Code: S3C4
ECTS: 6
Semester: 3rd
This workshop provides a comprehensive introduction to the essential tools and techniques for analysing, managing, mining, and visualising big data to extract meaningful insights from large datasets and communicate them effectively.
Introduction to big data characteristics, challenges, and opportunities; data acquisition, cleaning, preparation, and transformation workflows; data storage, management, and processing in large-scale data environments; structured, semi-structured, and unstructured data; exploratory data analysis and descriptive analytics; introduction to data mining techniques such as classification, clustering, regression, and association analysis; interpretation of patterns, trends, and relationships in complex datasets; visualisation principles and tools for analytical communication; dashboards, charts, and visual storytelling for decision support; project-based application of data analysis, mining, and visualisation methods to real or realistic datasets, with attention to methodological rigour and responsible data use.
- Search, analysis and synthesis of data and information, with the use of the necessary technology
- Adaptability to new situations
- Decision-making
- Working independently
- Team work
- Working in an interdisciplinary environment
- Production of new research ideas
- Project planning and management
- Showing social, professional and ethical responsibility and sensitivity to gender issues
- Criticism and self-criticism
- Production of free, creative and inductive thinking
Upon successful completion of the course, students will be able to:
Knowledge
- explain the main characteristics, challenges, and methodological issues of big data analysis and management;
- describe core approaches to data acquisition, cleaning, transformation, mining, and visualisation;
- identify appropriate analytical techniques for extracting patterns and insights from large datasets.
Skills
- manage and process data using suitable tools and workflows;
- apply selected data mining methods to identify patterns, relations, and trends in complex datasets;
- produce visual representations and data narratives that effectively communicate analytical findings.
Competences
- interpret large-scale data critically and responsibly in support of decision-making or research;
- combine computational analysis with clear visual and verbal communication of results;
- work collaboratively on data-oriented problems using structured and methodologically sound approaches.
