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El. knyga: Data Visualization: Charts, Maps, and Interactive Graphics [Taylor & Francis e-book]

(Kingston University & St George's, University of London)
  • Taylor & Francis e-book
  • Kaina: 133,87 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Standartinė kaina: 191,24 €
  • Sutaupote 30%
This is the age of data. There are more innovations and more opportunities for interesting work with data than ever before, but there is also an overwhelming amount of quantitative information being published every day. Data visualisation has become big business, because communication is the difference between success and failure, no matter how clever the analysis may have been. The ability to visualize data is now a skill in demand across business, government, NGOs and academia.

Data Visualization: Charts, Maps, and Interactive Graphics gives an overview of a wide range of techniques and challenges, while staying accessible to anyone interested in working with and understanding data.

Features:











Focusses on concepts and ways of thinking about data rather than algebra or computer code. Features 17 short chapters that can be read in one sitting. Includes chapters on big data, statistical and machine learning models, visual perception, high-dimensional data, and maps and geographic data. Contains more than 125 visualizations, most created by the author. Supported by a website with all code for creating the visualizations, further reading, datasets and practical advice on crafting the images.

Whether you are a student considering a career in data science, an analyst who wants to learn more about visualization, or the manager of a team working with data, this book will introduce you to a broad range of data visualization methods.

Cover image: Landscape of Change uses data about sea level rise, glacier volume decline, increasing global temperatures, and the increasing use of fossil fuels. These data lines compose a landscape shaped by the changing climate, a world in which we are now living. Copyright © Jill Pelto (jillpelto.com).
Section I The basics
1. Why visualise?
2. Translating numbers to images
Section II Statistical building blocks 3.Continuous and discrete numbers
4.Percentages and risks
5. Showing data or statistics
6. Differences, ratios,
correlations Section III Specific tasks 7.Visual perception and the brain
8.
Showing uncertainty
9. Time trends
10. Statistical predictive models
11.
Machine learning techniques
12. Many variables
13. Maps and networks
14.
Interactivity
15. Big data
16. Visualisation as part of a bigger package
Section IV Closing remarks17. Some overarching ideas
Robert Grant is a British statistician specialising in data visualization and Bayesian models. He worked in biomedical research and taught statistics at St George's Medical School, Kingston University, the Royal College of Physicians of London, and the National Institute for Health and Care Excellence before launching his own training and coaching business, BayesCamp, in 2017.