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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 students' classification corresponds to the weighting of three assessment moments: • First exam 20% - 21 March • Second exam 25% - 29 April • Second exam 25% - DGE Evaluation Week • Project 30% Project Delivery 20% - 15 April Presentation 10% – 17 April
Language	Portuguese. Tutorial support is available in English.