Depends where you are starting from and what you want to learn. The linked book is a first year introduction, and does a good job of that. If you want to go further there are many other options:

* Statistical Inference by Casella and Berger. This book has a very good reputation for building statistics from first principles. I won't link to them, but you can find full PDF scans online with a simple search. Amazon reviews: https://www.amazon.com/Statistical-Inference-Roger-Berger/dp...

* Statistics by Freedman, Pisani, and Purves has similarly very good reviews and can be easily found online. Amazon reviews: https://www.amazon.com/Statistics-Fourth-David-Freedman-eboo...

* The majority of the Berkeley data science core curriculum books are online. This is not purely statistics but 1) is taught in a modern style that makes use of computation and randomization and 2) uses tools that may be useful to learn about.

1. https://inferentialthinking.com/chapters/intro.html (Data 8)

2. https://learningds.org/intro.html (Data 100)

3. http://prob140.org/textbook/content/README.html (Data 140)

4. https://data102.org/fa23/resources/#textbooks-from-previous-... (Data 102; this gets into machine learning and pure statistics)

The Berkeley curriculum is not the only one; there are tens, possibly hundreds, of online courses. The Berkeley curriculum is just 1) quite extensive and 2) the one I happened to read the most about when I was recently researching how data science is currently taught.

I like statistical rethinking. It’s targeted at science phd students so the focus is “how can you use statistics for testing your scientific hypotheses and trying to tease out causation”. It doesn’t go deep into the mathematics of things (though expects readers to be decently numerate and comfortable analysing data without statistics). It only really talks about Bayesian models and how to fit them by computer, so won’t cover much of the frequenting side of things at all.

ISLR/ISLP is free, was used in my masters and is excellent (and has an accompanying video series)

https://www.statlearning.com/

I particularly like Statistical Inference by George Casella and Roger Lee Berger.

You could also look at Introduction to Probability by Joseph K. Blitzstein and Jessica Hwang (available for free here: http://probabilitybook.net (redirects to drive)).

A couple of more introductory books that come at it from the point of view of "someone who can code" are: - https://greenteapress.com/wp/think-stats-2e/ (and the similar Think Bayes if you enjoy this one) - https://nostarch.com/learnbayes

Can second Statistical Rethinking though if you have the basics of stats and want to learn it again from a very different, more causal/bayesian point of view.

What is your background and what field will you be applying your knowledge to?

There can be a rather wide gap between a theoretical approach that you might encounter as taught by a statistician and an applied approach you might encounter in a business statistics or social science statistics course.

Depending on your math background and the area of intended application, in my opinion, it would sway recommendations for a first 'book' on statistics for self-learning.

A good introductory book is this one: "The Statistical Sleuth. A Course in Methods of Data Analysis" (3rd Edition) by Fred Ramsey and Daniel Schafer <http://www.statisticalsleuth.com/>.

Another good book at a more advanced level is this one: "Regression and Other Stories" by Andrew Gelman and Jennifer Hill <http://www.stat.columbia.edu/~gelman/arm/>.

Another good book at an even more advanced level is this one: "Data Analysis Using Regression and Multilevel/Hierarchical Models" by Andrew Gelman and Jennifer Hill <http://www.stat.columbia.edu/~gelman/arm/>.

These books are all very polished productions. What make makes them special is that they emphasis teaching the statistical way of thinking.

No one ever mentions Concrete Mathematics (Graham, Knuth, et al), but it's a classic text for math and stats.

https://pubs.aip.org/aip/cip/article-abstract/3/5/106/136800...

Good video lecture series:

https://www.thegreatcourses.com/courses/learning-statistics-...

Might be available for free via your local library, too.