Survival Analysis in R (Udemy.com)

The course is outstanding. We are taught the essentials about survival analysis. And from my point of view, it's all we need about it.

Really liked the easy explanations, the selected concepts and the examples. This is a really good introductory course into to the topic.

Good and clear presentation. Very useful for beginner. Hopefully there are courses about the covariate search, model assumption test, etc.

Quite good. Covered a fair amount of theory and practice. The only thing I'd like to see more of is recommended resources to read more on the topic; having something to explain a little deeper than the functional level on this particular topic would be extremely helpful.

Very good! As a suggestion, the course could present more sofisticated ideas such as system realiability of repairable equipments and parametric modelling using Weibull distribuition (and other distributions).

Excellent course, provides basic knowledge for survival analysis(and methods), as well as a few additional, but useful skills for data manipulation in survival analysis. It is definitely recommended!

Great course, I'm following along with my own project. I've done a lot of survival analysis courses and I think this is the best one.

This course forms a good introduction to survival analysis with clear and easy to follow instructions. The practical examples adds value to really understand the results and how to interpret them.

still have no received any responses to my questions. overall, one can use this course to get started in conducting survival analysis (or interpreting its results). it builds a decent foundation but analysts should supplement this course with additional reading from a textbook or university-level course to develop a more comprehensive understanding of survival analysis.

The instructor does a great job in presenting a good overview of the topic, I enjoyed following the lectures and I am sure I will soon have a chance to apply in practice what I learned here. Room for improvement: - low hanging fruits: a few more sentences on the confidence intervals, the interpretation of the model coefficients (beta lambda is scaled by a factor between 0 and 1 -> expected life increases), when to use parametric vs non-parametric models. - I think the section on survival trees would be impossible to follow without a basic understanding of ML. It shouldn't take a lot of extra material to give an intuition of the random forest algorithm Thanks again for the course!