Machine learning is the attention hog of data science, but there are other important aspects:
data quality (cleaning),
feature engineering, and
operationalization (using models in practice).
This talk gives some specific examples each.
I presented this during the Microsoft Machine Learning, Analytics and Data Science Conference in Redmond, WA, December 7, 2016 and The Data Science Conference in Chicago, IL, November 12, 2015.
If you found this helpful, I also recommend My Blog. I work at Microsoft, but my opinions are my own.