There are three good reasons for an actuarial science student to take real analysis:
1. Analytic methods are useful in many subfields of actuarial science. Anything that deals with probability is going to be easier to study if you know some analysis, and that includes machine learning and anything with randomized algorithms. Analytic combinatorics is also becoming a hot topic in the analysis of algorithms, and that's something you definitely need analysis for.
2. Research in actuarial science requires you to be comfortable with rigorous mathematics. You can get that out of taking algebra or topology, but because analysis is all about rigorously studying things you're already familiar with, it can be a gentler introduction.
3. Finally, almost every serious field of applied mathematics uses analytic methods. You really can't consider yourself literate unless you have that basic background.
1. Analytic methods are useful in many subfields of actuarial science. Anything that deals with probability is going to be easier to study if you know some analysis, and that includes machine learning and anything with randomized algorithms. Analytic combinatorics is also becoming a hot topic in the analysis of algorithms, and that's something you definitely need analysis for.
2. Research in actuarial science requires you to be comfortable with rigorous mathematics. You can get that out of taking algebra or topology, but because analysis is all about rigorously studying things you're already familiar with, it can be a gentler introduction.
3. Finally, almost every serious field of applied mathematics uses analytic methods. You really can't consider yourself literate unless you have that basic background.
Is real analysis useful for an actuarial scientist?
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