scalable workflows.
Key Advantages
- Mastery of scraping tools: BeautifulSoup, Scrapy, and Selenium.
- API integration: Obtain data directly from platforms like Twitter, Facebook, or Google.
- ETL workflow automation: Design pipelines to transform and load data into databases or data warehouses.
- Data analysis and visualization: Turn collected information into actionable insights.
- Real-world case studies: Apply your knowledge to concrete projects and business challenges.
Who is it aimed at?
- Data analysts and data scientists looking to automate data collection and scale their projects.
- Software engineers and web developers who want to integrate data collection into their applications and improve efficiency.
- Digital marketing and SEO professionals who need to efficiently collect data from the web for market analysis and campaign optimization.
- Researchers and academics interested in automating data extraction for studies and trend analysis.
- Entrepreneurs and startups looking to obtain relevant market data quickly and cost-effectively for strategic decision-making.
Flexibility and applicability
Designed for professionals and Students: Concise and practical modules, exercises with real data and access to up-to-date tools and resources.
Objectives and competencies
Optimize efficiency in information acquisition:
Implement cross-checking routines for data (sensors, reports, visual observations) to minimize errors and maximize the accuracy of the information received.
Facilitating predictive analytics and informed decision-making:
Implement Machine Learning models to forecast demand, optimize routes and anticipate machinery failures, integrating the results into accessible and understandable management dashboards.
Reduce reliance on error-prone manual processes:
Implement integrated and automated data management systems, validating information with cross-checks and early warnings to ensure the integrity and reliability of the information.
Scaling up processing capacity for large volumes of data:
Implement distributed processing architectures (Spark, Hadoop) and optimize algorithms to reduce latency and increase throughput.
Improve the quality and consistency of the data collected:
Implement data validation and cleaning protocols, standardize input formats, and train staff in best practices for data capture and recording.
Integrate diverse and complex data sources:
“Normalize, validate, and transform heterogeneous data to create a unified and coherent view.”
Curriculum - Modules
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 → analytical consumption
1.4. Definition of requirements: variables, tolerances, availability SLAs, environmental conditions, and criticality
1.5. Normalization and standardization: nomenclatures, signal catalogs, units, and metadata conventions
1.6. Quality from design: acceptance criteria, validation rules, change control and traceability
2.1. Sensor selection by variable: pressure, temperature, level, current, turbidity, energy, and vibration
2.2. Acquisition electronics: ADC/DAC, signal conditioning, filters, isolation, and noise suppression
2.3. Industrial and embedded interfaces: UART, I2C, SPI, CAN, RS-485, and wiring considerations
2.4. Field protocols: Modbus, NMEA, OPC UA, MQTT, and compatibility and integration criteria
2.5. Time synchronization: NTP/PTP, time-stamping, drift and coherence in multi-sensor series
2.6. Integration tests: functional tests, range verification, initial calibration and field acceptance
3.1. Continuous Ingestion: Queues, Brokers, Buffering, Store-and-Forward, and Loss of Connectivity Tolerance
3.2. Operational ETL/ELT: Parsing, Transformation, Enrichment, and Harmonization of Units and Scales
3.3. Automatic Validation: Rules, Physical Boundaries, Cross-Consistency, and Outlier Detection
3.4. Error Handling: Retries, Record Quarantine, Alerts, and Controlled Service Degradation
3.5. Format Normalization: JSON/CSV/Parquet, Schemas, Data Contracts, and Versioning
3.6. Pipeline observability: logs, metrics, traces, latency per stage, and backlog control
4.1. Data Models: Entities, Events, Signals, Time Series, and Sizing for High Volume
4.2. Databases and Repositories: Time-Series, Relational, and Data Lakes According to Query Patterns
4.3. Metadata and Lineage: Catalog, Provenance, Quality, Changes, and Transformation Auditing
4.4. Retention and Archiving: Policies, Compression, Partitioning, Cold Storage, and Verifiable Retrieval
4.5. Access Control: Roles, Permissions, Segregation by Project/Client, and Read/Write Traceability
4.6. Integrity and consistency: constraints, checksums, reconciliation and multi-source reconciliation
5.1. Integration with operations: CMMS/EAM, SCADA, IoT platforms, and ticketing systems
5.2. Operational dashboards: KPIs, thresholds, trends, comparisons, and views by asset/location
5.3. Intelligent alarms: rules, severity, noise suppression, correlation, and escalation by SLA
