ED2MIT, developed a series of training courses for topics related to data literacy, data management and modern platforms for digitalisation in the maritime industry to facilitate its readiness to Industry 4.0.
Technicians and VET teachers/trainers interested in Big Data and Data Management best practices and applications for maritime and offshore energy.
Access to training material
The course training material is available to freely access below. If you would like to run this course or have queries please contact the course coordinator and instructor, Yuri Demchenko, University of Amsterdam, Netherlands (email@example.com).
- Course 1: MATES Big Data
The first Big Data Course of the ED2MIT Pilot Experience was led by Yuri Demchenko from the University of Amsterdam, from 12-15 October 2020. Read more about the course here.
Access course content here.
- Course 2: Introduction to Big Data Analytics for the Maritime Sector
This course aimed to provide the necessary competences and skills for data handling in the maritime sector, including new technologies and tools used for data collection and handling (held on 19, 21, 26 & 28 January 2021). The course leaflet is available here.
Access course content here.
- Course 3: Industrial Data Spaces, Organisational Data Management & Governance for the Maritime Sector
This course aimed to outline the main concepts of Data Governance Architecture and organisational roles; develop the company’s Data Management Plan (DMP) and its implementation (held on 16, 18 & 23 February 2021). The course leaflet is available here.
Access course content here.
- Course 4: Introduction to Data Science & Analytics Foundation for the Maritime Sector
This course covered the main methods in statistical analysis, including data quality, main methods in machine learning, classification techniques and cluster analysis (ran April-May 2021). This course was self-taught with training materials provided and recommendations for further study.
Access to course content will be available soon.
ED2MIT uses proven methodology for defining targeted training curricula based on assessed skills demand and required learning outcomes, initially developed in the EDISON Project   and currently developed/adopted by the MATES project.
 EDISON Data Science Framework (EDSF) github.com/EDISONcommunity/EDSF/wiki/EDSFhome
 Yuri Demchenko, Tomasz Wiktorski, Steve Brewer, Juan José Cuadrado Gallego, EDISON Data Science Framework (EDSF) Extension to Address Transversal Skills required by Emerging Industry 4.0 Transformation, Proc. 5th IEEE STC CC Workshop on Curricula and Teaching Methods in Cloud Computing, Big Data, and Data Science (DTW2019), part of the eScience 2019 Conference, September 24 – 27, 2019, San Diego, California, USA – uazone.org/demch/papers/dtw2019-edsf4-course-transversal-v05.pdf
The following main topics for training workshops/webinars are suggested. They reflect the main competence areas as defined by DigComp 2.1 .
A. Data related competences and technologies
Data literacy is essential for future digital and data driven organisations, automation systems and Digital Twins, robotics and Artificial Intelligence powered systems. Data are used and processed at all stages of the technological process and in most cases require involvement of operators, maintainers, and developers – all of them may need to make decisions based on data provided by the processes.
- A.1. Big Data definition and technologies: 6V of Big Data and challenges for companies. Big Data examples from research and industry
- A.2. Data collection, access and sharing
- A.3. Data formats, data models, metadata
- A.4. Data storage and databases, SQL scripting and simple commands
- A.5. Data inspection, data protection, data backup and archiving
- A.6. Cloud based services and tools for data storage, sharing and management
- A.7. Open Data repositories, test datasets, developer communities
- A.8. Organisational and private Data Management, FAIR Data principles, organisational roles, Data Stewards
B. Cloud services and cloud economics
Knowledge of cloud technologies is essential for a digitally and data literate workforce as these technologies are widely used as infrastructure and remote computation platforms in modern industry automation.
- B.1. Cloud service models: IaaS, PaaS, SaaS, Apps. Use examples and Cloud Service Providers. Cost model of cloud services.
- B.2. Company IT infrastructure migrating to Cloud: benefits and problems
- B.3. Cloud and Big Data, Cloud based Big Data platform and services
- B.4. Data storing, backing up, sharing and processing in clouds (for organisational and private data)
- B.5. Practical exercises with Cloud services: Cloud management console and Cloud services deployment and access.
C. Digital content creation, access and management
This course provides essential information for the effective and safe use of digital technologies. The topics will be covered at an introductory level and guidelines for self-study will be provided.
- C.1. World Wide Web technology: protocol, HTML and webpages format, URL format and data communication via web browser.
- C.2. Web pages creation, tools, templates and services
- C.3. Domain name registration and hosting services, website and information deployment to cloud
- C.4. Active webpages and information protection, GDPR and data protection on Internet/web
- C.5. Security awareness when using general and interactive web services.
- C.6 Coordinated Vulnerability Disclosure (CVD) as part of DigComp
D. Data Science and Big Data Analytics
This course is provided as a general overview of the listed topics below. More in-depth training and learning will require more time commitment and pre-requisite knowledge.
- D.1. Statistical methods and probability theory
- D.2. Data description and Statistical Data Analysis
- D.3. Data preparation: data loading, data cleaning, data pre-processing, parsing, transforming, merging, and storing data
- D.4. Qualitative and Quantitative data analysis
- D.5. Classification: methods and algorithms
- D.6. Cluster analysis basics and algorithms
- D.7. Performance of data analytics algorithms and tools
- D.8. Visualisations of data analysis and dashboards
- D.9. Organising data analytics process following CRISP-DM and Data Science Process
 DigComp 2.1, 2017, The Digital Competence Framework for Citizens, by Stephanie Carretero, Riina Vuorikari and Yves Punie, Joint Research Center, 2017, EUR 28558 EN [online] ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/digcomp-21-digital-competence-framework-citizens-eight-proficiency-levels-and-examples-use
The courses are provided at the EQF levels 2-3 (awareness, simple tasks and guided regular tasks). Advance courses are at EQF levels 3-4 (simple tasks, guided regular tasks, and independent regular tasks).
Figure 1: EQF levels compared with achieved education and maintenance personnel positions .
 Application of European Qualification Framework (EQF) in Maintenance. Magazine for maintenance & asset management professionals: maintworld.com
- Course coordinator and instructor: Yuri Demchenko (University of Amsterdam, Netherlands)
- Course instructor: Adam Belloum (University of Amsterdam, Netherlands)
The MATES Strategy Baseline report consists of results which were obtained from the extensive work carried out by the MATES partners; workshops with experts, Delphi questionnaires, desk-top studies and surveys. This report synthesises the MATES strategy baseline to bridge the skills gap between training offers and the industry demands in the Maritime Technologies value chain. The full report can be accessed here. Below are the Lines of Actions identified in the report (see pages 17 and 18) which ED2MIT will address (SB = shipbuilding and ORE = offshore renewable energies):
- SB1: Digital and data driven technologies
- SB2: Automation and robotics
- SB6: 21st century skills
- SB8: Promoting women
- ORE1: New digital technologies
- ORE2: Energy storage
- ORE10: 21st century skills
- ORE12: Promoting STEM women in ORE