1Chapter 1. Introduction
58Chapter 6. Getting started with Processing and Mapping
21.1 What is data visualization?
596.1 Introduction to processing
31.2 Real-life examples of data visualization
606.2 Exporting and Distributing Your Work
41.3 Importance of data visualization
616.3 More About the size( ) Method
51.4 Advantages of data visualization
626.4 Loading and Displaying Data
61.5 Importance of data visualization for career
636.5 Functions
71.6 Who Uses Data Visualization?
646.6 Libraries Add New Features
81.7 The data visualization process
656.7 Sketching and Scripting
91.8 Data types, relationships, and visualization
666.8 Processing tutorial
10formats
676.9 Formatting data
111.9 Data relationships
686.10 Mapping
121.10 Formats
696.11 Data on a Map
131.11 Chapter Summary
706.13 Two-Sided Data Ranges
141.12 Exercise
716.14 Updating Values over Time (Acquire, Mine)
15Chapter 2. History of Data Visualization
726.15 Smooth Interpolation of Values over
162.1 Introduction
73Time (Refine)
172.2 Early history: Before 16th Century
746.16 Summary
182.3 Effect of the invention of paper and
756.17 Exercise
19parchment on data visualization
76Chapter 7. Time Series
202.4 Data Visualization post 16th century
777.1 Time series visualization and analytics
212.5 Data Visualization in 18th Century
787.2 Time series chart
222.6 Data Visualization in late 19th Century
797.3 Using a dashboard for visualizing time
232.7 Statistical Historiography
80series data
242.8 Summary
817.4 Tools for graphing time-series data
252.9 Exercise
827.5 Grafana
26Chapter 3. The Art and Science of Data Visualization
837.6 Time series custom graphs
273.1.The Theory of Data Visualizations
847.7 Building custom graphs using Dygraphs
283.2.The Mantras
85Charting Library
293.3. Use the right tool for the job.
867.8 Time series chart with anomaly detection
303.4 A Quick Tangent
87visualization
313.5. Making Excellent Visualizations
887.9 Time series chart with a prediction or
323.6. Dealing with chartjunk
89trend line
333.7 Common Mistakes
907.10 Summary
343.8 Over-complex visualizations
917.11 Exercise
353.9 Basic principles for data visualization
92Chapter 8. Integrating Processing with Java11
363.10 Practical Advice
938.1 Programming Modes
373.11 Summary
948.2 Additional Source Files (Tabs)
383.12 Exercise
958.3 Using .java Source Files
39Chapter 4. Data Visualization Through Their Graph Representations
968.4 The Preprocessor
404.1 Introduction
978.5 API Structure
414.2 Data and Graphs
988.6 Embedding PApplet into Java Applications
424.3 Graph Layout Techniques
998.7 Using Libraries
434.4 Force-directed Techniques
1008.7 Summary
444.5 Multidimensional Scaling
1018.8 Exercise
454.6 The Pulling Under Constraints Model
102Chapter 9. More on Data Visualizations
464.7 Bipartite Graphs
1039.1 Differences Between Data Science vs. Data
474.8 Summary
104Visualization
484.9 Exercise
1059.2 Data Visualization and big data analysis
49Chapter 5. Data Visualization tools
1069.3 Big Data Visualization
505.1 What Are Data Visualization Tools?
1079.4 Importance Of Big Data Visualization
515.2 What Do the Best Data Visualization Tools
1089.5 How Data Visualization Works
52Have in Common?
1099.6 Is Big Data Visualization For You?
535.3 Data Visualization Tools Comparison
1109.7 The Challenges Of Big Data Visualization
545.4 How to Create Simple Visualizations with
1119.8 Big Data Visualization Tools
55Google Charts and Pandas Dataframes
1129.9 Summary
565.5 Summary
1139.10 Exercise
575.6 Exercise
114Glossary
