Advisory Prerequisites: Experience with elementary statistics and statistical analysis software, ideally R
Description: This course introduces students to the foundational principles and practical skills of data visualization, with a focus on both exploratory data analysis and enhancing the clarity and impact of statistical communication. The course emphasizes visualization as a critical tool for statistical reasoning, model interpretation, and scientific communication. Students will learn to design and evaluate visualizations that are accurate, clear, and audience-appropriate. Topics include using visualization for exploratory data analysis, common pitfalls in data presentation, best practices for visual storytelling, and
the role of graphics in conveying complex analytical insights.
Hands-on exercises will focus primarily on R, using ggplot2 and the tidyverse, with supplementary coverage of Python, Stata, and tools for interactive dashboards.
Learning Objectives: -Conceptualize and implement effective data visualizations using R and ggplot2; Learn common exploratory data analysis visualizations techniques that helps analysts understand their data and its structure; Select appropriate visualization techniques based on data type, analytical goals, and audience needs; Critically evaluate data visualizations for effectiveness and accuracy; Communicate complex data insights through clear and impactful storytelling; Incorporate reproducibility and transparency into the data visualization process using modern coding practices; Address common challenges and pitfalls in data visualization, including choosing incorrect statistical transformations, misleading uses of scale and range, and poor design; Integrate visualizations into workflows for statistical modeling and applied data science.
