Currículo

Análise de Dados para Economia e Gestão ADEG

Contextos

ECTS

6.0 (para cálculo da média)

Objectivos

Never leaving sight of the distinction between correlation and causation effects, the analysis of economic and business data is paramount to supporting evidence-based decisions by policymakers in central and local governments, business managers, households, and consumers. This is because data analysis is crucial to understand (i) heterogeneity across groups of consumers, firms, households, regions, or countries, (ii) dynamics over time, and (iii) associations that are present in virtually all economic and business phenomena. The importance of data analysis applied to economics and management cannot be overlooked and is therefore discussed early-on at the very start of the course, serving as an anchor for the work developed throughout the semester. Goals The DAEM course has three main goals: 1. Understanding Economic and Business Concepts: the DAEM course aims to introduce students to basic data analysis and statistical techniques. Students will learn the specific vocabulary and methodological frameworks necessary for analyzing economic and business-related issues. 2. Data Analysis Skills: the DAEM course aims to equip students with the basic skills to collect, analyze, interpret, and present data relevant to economics and business. This will involve learning basic statistical methods, data visualization techniques, and how to use software tools like Excel and R for data analysis. 3. Critical Thinking and Problem-Solving: the DAEM course aims to foster critical thinking and problem-solving skills among students through (a) traditional pen-and-paper exercises and (b) modern computer-aided hands-on exercises that allow making use of large(r) amounts of data. This way, students will learn to apply economic, business and statistical concepts to real-world problems, evaluate different courses of action, and make informed decisions based on data analysis. Overall, and by achieving these goals, students will be better prepared to understand and engage with economic and business problems in their academic and professional pursuits.

Programa

Syllabus: 1. Why economic and business data analysis is important? 2. Fundamental concepts of statistical analysis and visualization of economic and business data 2.1. Probability of an event defined as its frequency of occurrence (frequentist view) 2.2. Population, sample, representative sample, and random sample (when the probability of each element of the population being selected into the sample is known to the researcher) 2.3. Statistical units of analysis, variables, and types of variables or data 2.3.1. Qualitative data (nominal and ordinal) and Quantitative data (discrete and continuous) 2.3.2. The case of continuous data grouped into (equal - vs. unequal-length) classes 2.3.3. Cross-sectional data, time-series data, longitudinal or panel data 3. Univariate data analysis and visualization (one variable) 3.1. Frequencies 3.1.1. Simple (absolute and relative) frequencies and cumulative (absolute and relative) frequencies 3.1.2. Frequency tables, bar and line plots, histograms 3.2. Location measures 3.2.1. Central tendency (mean, median, and mode) and non-central tendency (quartiles, deciles, and percentiles) location measures 3.2.2. Asymmetry and skewness (positive and negative, to the right and to the left) 3.2.3. The box-and-whisker diagram or boxplot 3.3. Dispersion measures 3.3.1. Absolute dispersion measures: range, interquartile range, standard deviation, and variance 3.3.2. Relative dispersion measures: the coefficient of variation 3.4. Concentration measures 3.4.1. Assessing how (un)equally distributed are the cumulative relative frequencies of a variable over the cumulative relative frequencies of the statistical units of analysis 3.4.2. The Lorenz curve and the Gini Index 3.5. Visualization of and measures for the analysis of time-series 3.5.1 Line plots and components of times series (trend, seasonality, cyclicality) 3.5.2. Absolute changes and rates of change 3.5.3. Index numbers, value-, quantity- and price indices 3.5.4. The deflator to transform a variable in current prices to constant prices; 4. Bivariate data analysis and visualization (two variables) 4.1. Linear and non-linear relationships between two variables and xy-scatterplots 4.2. Covariance and the linear correlation coefficient 4.3. The simple linear regression model 4.3.1. The ordinary least squares (OLS) method of fitting a linear regression to xy-data 4.3.2. Estimating the intercept and the slope of the linear regression model 4.3.3. The r-square (R2) measure of goodness-of-fit 4.3.4. Interpreting the intercept and the slope of the linear regression model and doing prediction

Método de Avaliação

In line with its main goals and expected learning outcomes, there will be two types of sessions in the DAEM course: traditional theoretical sessions plus computer-mediated laboratory sessions. The theoretical sessions will be devoted to presenting and discussing each data analysis topic listed in the program. The focus of such theoretical sessions will be on learning specific vocabulary and methodological frameworks necessary for analyzing economic and business-related issues and data, while learning how to compute several basic statistical metrics by-hand and how to interpret them. In these sessions, students will therefore develop their critical thinking and problem-solving skills by means of traditional pen-and-paper exercises. The computer-mediated laboratory sessions will follow-up on the theoretical sessions and will be essentially applied, devoted to discussing, illustrating, and allowing students to de facto conduct each data analysis topic listed in the program with the use of computer software, namely Excel and R. The focus of such applied sessions will be on learning basic practical skills to collect, analyze, interpret, and visualize economic and business-related data. In these sessions, students will therefore develop their critical thinking and problem-solving skills by means of computer-aided hands-on exercises that allow making use of large datasets. The economic and business-related problems, their framing, and the datasets used in the laboratory sessions will be adopted, and adapted, from the online ebook CORE/Doing Economics, which relies on the use of Excel and R. In the applied sessions, the use of Artificial Intelligence (AI) to program in R for the purposes of data collection, data analysis, and data visualization will also be discussed at a rather introductory-level, making sure that the ethical issues concerning the use of AI are also brought to the table. Assessment To maximize the likelihood that the evaluation throughout the semester (ALS) in the DAEM course facilitates learning and is effective from the point of view of encouraging on-going study from students, there will be four 30-minute in-class individual assignments, each one worth 25% of the final grade. Specifically, there will be two 30-minute theory-based in-class individual assignments with traditional exercises to be solved by-hand. The two theory-based in-class assignments will take place in two previously scheduled theoretical classes. In addition, there will be two 30-minute data-based in-class individual assignments with interpretation of outputs in R and an empirical analysis of an actual database to be done in R. This 2-part structure will allow to distinguish the ability of students to execute a data analysis from the ability to interpret it, while evaluating both skills. The two data-based in-class assignments will take place in two previously scheduled laboratory classes. As part of the effort to pursue the aforementioned effectiveness, the sequence of assignments throughout the semester will be the following: - TA1: Theory-based in-class Assignment 1 (25%) - DA1: Data-based in-class Assignment 1 (25%) - TA2: Theory-based in-class Assignment 2 (25%) - DA2: Data-based in-class Assignment 2 (25%)

Carga Horária

Bibliografia

Não foi definida bibliografia principal

Disciplinas de Execução

2025/2026 - 2 Semestre