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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/
Language	Portuguese. Tutorial support is available in English.