The course focuses on applying advanced data science techniques to extract, analyze, and utilize knowledge from web-based information. Students will delve into semantic search and information retrieval methods to organize and enhance access to large datasets. The course will introduce retrieval-augmented knowledge systems and crowdsourcing as tools to enrich and validate data. Through practical use cases, participants will apply data science and visualization techniques to uncover patterns and insights from complex web data. By the end of the course, students will be equipped to design data-driven solutions for challenges in web and information science.

Knowledge and Understanding (Dublin Descriptor 1)
Students will gain in-depth knowledge of web and data science techniques, including information retrieval (e.g., link-based and semantic search), web-scale data analytics, and crowdsourcing methods. They will also understand the full data science and engineering pipeline—from problem formulation and business requirements to implementation and delivery of results—along with the principles behind retrieval-augmented systems, vector search, and semantic technologies.

Applying Knowledge and Understanding (Dublin Descriptor 2)
Students will apply their knowledge by defining real-world data problems and translating them into concrete technical requirements. They will design, develop, and evaluate prototype systems (demonstrator prototypes) that solve these problems using appropriate tools and methods. This includes structuring projects into clear phases, making and justifying design decisions, and iteratively refining their solutions.

Making Judgements (Dublin Descriptor 3)
Students will learn to critically assess data challenges by identifying goals, modeling assumptions and constraints, estimating required resources, and evaluating potential risks. They will also consider economic and strategic aspects, such as drafting basic business plans and identifying viable use cases for their technical solutions.

Communication (Dublin Descriptor 4)
Throughout the course, students will work in teams and develop their ability to communicate effectively in both written and oral forms. They will deliver technical presentations, engage in peer discussions, and present their final project as a persuasive pitch tailored to a potential investor or stakeholder, refining both individual and group communication skills.

Lifelong Learning Skills (Dublin Descriptor 5)
By engaging in open-ended, hands-on projects and staying current with evolving web and data technologies, students will cultivate the ability to learn autonomously. The course encourages reflective thinking, adaptability, and continuous development of both technical and soft skills—essential for future professional growth in dynamic data-centric fields.

This course offers a comprehensive journey through modern data science and engineering pipelines, with a strong focus on web data and real-world applications. Students will explore the end-to-end process from business needs to technical implementation, covering key areas such as:

• Data Pipelines for Big Data: Design and implementation of scalable data pipelines—from data ingestion to result delivery—based on business requirements and technical specifications.

• Information Retrieval: Foundations of web search engines, including indexing, ranking, and evaluation techniques, with hands-on experience using tools like ElasticSearch.

• Vectorization and Vector Databases: Core principles of knowledge vectorization, vector search technologies, vector databases, and feature stores.

• Web Scraping and APIs: Practical techniques for extracting data from websites and APIs, with an emphasis on ethical and scalable data access methods.

• Semantic Technologies: Enhancing data interpretation and interoperability using ontologies, knowledge graphs, and semantic agents in line with the Semantic Web vision.

• Retrieval-Augmented Knowledge Systems: Integration of external knowledge into AI models through methods like Retrieval-Augmented Generation (RAG) and the use of intelligent agents.

• Crowdsourcing in Data Science: Leveraging the crowd for data collection, enrichment, validation, and quality control in scalable, human-in-the-loop workflows.

• Data Communication: Tools and methods for effectively visualizing and presenting insights derived from web data.

• Web-Scale Data Science Applications: Practical use cases involving pre-processing, transformation, enrichment, and analysis of large-scale web data using tools such as Python, pandas, Apache Spark, and large language models. Topics include recommender systems, social media analytics, market intelligence, and personalized content feeds.

• Ethics of Data Science: Critical examination of the ethical, legal, and social implications of data-driven systems, including privacy, bias, transparency, and accountability in large-scale web and AI applications.

• SDG4 - QUALITY EDUCATION
• SDG8 - DECENT WORK AND ECONOMIC GROWTH
• SDG9 - INDUSTRY, INNOVATION AND INFRASTRUCTURE

Students are expected to have a basic understanding of data structures and algorithms, including experience with databases and SQL. Basic knowledge of Python and statistics is advised. Prior coursework or experience in machine learning or data analysis is recommended but not mandatory.

The assessment for this course will be a combination of theoretical understanding and practical application, aligned with the Dublin Descriptors for higher education learning outcomes. Students will complete a written exam to demonstrate their knowledge and understanding of key concepts, principles, and methodologies in web and data science (DD1). Through this, they will also show their ability to apply knowledge and understanding to problem-solving scenarios and case-based questions (DD2).

In addition, students will have the opportunity to undertake an optional project, where they will apply course techniques to a real-world use case—designing and implementing data-driven solutions. This component assesses their capacity for making informed judgments based on data and technical evidence (DD3), as well as their communication skills in presenting analytical results effectively through written and visual media (DD4).

Finally, through reflection and project work, students are encouraged to demonstrate learning skills that enable autonomous study and professional development in data-intensive environments (DD5).