Data science, one of the most significant developments in computing in the 21st century, is a discipline in the making, drawing principles, methods and tools from established fields like computer science, statistics, science, business, politics, and any domain with adequate data. Yet, data science is just starting. What are its underlying principles and techniques (models, methods) that are applicable across different use cases and fields of application? What «science» underlies this emerging discipline?
We are launching an activity to develop a reference framework for data science and invite your participation, organized in a two-stage process:
We invite contributors to present their ideas at the first symposium and to continue the development of a reference framework for data science as an activity in the intl. data science community, to be finalized during the second symposium.
Subjects of interest include, but are not limited to:
Outline of the initial workshop and call for contributions:
From Braschler et al. (Eds.), “Applied Data Science – Lessons Learned for the Data-Driven Business”, Berlin, Heidelberg: Springer, 2019:
 Jordan M., “Artificial Intelligence – The Revolution Hasn’t Happened Yet”. UC Berkeley, April 18, 2018.
 Jordan M., “Dr. AI or: How I Learned to Stop Worrying and Love Economics”. Harvard Data Science Review.
 Donoho D., “50 years of Data Science”. Princeton, September 18, 2015.
Prof. AI/ML, Scientific Director ZHAW digital
Senior Researcher Deep Learning
Prof., Head of Applied Complex Systems Science
Applied Complex System Science