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Laboratory of Applied Economics

Laboratory of Applied Economics

Code	14812
Year	3
Semester	S2
ECTS Credits	6
Workload	TP(60H)
Scientific area	Economics
Entry requirements	Not applicable.
Mode of delivery	Face-to-face
Work placements	Not applicable
Learning outcomes	• To train students in the advanced use of spreadsheet for presentation and transformation of data. • To empower students for the manipulation of large volumes of data. • To empower students to use data analysis and visualization software (Excel, Power BI and R). • To empower students with the clear and effective presentation of data driven insights.
Syllabus	1 - Data types and structure 2 - Introduction to databases 3 - Data collection 4 - Data preparation 5 - Data transformation 6 - Data visualization 7 - Extraction of data information
Main Bibliography	Hadley Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund, R for Data Science (2e), 2023 Aspin, Adam, Pro Power BI Desktop, Apress, 2nd edition, 2018 Guerrero, Hector,. Excel Data Analysis: modeling and simulation, Spinger, 2nd edition, 2019. Provost, Foster e Fawcett, Tom,. Data Science for Business: What you need to know about data mining and data-analytic thinking, 1str edition, 2013
Teaching Methodologies and Assessment Criteria	The classes are theoretical and practical. In the first part of the class are exposed the theoretical concepts and analysis techniques. In the second part the techniques and concepts are applied with practical exercises carried out using the software. The students' classification corresponds to the weighting of three assessment moments: • First frequency 40% - 17 April • Second frequency 40% - DGE Evaluation Week • Work 20% - 31 May
Language	Portuguese. Tutorial support is available in English.