
Introduction to Information Visualization in Computing and Data Science.
Master the information visualization process by turning complex data into highly perceivable graphical representations through data preparation, encoding, and interactive viewing to yield quick, actionable insights.
Explore fundamental charts for information visualization, including bar, line, scatter, and bubble plots, and learn how to choose and use them for ordinal and quantitative data.
Explore how interaction shapes information visualization, using techniques like selection, linking, dynamic filtering, encoding, pan and zoom, and sorting, guided by Norman’s stages of action and design guidelines.
Explore how to visualize geographical data and networks and trees, using dot maps, density maps, choropleth maps, and cartograms, with geovisualization techniques.
Data is growing tremendously on daily basis and visual representation is crucial to understand this growing data. The perceptive power of the human eye makes it easy for humans to understand complex phenomena. A visual representation should be easy to understand and easy to communicate with people. Visualizations help you explore hidden information in big data and are more reliable than statistical insights. Visualization helps people plan more effectively and interactively.
Due to huge scope of visualization in data science and related fields, this course is designed for students with various backgrounds including computer science, engineering, mathematics, economics, and business schools.
Our objective is to cover all main topics in this course such as motivation and purpose of information visualization, information visualization by examples, data representation, data description, data types, dataset types, attribute types, charts, visual encoding, information visualization with color, visualization process, human perception, analysis by example, interaction, visualization pipeline and techniques, design visual user interfaces, validation in information visualization, visualizing geographical data, visualizing networks and trees.
This course helps students to able to learn the concepts of information visualization, understanding of information visualization process, application of information visualization, design of information visualization methods, and so on.
To understand sensitivity of data, effective visualization methods are essential. Therefore, I would like to urge you all students and practitioners to take this course in order to develop more effective and intuitive visualization methods.