5.4. Action automation: playbooks, triggers, loop closures, and intervention logging
5.5. Multichannel notifications: email, messaging, webhooks, and proof of delivery/acknowledgment
5.6. Performance evaluation: MTTA/MTTR, false alarms, monitoring coverage, and continuous improvement
6.1. Data attack surface: endpoints, gateways, credentials, APIs, and basic hardening
6.2. Communications security: encryption, authentication, key rotation, and network segmentation
6.3. Environmental robustness: corrosion, watertightness, electromagnetic interference, and preventive maintenance
6.4. Service continuity: redundancies, failover, backups, recovery testing, and contingency plans
6.5. Compliance and evidence: logs, internal audits, data policies, and operational traceability
6.6. Final applied project: system design, sensor integration, ingestion pipeline, QA/QC, dashboard and documented technical delivery
Plan de estudio - Módulos
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 → analytical consumption
1.4. Definition of requirements: variables, tolerances, availability SLAs, environmental conditions, and criticality
1.5. Normalization and standardization: nomenclatures, signal catalogs, units, and metadata conventions
1.6. Quality from design: acceptance criteria, validation rules, change control and traceability
2.1. Sensor selection by variable: pressure, temperature, level, current, turbidity, energy, and vibration
2.2. Acquisition electronics: ADC/DAC, signal conditioning, filters, isolation, and noise suppression
2.3. Industrial and embedded interfaces: UART, I2C, SPI, CAN, RS-485, and wiring considerations
2.4. Field protocols: Modbus, NMEA, OPC UA, MQTT, and compatibility and integration criteria
2.5. Time synchronization: NTP/PTP, time-stamping, drift and coherence in multi-sensor series
2.6. Integration tests: functional tests, range verification, initial calibration and field acceptance
3.1. Continuous Ingestion: Queues, Brokers, Buffering, Store-and-Forward, and Loss of Connectivity Tolerance
3.2. Operational ETL/ELT: Parsing, Transformation, Enrichment, and Harmonization of Units and Scales
3.3. Automatic Validation: Rules, Physical Boundaries, Cross-Consistency, and Outlier Detection
3.4. Error Handling: Retries, Record Quarantine, Alerts, and Controlled Service Degradation
3.5. Format Normalization: JSON/CSV/Parquet, Schemas, Data Contracts, and Versioning
3.6. Pipeline observability: logs, metrics, traces, latency per stage, and backlog control
4.1. Data Models: Entities, Events, Signals, Time Series, and Sizing for High Volume
4.2. Databases and Repositories: Time-Series, Relational, and Data Lakes According to Query Patterns
4.3. Metadata and Lineage: Catalog, Provenance, Quality, Changes, and Transformation Auditing
4.4. Retention and Archiving: Policies, Compression, Partitioning, Cold Storage, and Verifiable Retrieval
4.5. Access Control: Roles, Permissions, Segregation by Project/Client, and Read/Write Traceability
4.6. Integrity and consistency: constraints, checksums, reconciliation and multi-source reconciliation
5.1. Integration with operations: CMMS/EAM, SCADA, IoT platforms, and ticketing systems
5.2. Operational dashboards: KPIs, thresholds, trends, comparisons, and views by asset/location
5.3. Intelligent alarms: rules, severity, noise suppression, correlation, and escalation by SLA
5.4. Action automation: playbooks, triggers, loop closures, and intervention logging
5.5. Multichannel notifications: email, messaging, webhooks, and proof of delivery/acknowledgment
5.6. Performance evaluation: MTTA/MTTR, false alarms, monitoring coverage, and continuous improvement
6.1. Data attack surface: endpoints, gateways, credentials, APIs, and basic hardening
6.2. Communications security: encryption, authentication, key rotation, and network segmentation
6.3. Environmental robustness: corrosion, watertightness, electromagnetic interference, and preventive maintenance
6.4. Service continuity: redundancies, failover, backups, recovery testing, and contingency plans
6.5. Compliance and evidence: logs, internal audits, data policies, and operational traceability
6.6. Final applied project: system design, sensor integration, ingestion pipeline, QA/QC, dashboard and documented technical delivery
#VALUE!
#VALUE!
#VALUE!
#VALUE!
Career opportunities
- Data Analyst: Extracting, cleaning, and analyzing data to identify patterns and trends.
- Automation Engineer: Designing and implementing automated systems for data collection and processing.
- Data Scientist: Developing predictive models and machine learning algorithms from collected data.
- Business Intelligence (BI) Specialist: Creating dashboards and reports for data visualization and analysis, supporting strategic decision-making.
- Process Automation Consultant: Advising companies on implementing automated data collection systems to improve efficiency.
