Visual Analytics for Data Scientists

This textbook provides an introduction to visual analytics, explaining why it is an essential part of data science methodology and practices. It covers the general principles underlying visual analytics approaches, methods, and workflows for different data types, and includes numerous examples and exercises to help students understand and apply the techniques.

Format: Paperback / softback
Length: 440 pages
Publication date: 31 August 2021
Publisher: Springer Nature Switzerland AG

This comprehensive textbook delves into the realm of visual analytics, presenting the fundamental principles and showcasing effective techniques that have proven their utility and can be easily replicated. With a special emphasis on illustrative examples, it elucidates the need for and practical applications of visualizations, providing detailed explanations of their usage.

The book begins by introducing the core concepts and principles of visual analytics, emphasizing its significance as an integral component of data science methodology and practices. It then delves into the general principles underlying visual analytics approaches, encompassing appropriate visual representation, interactive techniques, and computational methods. The text proceeds to discuss how visualizations can be utilized to gain insights into data properties and identify potential data quality issues that may hinder analysis.

The second part of the book focuses on presenting various visual analytics methods and workflows, organized by different data types such as multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images, and video. For each data type, the specific properties and challenges are explored, relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The emphasis is on demonstrating how these methods can be applied and utilized to address data-driven challenges.

While the book provides detailed explanations of the methods, it does not delve into the intricate micro-level details of their workings. Instead, it focuses on showcasing their practical applications and how they can be integrated into data analysis processes. Additionally, the book addresses the limitations of the methods and identifies potential pitfalls to avoid.

Designed primarily for students in data science, as well as professionals engaged in or aspiring to undertake practical data analysis, this textbook offers numerous examples that illustrate the practical application of visual analytics techniques. These examples serve as valuable resources for understanding the power and versatility of visualizations in data analysis.

In conclusion, this textbook serves as an invaluable resource for anyone seeking to gain a deeper understanding of visual analytics and its applications in data science. It provides a comprehensive and practical guide that covers the key principles, methods, and workflows of visual analytics, enabling readers to effectively analyze and interpret data through the power of visualizations.

Weight: 700g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030561482
Edition number: 1st ed. 2020