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Information Visualization From data fundamentals and visual encoding to complex relational and time-series visualizations [#1009575]
Free Download Information Visualization
by Sougata Mukherjea;
English | 2026 | ISBN: 9365891124 | 312 pages | True EPUB | 36.05 MB
Data and information visualization has become the single most vital skill for understanding big data and driving effective decision-making. The amount and complexity of information produced in science, engineering, business, and everyday human activity are increasing at staggering rates and visualization helps people understand and analyze information.
This textbook offers a complete overview of visualization techniques in sequential form to visualize the different types of data, like tables, graphs, trees, and text. We will explore how visualization goes beyond presenting information by leveraging the power of computer interaction to help people analyze, understand, and make decisions from data. Moreover, we will describe some fundamental principles of creating aesthetic visualization. The final section on implementation includes a practical guide to the best tools and libraries (like D3.js) and the design principles for creating compelling dashboards, concluding with the emerging intersection of AI and visualization and visual analytics for deep learning.
By the end of this book, you will transition from a data novice to a visualization expert, capable of applying advanced network and multidimensional techniques. You will be fully equipped to handle any data structure and leverage AI for better insights, giving you a competitive edge in data science and visual analytics roles.
What you will learn
● Learn data types, visual encoding, and perceptual mapping principles for clarity.
● Understand how to design and implement information visualizations.
● Know how information visualizations use dynamic interaction methods.
● Develop skills in critiquing different visualization techniques in the context of user goals and objectives.
● Learn how ML techniques can be utilized in visualization.
● Learn how visual analytics can help in interpreting deep neural networks.
● Apply algorithms for tree and graph visualization using node-link layouts.
● Select appropriate vis libraries (e.g., D3.js) and adhere to perceptual design rules.
Who this book is for
The book is for anyone who wants to gain insights from data using visualization techniques - students, academicians, data scientists, software developers, etc. A basic knowledge of software is all that is required to gain insights from the book.
Table of Contents
1. Introduction
2. Data
3. Visual Encoding
4. Visualizing Tables
5. Visualizing Multi-dimensional Data
6. Interactions
7. Visualizing Time
8. Visualizing Trees
9. Visualizing Graphs
10. Graph Tree Interactions
11. Visualizing Text
12. Implementation
13. Design Principles
14. Visual Analytics for Deep Learning
15. AI Techniques for Visualization
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