Reddit reviews Doing Data Science: Straight Talk from the Frontline
We found 3 Reddit comments about Doing Data Science: Straight Talk from the Frontline. Here are the top ones, ranked by their Reddit score.
O'Reilly Media
We found 3 Reddit comments about Doing Data Science: Straight Talk from the Frontline. Here are the top ones, ranked by their Reddit score.
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:
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.