Information Visualization Specialization

Start Date: 10/04/2020

Course Type: Specialization Course

Course Link:

About Course

This specialization provides learners with the necessary knowledge and practical skills to develop a strong foundation in information visualization and to design and develop advanced applications for visual data analysis. The specialization is characterized by two main complementary features: (1) providing a strong understanding of visual perception and the theory of visual encoding to design and evaluate innovative visualization methods; (2) providing the necessary skills to develop advanced web-based applications for visual data analysis. The specialization is organized around four courses that cover fundamentals, applied perception, advanced visualization method and interactive visualization. The specialization is meant to prepare students to work on complex data science projects that require the development of interactive visual interfaces for data analysis. The courses can also be taken individually to improve relevant skills in visualization. For instance, the course on applied perception provides unique skills to evaluate and design innovative visualization in all sorts of scenarios.

Course Syllabus

Information Visualization: Foundations
Information Visualization: Applied Perception
Information Visualization: Programming with D3.js
Information Visualization: Advanced Techniques

Coursera Plus banner featuring three learners and university partner logos

Course Introduction

Design, evaluate and develop data visualizations. Master the foundational principles and practice of information visualization Information Visualization Specialization The Information Visualization Specialization is an intensive six-week course taught by faculty from an architecture school at the University of Leeds, in which case the Specialization is also available in English. In this Specialization you will first of all become familiar with the field of Information Visualization and its various disciplines. We will move on to the theoretical background of Visualization, using it to understand our applications in programming, data visualization, and control. We will use it to design our own visualization programs, and finally to execute them. This specialization is intended to help you apply the theory and techniques you have learned to solve problems in Information Visualization. You will, therefore, get hands-on experience of a very personal kind. We will endeavor to make the course as convenient to follow as possible. We have selected videos and short PE lessons to facilitate the learning process, but there are also quizzes and a final project that is designed to supplement and confirm your mastery of the Specialization. The Information Visualization Specialization is an exciting, up-and-coming field that is developing fast. This specialization has been created with this in mind: a stable, well-funded research program, an active and vibrant community, and a strong future. This is a space for exploration, not just of theories and techniques, but also of ideas and approaches. To better meet these objectives, the Specialization has been divided into six tracks. The first track focuses on theoretical foundations of

Course Tag

Information Visualization (INFOVIS) D3.Js Data Visualization (DataViz)

Related Wiki Topic

Article Example
Information visualization Information visualization or information visualisation is the study of (interactive) visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and geographic information. However, information visualization differs from scientific visualization: "it’s infovis [information visualization] when the spatial representation is chosen, and it’s scivis [scientific visualization] when the spatial representation is given".
Information visualization Information visualization insights are being applied in areas such as:
Information visualization Information visualization presumes that "visual representations and interaction techniques take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways."
Information Visualization (journal) "Information Visualization" is abstracted and indexed in:
Information visualization The modern study of visualization started with computer graphics, which "has from its beginning been used to study scientific problems. However, in its early days the lack of graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the special issue of Computer Graphics on Visualization in "Scientific Computing". Since then there have been several conferences and workshops, co-sponsored by the IEEE Computer Society and ACM SIGGRAPH". They have been devoted to the general topics of data visualisation, information visualization and scientific visualisation, and more specific areas such as volume visualization.
Information visualization reference model The Information visualization reference model is an example of a reference model for information visualization, developed by Ed Chi in 1999., under the name of the "data state model". Chi showed that the framework successfully modeled a wide array of visualization applications and later showed that the model was functionally equivalent to the data flow model used in existing graphics toolkits such as VTK.
Visualization Library Visualization Library design is based on algorithmic and data structure specialization and separation, unlike many other 3D frameworks part of the so-called "uber scene graph" family, that is, those 3d engines that keep all the rendering information in a single hierarchical structure. Thus, Visualization Library uses different data structures (possibly hierarchical) to manage each particular domain of the rendering pipeline.
Information Visualization (journal) Information Visualization is a quarterly peer-reviewed academic journal that publishes covers the field of information science, in particular regarding theories, methodologies, techniques and evaluations of information visualization and its applications. The editor-in-chief is Chaomei Chen (Drexel University). It was established in 1998 by Palgrave Publishing, and is currently published by SAGE Publications.
Data visualization Data visualization is closely related to information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching and development. According to Post et al. (2002), it has united scientific and information visualization.
Software visualization Software visualization uses a variety of information available about software systems. Key information categories include:
Information visualization Data analysis is an indispensable part of all applied research and problem solving in industry. The most fundamental data analysis approaches are visualization (histograms, scatter plots, surface plots, tree maps, parallel coordinate plots, etc.), statistics (hypothesis test, regression, PCA, etc.), data mining (association mining, etc.), and machine learning methods (clustering, classification, decision trees, etc.). Among these approaches, information visualization, or visual data analysis, is the most reliant on the cognitive skills of human analysts, and allows the discovery of unstructured actionable insights that are limited only by human imagination and creativity. The analyst does not have to learn any sophisticated methods to be able to interpret the visualizations of the data. Information visualization is also a hypothesis generation scheme, which can be, and is typically followed by more analytical or formal analysis, such as statistical hypothesis testing.
Information visualization reference model In 1999 Stuart Card, Jock D. Mackinlay, and Ben Shneiderman present their own interpretation of this pattern, dubbing it the information visualization reference model.
Information visualization reference model According to Chi (2000), he and J.T. Reidl "in 1998 extends and proposes a new way to taxonomize information visualization techniques by using the "Data State Model". Many of the techniques share similar operating steps that can easily be reused. The Data State Model not only helps researchers understand the space of design, but also helps implementers understand how information visualization techniques can be applied more broadly".
Visualization (graphics) Information visualization concentrates on the use of computer-supported tools to explore large amount of abstract data. The term "information visualization" was originally coined by the User Interface Research Group at Xerox PARC and included Jock Mackinlay. Practical application of information visualization in computer programs involves selecting, transforming, and representing abstract data in a form that facilitates human interaction for exploration and understanding. Important aspects of information visualization are dynamics of visual representation and the interactivity. Strong techniques enable the user to modify the visualization in real-time, thus affording unparalleled perception of patterns and structural relations in the abstract data in question.
Interactive visualization More frequently, the representation of the information is changed rather than the information itself (see Visualization (graphic)).
Information visualization The field of information visualization has emerged "from research in human-computer interaction, computer science, graphics, visual design, psychology, and business methods. It is increasingly applied as a critical component in scientific research, digital libraries, data mining, financial data analysis, market studies, manufacturing production control, and drug discovery".
Data visualization KPI Library has developed the "Periodic Table of Visualization Methods," an interactive chart displaying various data visualization methods. It includes six types of data visualization methods: data, information, concept, strategy, metaphor and compound.
Scientific visualization Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways. Visual representations and interaction techniques take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. The key difference between scientific visualization and information visualization is that information visualization is often applied to data that is not generated by scientific inquiry. Some examples are graphical representations of data for business, government, news and social media.
Interactive visualization Another type of interactive visualization is collaborative visualization, in which multiple people interact with the same computer visualization to communicate their ideas to each other or to explore information cooperatively. Frequently, collaborative visualization is used when people are physically separated. Using several networked computers, the same visualization can be presented to each person simultaneously. The people then make annotations to the visualization as well as communicate via audio (i.e., telephone), video (i.e., a video-conference), or text (i.e., IRC) messages.
Data visualization There are different approaches on the scope of data visualization. One common focus is on information presentation, such as Friedman (2008) presented it. In this way Friendly (2008) presumes two main parts of data visualization: statistical graphics, and thematic cartography. In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization: