Original link: http://vis.pku.edu.cn/blog/datavis2022/
In the face of surging data in the era of big data, how to analyze data intuitively and efficiently, gain insight into subtleties, and make decisions thousands of miles away has become the key to grasping opportunities. Visualization and visual analysis technology can effectively combine human cognitive ability and machine computing ability, and are the bridge and link between users and data.
In the course, you will understand the basic principles, key technologies and methodological tools of visualization and visual analysis, understand the latest research progress in the field of visualization, and have the opportunity to try open visualization tasks and design visual analysis solutions for target data. and implement it.
The course is comprehensively examined based on course design results and classroom participation. The course is aimed at graduate students of all majors who are interested in the research and application of visual chemistry, and who are willing to learn basic visual programming and use visual tools.
Course number : 04804013 (3 credits)
Class time : 10-12 classes every Wednesday
Yuan Xiaoru Researcher
School of Intelligence, Key Laboratory of Machine Perception and Intelligence, Ministry of Education, Visualization and Visual Analysis Laboratory, Peking University
Courses are taught using a learning paradigm that emphasizes communication, initiative, and creativity. Through this course, you will discuss and communicate with students from different professional backgrounds, experience the collision of thinking from different perspectives, and complete visualization works together with team members in the course design project. This course will improve your ability to study and research, and develop the thinking and ability to actively explore open-ended visualization tasks through active discussions and exchanges in the classroom.
This course will be evaluated based on a combination of course participation, daily homework, thesis reading, and course design results. Students who actively participate in discussions and have innovative ideas will be rewarded with grades. The course is designed for groups to work together to complete visual works and make a defense report.
We are open to graduate students of all majors who are interested in the research and application of visual chemistry, have a certain programming foundation or can flexibly use visualization tools, regardless of major or department background.
Course design for previous years
More visualization cases
Grand Canal Stream Domain Pedestrian Trail
With the help of the interactive timeline and map, users can watch the overview view of the movement of ancient celebrities in the Grand Canal Basin, and can also focus on the specific trajectories of celebrities who have passed through the canal, so as to understand the cultural chapters written by the Millennium Canal.
The Epic of People Migration in the Grand Canal Basin—China Grand Canal Theme Art Exhibition Zhejiang Art Museum 2022/8/11 – 10/11
Peking University’s achievements in archaeological discoveries
As an extension of the curriculum design, this project shows the distribution of various achievements of Peking University’s archaeological discoveries in time, space and type, as well as the cooperation with archaeological units at all levels across the country.
Visualization of Peking University’s Archaeological Discovery Achievements—100 Years of Archaeology at Peking University Sackler Museum, 2022/5
Fanghan Manifold – Rheology
By means of repeated character browsing and context comparison, this paper analyzes the changes in Wang Xizhi’s calligraphic style and structure, and explores the evolution of inscriptions on inscriptions and the similarities and differences of different brush styles.
Fanghan Sea Manifold—Second Prize Work in the Ancient Book Track of ChinaVis 2022 Visualization Competition
Students who are interested in visualization-related work, are interested in the research and application of visualization, or want to use visualization in their own study and work are welcome to take courses.
Course homepage: http://vis.pku.edu.cn/course/datavis_f22/ .
If you have any questions, please consult xiaoru.yuan[at]pku.edu.cn.
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