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Applied Economics Laboratory

Code 12119
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 and Power BI).
• 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 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 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 classification of students corresponds to the weighting of three learning moments: first test 40% (17 April) + second test 40% (8 June) +project 20% (31 May).
Language Portuguese. Tutorial support is available in English.
Last updated on: 2024-06-12

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