This document provides an overview of key concepts for working with D3, including:
- D3 uses standard web technologies like HTML, SVG, and CSS rather than introducing new representations. Learning D3 largely means learning web standards.
- Visualization with D3 requires mapping data to visual elements using scales. Scales are functions that map from data values to visual values like pixel positions.
- Selections in D3 correspond to elements in the DOM. Data joins allow binding data to selections to drive attribute updates. The enter, update, exit pattern is used to handle new, existing and removed data.
- Common scale types include linear, log, quantize and quantile for quantitative data, and
This document provides an overview of key concepts for working with D3, including:
- D3 uses standard web technologies like HTML, SVG, and CSS rather than introducing new representations. Learning D3 largely means learning web standards.
- Visualization with D3 requires mapping data to visual elements using scales. Scales are functions that map from data values to visual values like pixel positions.
- Selections in D3 correspond to elements in the DOM. Data joins allow binding data to selections to drive attribute updates. The enter, update, exit pattern is used to handle new, existing and removed data.
- Common scale types include linear, log, quantize and quantile for quantitative data, and
This document discusses design thinking and how it relates to data and creativity. It provides an overview of design thinking as a process that balances analytical thinking and intuitive thinking. It also discusses how design thinking can be applied to business processes and problem solving. Additionally, it explores how data and creativity can work together, noting that creativity separates humans from animals and involves adapting nature to solve problems. The document advocates for educational systems that encourage students to use existing knowledge to address new challenges independently.
This document is a profile for Ryan Kim, who is described as a "data reading man". It lists his educational and professional background, including degrees in management information systems and computer engineering. It also lists his social media profiles and websites. The document references various sources and topics related to data visualization, analysis, and using data to drive innovation.
This document contains summaries and excerpts from the Harvard Business Review article "When Data Visualization Works And When It Doesnt" published on March 27, 2013. It discusses three reasons for visualizing data: confirmation, education, and exploration. It also addresses factors that influence the effectiveness of data visualization like data quality, context, and creator biases.