Teaching Methodologies
The classes are designed, according to the curriculum plan, to be both theoretical and practical. They are planned and prepared considering active learning activities, to actively engage all students at various moments or throughout the entire class.
In the theoretical part of the lesson, the expository method will be frequently used to introduce concepts, fundamental results, and methods, interspersed with tasks that encourage active participation by all students. These tasks include posing questions to and by students, orally and/or on a platform, as well as proposing debates/discussions in small groups on certain exposed aspects/topics.
The practical part will be designed to comprehensively develop the listed skills. This will be achieved through commented exemplification of procedures and/or problem-solving under the guidance/tutoring of the teacher. Autonomous work or work in small groups will be encouraged. There will be a strong interaction between theory and practice, with a central focus on visualizing and dealing with actual scenarios.
Learning Results
Statistical data analysis holds relevance across various business contexts, enabling description, exploration, diagnosis, and comprehension of real phenomena, crucial in guiding decision-making processes. The focus of the Data Analysis curriculum is precisely on recognizing and harnessing this potential.
The following learning outcomes are thus defined:
1. identify contexts and situations conducive to data-driven studies;
2. collect and statistically describe the dataset that supports the analysis;
3. extract insights regarding a phenomenon via exploratory analysis of the associated dataset;
4. identify and apply descriptive and exploratory statistical techniques to support tangible decision-making;
5. utilize software to implement statistical data analysis techniques effectively.
Program
1. Introduction
1.1. Data types
1.2. Sampling
2. Fundamental data analysis
2.1. Univariate descriptive analysis
2.2. Bivariate descriptive analysis
3. Complementary data analysis
3.1. Transformation and creation of new variables
3.2. Multivariate descriptive analysis
4. Data analysis of temporal data
4.1. Components of time series
4.2. Trendlines
4.3. Decomposition
Internship(s)
NAO
Bibliography
Albright, S.C,. & Winston, W.L. (2019). Business Analytics: Data Analysis and Decision Making, 7th Edition. Cengage Learning.
Alwan, L.C., Craig, B.A., & McCabe, G.P. (2020). The Practice of Statistics for Business and Economics, 5th Edition. MacMilan.
Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D., & Cochran, J.J. (2019). Statistics for Business & Economics, 4th Edition. Cengage Learning.
Curto, J.D. (2019). Potenciar os Negócios? A Estatística Dá uma Ajuda! (Muitas Aplicações em Excel e poucas fórmulas…), 3.ª Edição. Edição do Autor.
Evans, J.R. (2020). Business Analytics, 3rd Edition. Pearson.
Jones, J.S., & Goldring, J. (2022). Exploratory and Descriptive Statistics. Sage.
Murteira, B., Ribeiro, C.S., Silva, J.A., Pimenta, C., & Pimenta, F. (2023). Introdução à Estatística, 4.ª Edição. Escolar Editora.