Data science in context: foundations, challenges, opportunities

Data Science. Foundations of data science

Data science is transdisciplinary

A framework for ethical considerations

Applying Data Science. Data science applications : six examples

The analysis rubric

Applying the analysis rubric

A principlist approach to ethical considerations

Challenges in Applying Data Science. Tractable data

Building and deploying models

Dependability

Understandability

Setting the right objectives

Toleration of failures

Ethical, legal, and societal challenges

Addressing Concerns. Societal concerns

Education and intelligent discourse

Regulation

Research and development

Quality and ethical governance

Concluding thoughts

Appendix Summary of recommendations from part IV.