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.
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.