This course aims to provide basic knowledge and hand-on experience on Data Science & Analytics basis, methods, technologies, tools and best practices, which are considered as key factors in digital transformation for the enterprises of the future.
Provide a general overview of the necessary competences and skills for data handling in the maritime sector. Reviewing the best practices in teaching Big Data technologies for Data Science & Analytics, discussion on specific tasks and requirements for maritime sector. Learning about new technologies and tools used for data collection & handling.
Technicians and VET teachers/trainers interested in Big Data and Data Management best practices and applications for maritime and offshore energy sectors. MATES partners and MATES TG experts. Women will be prioritized.
Data Science & Analytics: principles, methods and approaches. CRISP-DM and organisation of data analytics process
Statistical analysis methods
Data preparation and pre-processing
Machine Learning & Classification techniques
Machine Learning & Cluster analysis
Each of 5 sessions will include lectures, practice and interactive discussions. Practice will include working with Data Analytics tools for data preparation, analysis and reporting, using provided datasets.
Online course has limited capacity: 30 attendees.
At registration, please indicate your prerequisite conditions. If registration numbers exceeds the maximum number of attendees, a waiting list will be created, Pre-requisite conditions may apply.
Attendees should have
- Basic knowledge of computer systems and Internet applications.
- Familiarity with Python programming language.
- Basic knowledge of statistical methods.
Proceso de aplicación
Deadline for registration: 26th February 2021
Resultados del aprendizaje
- Understand the basic concepts and approaches in Data Science and Analytics, data analytics process and stages.
- Understand main methods in statistical analysis, data exploration and data preparation.
- Understand main methods in machine learning, classification techniques and cluster analysis.
Categorías CINE (ISCED)