Reddit mentions of Doing Data Science: Straight Talk from the Frontline

The book Doing Data Science, cowritten by Cathy O'Neil (of Weapons of Math Destruction may be of interest to you.

> In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.

I haven't read the whole thing yet, but it's well-written and has a nice survey of topics.

Doing data science by Cathy O’Neal
https://www.amazon.com/Doing-Data-Science-Straight-Frontline/dp/1449358659/ref=mp_s_a_1_3?crid=32WET5G295L42&keywords=doing+data+science&qid=1556807847&s=gateway&sprefix=doing+data+sci&sr=8-3

She does a great job explaining the process of teaching data science using the principles of data science.

I teach applied math, stats, and computation courses to B.S. degree seeking students. Two observations:

• First, the theory is not pointless. I know, you didn't say or imply that. But I just have to get that out there.
  
  
• Second, my observation is that many applied mathematics/statistics courses at the undergraduate level do an astoundingly poor job of connecting mathematics to real world data. I can go on about this for days, but I'll stop with saying that (a) this is an incredible disservice to students across all disciplines that require any math, and (b) there are mathematicians and statisticians who are aware of the problem and working to make it better.
  
  Okay, rant concluded, book recommendations! First, try Doing Data Science by O'Neil and Schutt. This assumes some knowledge of linear algebra, stats, and programming. Examples are given in R. I think this book is very good at bringing out the idea that data science involves both theory and experience, and is good at bringing out the
  feel" of working on data science problems.
  
  Second, if your math background is calling for plenty of math, you might take a look at Machine Learning from a Probabilistic Perspective. This takes a closer look at the data modeling process, which is somewhat lacking in more CS-oriented texts on ML. Requires good knowledge of probability, obviously.