Data Analysis and Statistics for Social Scientists in R

How do we use data to address critical social, economic, and policy challenges? This comprehensive text-based course bridges the gap between statistical theory and social science applications, helping you make sense of complex human behavior through data. You will transition from understanding basic probability to executing sophisticated empirical analyses. By studying clear written explanations, analyzing code examples, and completing structured exercises, you will gain the skills to model relationships, evaluate policy impacts, and draw reliable conclusions from social data. What you'll learn: - Understand core principles of probability and statistical inference for social science. - Apply linear regression and econometric techniques to identify relationships between variables. - Design and analyze randomized control trials and A/B tests to measure policy effectiveness. - Utilize the R programming language and modern tidyverse packages for data manipulation and visualization. - Evaluate causal claims and recognize the limitations of observational data. - Implement foundational machine learning concepts for predictive modeling in social contexts. The course begins with foundational definitions of probability and descriptive statistics before guiding you through regression analysis, experimental design, and modern computational tools. You will read through practical case studies and examine written code snippets that demonstrate how to clean, analyze, and interpret real-world datasets. This course is designed for beginners, requiring no prior background in statistics or programming. Start your journey into social data science today and unlock the power of evidence-based analysis.