I found this course to be very helpful, it has a good balance of reading material and labs to apply what you learn. The course is from the Austin’s Department of Statistics and Data Sciences.
Note: In this course, Dr. Michael J. Mahometa uses R. But I'd recommend you not to focus on R vs Python debates; the goal of this course is to learn about Statistics & Data Analysis in real-world scenarios. With that in mind, even just going through the reading material and lecture videos will be valuable enough if you're starting from scratch (but I'd recommend you to take the extra step and complete the Labs too).
This
https://www.amazon.com/Probability-Statistics-Engineers-Scie...
is the newer version of the stats book i had in undergrad,
But @anst makes a good point about scikit learn. there is alot of good math to learn just from the docs and you can then investigate further on wiki, quora, stackexchange.
for the what's up in Data Science i like datatau.com.
and there are some great podcasts too, like datascienceathome and partiallyderivative (there are lists).
There's a series of courses on Coursera, part of a Specialization from Duke titled something like "Statistics and Probability with R" or something like that. I've taken the first few classes in that series and have found them pretty good. The class on Bayesian Statistics is a little more difficult, but not too bad. I'll just say that you might want to complement the class with another book or other references on Bayesian stats. I've used this book:
[Foundations of Data Analysis](https://courses.edx.org/courses/course-v1:UTAustinX+UT.7.11x...)
Note: In this course, Dr. Michael J. Mahometa uses R. But I'd recommend you not to focus on R vs Python debates; the goal of this course is to learn about Statistics & Data Analysis in real-world scenarios. With that in mind, even just going through the reading material and lecture videos will be valuable enough if you're starting from scratch (but I'd recommend you to take the extra step and complete the Labs too).