The document outlines the integration of design thinking within the data science process, emphasizing stages such as understanding the problem, formulating hypotheses, and presenting results. It highlights the importance of a human-centered approach, encouraging data scientists to engage with people. Additionally, it provides valuable resources for further learning about design thinking and its application in problem-solving.

1. Design Thinking
for Data ScienceZeydy Ortiz, Ph. D.
DataCrunch Lab, LLC
Twitter: @DrZeydy | @DCrunchLab

2. DataCrunch
Lab
Data Science Process
2
Understand
the problem
Formulate
hypothesis
Understand
the data
Analyze
data
Prepare
results
Present
results

3. DataCrunch
Lab
Design Thinking Process
3
Empathize
Define
Ideate
Prototype
Test

4. DataCrunch
Lab
Valuable Resources
4
 A Virtual Crash Course in Design Thinking
https://dschool.stanford.edu/resources-collections/a-virtual-
crash-course-in-design-thinking
 “The Best Data Scientists Get Out and Talk to People” –
Thomas C. Redman, Harvard Business Review
https://hbr.org/2017/01/the-best-data-scientists-get-out-and-
talk-to-people
 Design Thinking Explained http://www.designkit.org/human-
centered-design
 5 Stages in the Design Thinking Process
https://www.interaction-design.org/literature/article/5-
stages-in-the-design-thinking-process?ep=cv3
 Design Thinking, Essential Problem Solving 101- It’s More
Than Scientific https://www.interaction-
design.org/literature/article/design-thinking-essential-
problem-solving-101-it-s-more-than-scientific?ep=cv3
 Design Thinking Methods http://www.designkit.org/methods