If you are looking for an easy to use and understand book on survival analysis basics, I recommend this. The "walk you through it with examples and highlighted key terms" approach is unique among textbooks and make it a go to book for me (I'm an epidemiologist). I appreciate the book's candid discussions on the mathematical assumptions of the models, as well as the many examples of SAS and Stata code. If you have a unique data problem or question (or are a statistician), you may find this doesn't go in depth enough. However, understanding the concepts reviewed in this book will give you a huge leg up professionally--and let you understand just how many people use survival modeling but really know little about it. ;)
Standard regression methods will not work for events that are censored (observation partially known), hence survival analysis.
I asked around for where to start on the subject, and I was invariably led to this text. Drs Kleinbaum and Klein delivered on this. Starting from the general introduction to the subject, Kaplan-Meier estimates, Log-rank tests, Nelson-Aalen estimates, RMST, Cox models, etcetera.
I worked through several sections on the text, aided by Python's lifelines package, and it was 👌🏿.