114年第2學期-1172 資料視覺化分析 課程資訊

評分方式

評分項目	配分比例	說明
Attendance and class paticipation	30	Students are required to attend class
Assignments	30
Final project	40

教育目標

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.

課程概述

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.

基本資料

選修課，學分數：0-3
上課時間：一/6,7,8
修課班級：共選修1-4(管院開)
修課年級：1年級以上
選課備註：全英授課，開放全校學生修習，限30人。

參考書目

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