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Tools for Economics

Tools for Economics

Code	16513
Year	1
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 Wickman, Mine Cetinkaya-Rundel and Garret Grolemund, R for Data Science, 2 nd edition, 2023 Aspin, Adam, Pro Power BI Desktop, Apress, 3rd edition, 2020 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, 1st edition, 2013
Teaching Methodologies and Assessment Criteria	Learning is problem-based. Guided activities are carried out with the aim of preparing students to solve real-world issues. Real data and software are used to answer the problems. The teaching-learning classification is continuous and corresponds to the weighting of the following assessment moments: - first session 30% - March 22nd - Second test 40% - DGE Assessment Week - Work 30% - May 22nd
Language	Portuguese. Tutorial support is available in English.