- Software Developer: Creating applications and tools for data collection, storage, and processing.
- Database Administrator: Managing and Database maintenance to ensure the integrity and availability of collected data.
Market Researcher: Use of automated data for consumer behavior analysis and identification of market opportunities.
“`
Admission requirements
Academic/professional profile:
Degree/Bachelor's degree in Nautical Science/Maritime Transport, Naval/Marine Engineering, or a related field; or proven professional experience in bridge/operations.
Language proficiency:
Recommended functional maritime English (SMCP) for simulations and technical materials.
5. Induction
Updated resume, copy of degree or seaman's book, ID card/passport, letter of motivation.
Technical requirements (for online):
Equipment with camera/microphone, stable connection, ≥ 24” monitor recommended for ECDIS/Radar-ARPA.
Admission process and dates
1. Online
application
(form + documents).
2. Academic review and interview
(profile/objectives/schedule compatibility).
3. Admission decision
(+ scholarship proposal if applicable).
4. Reservation of place
(deposit) and registration.
5. Induction
(access to campus, calendars, simulator guides).
Scholarships and grants
- Automate Your Data Pipeline: Learn to extract data from multiple web sources efficiently and without code.
- Advanced Web Scraping Techniques: Master the art of selective collection, avoiding bottlenecks and optimizing the process.
- No-Code Tools: Discover intuitive platforms to build complex data flows without programming.
- Analysis and Visualization: Transform collected data into valuable insights for strategic decision-making.
- Real-World Case Studies: Apply what you’ve learned to practical projects and get tangible results from day one.
Testimonials
I successfully automated the collection of competitor pricing data from 50 websites, reducing analysis time from 2 days to 30 minutes and eliminating manual errors, enabling the company to make faster and more strategic pricing decisions.
I applied the knowledge from the Robotics and Underwater Technology course to develop an autonomous navigation system for an ROV inspecting oil platforms, which reduced inspection time by 40% and increased the accuracy of the data collected, generating significant savings in operating costs for the company.
I implemented an automated data collection system that reduced data processing time by 80% and eliminated manual errors, allowing the analytics team to focus on data interpretation and strategic decision-making.
I implemented an automated data collection system that reduced data processing time by 60% and eliminated manual entry errors, allowing our analytics team to focus on data interpretation and strategic decision-making.
Frequently asked questions
It involves the use of technology to collect data without human intervention or with minimal intervention, allowing for greater efficiency, accuracy, and reach.
Yes. The itinerary includes ECDIS/Radar-ARPA/BRM with harbor, ocean, fog, storm, and SAR scenarios.
Online with live sessions; hybrid option for simulator/practical placements through agreements.
Data extraction from websites, forms, databases, and documents.
Recommended functional SMCP. We offer support materials for standard phraseology.
Yes, with a relevant degree or experience in maritime/port operations. The admissions interview will confirm suitability.
Optional (3–6 months) through Companies & Collaborations and the Alumni Network.
Simulator practice (rubrics), defeat plans, SOPs, checklists, micro-tests and applied TFM.
A degree from Navalis Magna University + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
#VALUE!
Request information
- Complete the Application Form
- Attach your CV/Qualifications (if you have them to hand).
- Indicate your preferred cohort (January/May/September) and whether you want the hybrid option with simulator sessions.
Teachers
Eng. Tomás Riera
Full Professor
Eng. Tomás Riera
Full Professor
Naval engineer specializing in the selection, integration, and optimization of propulsion systems for yachts, high-speed craft, and service vessels.
Eng. Sofía Marquina
Full Professor
Eng. Sofía Marquina
Full Professor
Materials engineer specializing in marine composites and corrosion protection systems for ships, yachts, and port structures.
Eng. Javier Bañuls
Full Professor
Eng. Javier Bañuls
Full Professor
Naval electrical engineer with over fifteen years of experience in the design, integration, and operation of power and automation systems.
Dr. Nuria Llobregat
Full Professor
Dr. Nuria Llobregat
Full Professor
Specialist in meteorological and operational decision-making for coastal and offshore navigation, integrating wind, wave, and current models.
Dr. Pau Ferrer
Full Professor
Dr. Pau Ferrer
Full Professor
Naval engineer with a PhD in applied hydrodynamics and over a decade of experience designing high-performance hulls and marine structures.
Cap. Javier Abaroa (MCA)
Full Professor
Cap. Javier Abaroa (MCA)
Full Professor
MCA-certified captain with command of ocean-going vessels, specializing in advanced maneuvering, navigation management, and bridge safety.