ISBN/ASIN: 1430258063,9781430258063 | 2013 | English | pdf | 216/207 pages | 10.8 Mb
Publisher: Apress | Author: Tom Barker | Edition: 1
Gathering and analyzing empirical data is the key to truly understanding anything. We can track operational metrics to quantify the health of our products in production. We can track quality metrics of our projects, and even use our data to identify bad code. Visualizing this data allows anyone to read our analysis and easily get a deep understanding of the story the data tells.
Developers at all levels interested in data visualization, beginning to intermediate engineering managers, statisticians, mathematicians, economists and any others interested in data visualization.
Table of Contents Techniques for Data Visualization The R Language A Deeper Dive into R Data Visualization with D3 Visualizing Spatial Information from Access Logs (Data Maps) Visualizing Defects over Time (Time Series) Bar Charts Correlation Analysis with Team Dynamics (Scatterplot and Bubble Chart) Balancing Delivery with Quality (Parallel Coordinates Chart)