Data visualization is an important issue that can arise in high-dimensional data analysis. It has become increasingly more important due to the advent of computer and graphics technology. The difficulty lies on how to visualize a high dimensional structure or data set. Such kinds of questions do have a common root in Statistics. This course will introduce some statistical methodologies useful for exploring voluminous data. The main topics include, but not limited to, two parts. The first part is based on dimension reduction methods which include Principal Component Analysis (PCA), Projection Pursuit, Sliced Inverse Regression (SIR), Principal Hessian Direction (PHD), Minimum Average Variance Estimation (MAVE) and LASSO etc. The second part is just a collection of dimension free methods which consist of Parallel Coordinate Plot, Matrix Visualization, Generalized Association Plots (GAP) etc. Most of methods will be discussed from both theoretical and practical perspective for the entire course. Examples from various application areas will be given.

Graphical Data Analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis, data mining, and network analysis. The primary focus of this course is to equip students with the necessary knowledge and skills to utilize computer software, such as Python, R, Jamovi, JASP, and Excel, to analyze and visualize data using graphical displays. By using real datasets, students will learn how graphic displays can reveal hidden patterns and trends in data that are not always apparent through traditional statistical methods. The course will cover a range of topics related to Graphical Data Analysis, including data visualization principles, statistical graphics, exploratory data analysis, and data preparation. By the end of the course, students will have a solid understanding of these concepts and will be able to apply them to a variety of real-world data analysis problems.

Wilke, C. O. (2019). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. https://clauswilke.com/dataviz/ (Free).