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Artificial Intelligence and Data Science
Data Extraction and Transformation

Data Extraction and Transformation

Code	16677
Year	2
Semester	S2
ECTS Credits	6
Workload	PL(30H)/T(30H)
Scientific area	Informatics
Entry requirements	N/A
Learning outcomes	Upon successful completion of the course, students should be able to: -Know and apply methods of extracting data from various sources; - Know how to apply data processing methodologies to improve data quality and establish consistency; - Know how to identify and use relational or non-relational databases data loading technologies.
Syllabus	- Data warehouse - Crawler and data scraping; - Data parsing; - Text representation - Representation of multimedia data - Filtering, validation, and authentication of data; - Mechanisms to guarantee quality and fidelity of sources; - Protection/encryption of sensitive data in terms of privacy; - Data replication; - Data virtualization; - Integration of multi-source streams;
Main Bibliography	- Caserta, Joe, and Ralph Kimball. The Data Warehouseetl Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Wiley, 2013. - Sharda, Ramesh, Dursun Delen, and Efraim Turban. Analytics, data science, & artificial intelligence: Systems for decision support. Pearson Education Limited, 2021. - Ankam, Venkat. Big data analytics. Packt Publishing Ltd, 2016. - Furht, Borko, and Flavio Villanustre. Big data technologies and applications. Berlin, Germany: Springer, 2016. - Sedkaoui, Soraya. Data analytics and big data. John Wiley & Sons, 2018. - Leskovec, Rajaraman, Ullman. “Mining of massive datasets”. Cambridge University Press, 2014. Available at http://www.mmds.org/
Teaching Methodologies and Assessment Criteria	The contents of this course unit are presented in theoretical lectures (expository and interactive method), and its practical aspect is explored in practical laboratory classes. Each type of class has two hours of contact per week. The practical classes are guided by laboratory manuals that students execute on laboratory computers. The practical and the proposed individual and group assignments are designed so that students develop the technical abilities described in the objectives by designing and implementing labs. The theoretical and practical components are assessed using two main elements: - Labs 50% - 10 values; - Project 50% - 10 values.
Language	Portuguese. Tutorial support is available in English.