Dr. Marina AlcoverAcademic director focused on measurable results and operational excellence in maritime education. Integrates IMO/STCW regulations and ISO 21001/9001 f
Courses · 300 h · 5 modules
Automated Data Collection Course
The Automated Data Collection courseThis 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 coll
Duration300 h
FormatOnline
Starts21 December
Places20 / 20
Prior knowledge
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Programme overview
What is the Automated Data Collection 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.
Programme type
Course
Study mode
Online
Duration
300 hours
Modules
5
Programme highlights
- 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.
- Increase Boost the efficiency of your business with Automated Data Collection and gain a competitive edge.
Curriculum
Programme Structure
The programme is structured in 5 modules, listed below in order of study.
01
Fundamentals of automated data capture, acquisition architectures and end-to-end flow design
- Key concepts of automated acquisition: Sampling, frequency, resolution, latency and operational continuity
- Typical architectures: Edge, gateway, cloud and hybrid models for maritime, port and industrial operations
- Data Flow Design: Source → Acquisition → Validation → Storage → Analytical Consumption
- Definition of requirements: variables, tolerances, availability SLA, environmental conditions and criticality
- Standardization and standardisation: nomenclatures, signal catalogues, units and metadata conventions
02
Instrumentation, sensors, interfaces and protocols for automated collection
- Selection of sensors per variable: pressure, temperature, level, currents, turbidity, energy and vibration
- Acquisition electronics: ADC/DAC, signal conditioning, filters, isolation and noise suppression
- Industrial and embedded interfaces: UART, I2C, SPI, CAN, RS-485 and cabling considerations
- Field Protocols: Modbus, NMEA, OPC UA, MQTT and Compatibility and Integration Criteria
- Temporary Sync: NTP/PTP, Time-Stamping, Drift and Multi-Sensor Series Coherence
03
Intake automation, ETL/ELT and real-time data validation
- Continuous Intake: Queues, Brokers, Buffering, Store-and-forward and Connectivity Loss Tolerance
- Operational ETL/ELT: Parsing, transformation, enrichment and harmonization of units and scales
- Automatic validation: rules, physical limits, cross-coherence, and outlier detection
- Error Management: Retries, Registry Quarantine, Alerts, and Controlled Degradation of the Service
- Format normalization: JSON/CSV/parquet, schemas, data contracts and versioning
04
Data storage, modelling and governance for time series and georeferenced data
- Data Models: Entities, Events, Signals, Time Series and High Volume Dimensioning
- Databases and repositories: Time-Series, Relationals and Data Lake by query patterns
- Metadata and lineage: catalogue, origin, quality, changes and audit of transformations
- Retention and Archive: Policies, Compression, Partitioning, Cold Storage, and Verifiable Recovery
- Access control: Roles, permissions, project/client segregation, and read/write traceability
05
Cybersecurity, field robustness and automated collection applied project
- Data Attack Surface: Endpoints, Gateways, Credentials, APIs and Basic Hardening
- Communications security: encryption, authentication, key rotation, and network segmentation
- Environmental robustness: corrosion, tightness, electromagnetic interference and preventive maintenance
- Service continuity: redundancies, failover, backups, recovery tests and contingency plans
- Compliance and Evidence: Records, Internal Audits, Data Policies, and Operational Traceability
Workload
Study Time and Workload
300 hours of study, online and at your own pace. Move the slider to the hours a week you can really put in.
Hours a week8
Weeks to finish—
You finish around—
Who it is for
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
Faculty
Academic direction and faculty
The teaching team at Navalis: working professionals who deliver and oversee the institute's programmes.
Eng. Diego Cifuentes
Cap. Javier Abaroa (MCA)
Dr. Pau Ferrer
Dr. Nuria Llobregat
Eng. Javier Bañuls
Eng. Sofía Marquina
Eng. Tomás Riera
Verified reviews
Student Reviews
Reviews collected and verified by Opynio, not written by us.
Outcomes
Learning Outcomes
- 01Optimize efficiency in information acquisition
- 02Facilitating predictive analytics and informed decision-making
- 03Reduce reliance on error-prone manual processes
- 04Scaling up processing capacity for large volumes of data
- 05Improve the quality and consistency of the data collected
- 06Integrate diverse and complex data sources
Careers
Career Opportunities
01Data Analyst: Extracting, cleaning, and analyzing data to identify patterns and trends.
02Automation Engineer: Designing and implementing automated systems for data collection and processing.
03Data Scientist: Developing predictive models and machine learning algorithms from collected data.
04Business Intelligence (BI) Specialist: Creating dashboards and reports for data visualization and analysis, supporting strategic decision-making.
05Process Automation Consultant: Advising companies on implementing automated data collection systems to improve efficiency.
06Software Developer: Creating applications and tools for data collection, storage, and processing.
07Database Administrator: Managing and Database maintenance to ensure the integrity and availability of collected data.
08Market Researcher: Use of automated data for consumer behavior analysis and identification of market opportunities.
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Questions
Entry Requirements
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 Institute + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
Online admissions
Admissions Process
No waiting for a phone call. If your documents are complete, you can pay in the same session.
Entry requirements
Academic/Professional Profile:Basic programming knowledge (Python, R, or another language).Familiarity with databases and APIs.Technical Competence:Understanding of data structures (JSON, XML).Ability to solve problems and debug code.Documentation:Updated CV highlighting experience in automation or data analysis projects.Optional: Portfolio with sample scripts or data collection projects.Technical Requirements (for online):Equipment with access to Programming software, stable internet connection.Basic knowledge of terminal operation (CLI).```
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Frequently Asked Questions
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