Visualization

CLASS INFO

• Semester: Fall, 2025
• Class time/ Class room: / Mirae Building
• Text book:
• Course Description: Visualization is a means of communicating the statistical properties of data effectively. This course introduces statistical methods for summarizing and communicating properties of high-dimensional structured data. In the first half, students learn statistical visualization tools and programming methods for creating visualizations. In the second half, they learn to represent high-dimensional data in lower-dimensional spaces and visualize it. Topics include nonlinear dimensionality reduction and methods for learning representations in latent spaces. The course is intended for third- and fourth-year undergraduates and requires prior knowledge of Python fundamentals and linear (matrix) algebra.

CONTENTS

• [Week 1] History of visualization
  • Course materials (link)
• [Week 2] Visualizing statistics
  • Course materials (link)
  • Practice: visualization programming fundamentals (link)
  • Practice: GGPLOT2 in Python (link)
• [Week 3] Visualizing data analysis results
  • Course materials (link)
  • Practice link (link)
• [Week 4] Visualizing spatial information
• [Week 5] Server and DB
  • Course materials (link)
• [Week 6] Data analysis on the inner product space
  • Course materials (link)
  • Practice: dimensionality reduction and functional data analysis (link)
• [Week 7] PCA and ICA
  • PCA (link)
  • PCA practice link (link)
• [Week 8] Midterm exam
• [Week 9] Factor Analysis
  • Factor Analysis (link)
  • Practice: factor analysis and visualization of iris data (link)
• [Week 10] Multi-dimensional Scaling
  • MDS (link)
  • Practice: factor analysis and visualization of iris data (link)
• [Week 11] Reproducing Kernel Hilber Space
• [Week 12] Functional PCA
• [Week 13] T-Stochastic Neighbor Embedding
  • T-Stochastic Neighbor Embedding (link)
  • Practice: fitting and comparing SNE and t-SNE (link)