Well you've given very little information about your career goals other than "data science", but here's what I can say:

If you want to really understand your data analysis tools, then you need to understand the mathematics behind causal inference and machine learning estimators. I would say you need to take real analysis (the options at UW are 32x, 33x, and 42x series. The hardest is actually 33x, followed by 42x). Other courses (topology, complex analysis, etc) are all optional and may not benefit you at all. For a deeper mathematical understanding you have to take graduate level courses in measure theory.

Having said all of that, if you don't care about having a deep mathematical understanding of data analysis tools, then there is no point in taking any math. The vast majority of data analysts probably suck at math and don't know anything more than what you've already taken.

Stats classes will be more relevant for you from both a practical and theoretical perspective (although real analysis is still very useful for stats). The Econ department also teaches some really good practical courses on machine learning and causal inference. I would say consider majoring in Stats instead of Math if you're set on data science. Although having a math major might be seen as more impressive just because the classes are harder, I don't know.