| Instructor: | Professor Cleveland, William |
|---|---|
| Office: | Haas 222 |
| E-Mail: | wsc AT purdue DOT edu |
| Meeting with Instructor: | Anytime, including evenings. Send email. |
The Critical Role of Visualization in the Statistical Analysis of Data
What are the areas that must be considered in visualization?
Visualization for Large, Complex Data Sets
Prerequisites: Knowledge of basic probability, mathematics through calculus and linear algebra, and basic statistics including least-squares fitting of parametric functions to data. No previous knowledge of data visualization is needed.
Credits: 3
Primary Audience: Graduate students in university departments where data are analyzed.
Description: All areas of learning from data -- statistics, machine learning, and data mining -- can benefit immensely from data visualization. Graphs allow us to explore data to see overall patterns and to see detailed behavior; no other approach can compete in revealing the structure of data so thoroughly. Graphs allow us to view complex mathematical models fitted to data, and they allow us to assess the validity of such models. But realizing the potential of data visualization requires methods and basic principles. The course is about methods and basic principles that help the data analyst to realize the potential of visualization. The material is divided into principles of graph construction, graphical methods, and graphical perception.
Texts: Each student will receive two books of the instructor, The Elements of Graphing Data and Visualizing Data, from which some of the material in the course will be taken. Other material will come from research papers or from new ideas and concepts documented in viewgraphs.