I believe this is useful for many students/researchers who come from related fields like computer science, and have not had the chance to take pure math courses in analysis.
My situation is as follows. I am a beginning graduate student in computer science. I want to take the measure theoretic probability course that starts in the beginning of September (and follows Rick Durrett's "Probability: Theory and Examples"). I really want to do it. So that leaves me with close to 3 months. And my current background in mathematics is limited to linear algebra/calculus/intuitive probability. Although I haven't done pure math, I have taken complexity theory course in computer science, and am comfortable with writing rigorous proofs and dealing with abstract concepts.
Could someone advise me the steps to reach the stage where I am able to understand the course? I have been going through Spivak's calculus to revise the feel of rigorous calculus. Should I go through "baby Rudin" next? Which chapters are enough? Is that all, or do I need to know more? I need to know all this in advance so that I can plan for the next 3 months. Is there an online set of lectures/notes that could be helpful?
Maybe it is useful to add that I am going to be simultaneously attending 2 semester standard real analysis sequence that begins with baby Rudin. But I want to be prepared for the probability course in advance.