In reply to Jon Dickens.

A full answer would require many paragraphs, but in brief: I don't distinguish between them.

However, each discipline has certain concepts that it emphasizes:

In statistics, an emphasis is making valid inferences about estimates in the face of random variation in the data.
In machine learning, the emphasis is predictive models that are accurate for future data (holdout samples) so ML stresses reducing bias by using the concepts of training, testing, and validation.
Data science incorporates data wrangling and ML: using tools to scrape and prepare data prior to model building.