DATA ANALYSIS FOR SOCIAL SCIENCES

Description

Objectives: Statistical analysis of univariate and multivariate data with particular focus on social and economic applications. Assessment: Two written midterm tests (1/3), a group project (1/3), and an oral exam (1/3). Non-attending students (non-compliant and exempt) will take a written exam (1/3) instead of the midterms. Teaching Methods: Book, slides, lecture notes, R scripts Prerequisites: Basic concepts of Mathematics Contents: Basic concepts of statistics Different types of data Exploratory data analysis Linear regression model Generalized Linear Models Network analysis Natural Language Processing Scripting in R Reference Texts: Suggested: C. Chapman and E. McDonnell Feit (2015) R for Marketing Research and Analytics, Springer. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Lecturer’s slides Thesis assignment criteria: TBD Extended Program And Reference Reading Material: Week 1: Introduction to statistics Different types of data: - quantitative: numeric, continuous, discrete - qualitative (or categorical) - textual data Extended Program And Reference Reading Material: Week 10: Network analysis: - Introduction to graph theory and summary statistic - Networks in R Extended Program And Reference Reading Material: Week 11: Natural Language Processing: - Introduction to text analysis - Textual data in R Extended Program And Reference Reading Material: Week 12: Recap of the topics and excercises. Extended Program And Reference Reading Material: Week 2: Introduction to R: interface and basic data processing - Basic summary statistics: min, mean, mode, quantiles, max, variance, standard deviation, coefficient of variation, correlation, covariance, etc. - Summary statistics in R Extended Program And Reference Reading Material: Week 3: Exploratory data analysis: - Basic summary statistics: min, mean, mode, quantiles, max, variance, standard deviation, coefficient of variation, correlation, covariance, etc. - Summary statistics in R - Data visualization: barplot, histograms, maps, pie chart, boxplot, etc. - Data visualization in R with ggplot2 - Main probability distributions: Gaussian, Bernoulli, Binomial, Poisson - Probability distributions in R Extended Program And Reference Reading Material: Week 4: Exploratory data analysis: - Basic summary statistics: min, mean, mode, quantiles, max, variance, standard deviation, coefficient of variation, correlation, covariance, etc. - Summary statistics in R - Data visualization: barplot, histograms, maps, pie chart, boxplot, etc. - Data visualization in R - Main probability distributions: Gaussian, Bernoulli, Binomial, Poisson - Probability distributions in R Extended Program And Reference Reading Material: Week 5: Linear regression model - Recap of statistical inference - Linear regression model - OLS method - Parameters' interpretation and model assessment - Linear regression in R Extended Program And Reference Reading Material: Week 6: Linear regression model - Recap of statistical inference - Linear regression model - OLS method - Parameters' interpretation and model assessment - Linear regression in R Extended Program And Reference Reading Material: Week 7: Generalized Linear Model - Model formulation - Estimation method - Parameters' interpretation and model assessment - GLM in R Extended Program And Reference Reading Material: Week 8: Generalized Linear Model - Model formulation - Estimation method - Parameters' interpretation and model assessment - GLM in R Extended Program And Reference Reading Material: Week 9: Network analysis: - Introduction to graph theory and summary statistic - Networks in R Intended learning outcomes: The student will be able to analyze different data using appropriate statistical methodologies. Data analysis will be done with R, that is an essential part of this course.