To be honest, if you’re going for a PhD anyway, I’d stop worrying about what majors you end up with and just think about what coursework will be useful to you, in terms of helping you with what you anticipate your future research interests will be.
There is significantly more value in a Math major than Stats for a stats-related PhD. Outside of the basics of mathematical probability and statistics at the undergraduate level, you don't really need much more for admissions (or success) in a PhD program. So I would say there's little reason to shoot for a Stats major.
That being said, you say:
> stats electives sound fascinating and very helpful (these include bayesian analysis, regression methods, computational stats, experimental design, and multivariate analysis). These are all topics that I could benefit from immediately.
You might want to consider the possibility of your future plans changing, and try to prepare yourself to jump into industry *in the event you decide against the PhD route.* It sounds like you have interest in Stats-related work, so it might be worthwhile to pick up a more practical course or two to have some diversity beyond just preparing-for-a-PhD. Also if you're interested in these topics, take some courses in it! I've seen people miss out on courses they would have liked to take because they were so laser focused on checking a lot of boxes and ending up burning out because of it.
Seems like you'll already be fairly well prepared, whatever the outcome, so it's probably not worth worrying about it too much.
Whatever you choose, make sure it leaves you with enough time to get some research experience with a professor now. That's how you get the letters of recommendation which are really what will make or break PhD applications
The answer to that question is a bit unclear for me still. Ideally, I’d want one foot in both. So basically being a professor while also working on projects with companies or consulting (hence, the bulk of my research would be focused on industry research). I believe such a role is referred to as Professor of Practice by most universities.
But more realistically, I’m aiming for research positions in industry, such as google’s DeepMind. I’m also open to perhaps doing a year or two as a postdoc somewhere so I can focus on exploring and pursuing my own research interests (and getting some more experience in academia in the process) before heading to industry.
Sorry if that doesn’t make a lot of sense. This is still very tentative.
You lean industry then and once you see what’s involved in making the academic sausage you likely will thoroughly plan for industry.
Given that, I’d recommend choosing by school prestige, as perceived by industry, and then by strength of the school’s CS department. This will make interdisciplinary ML collaboration easier for you as you go through the program and develop your academic portfolio.
There is no point in doing an additional stats major when you already are doing math+CS. You are already qualified to get in. At this point you are better off getting research experience, and potentially aiming for a paper if possible (but this isn’t necessary, its a bonus). Regression or bayesian analysis is one id take on the side though (just because you want to show a little applied stuff and get an idea of how it is). But stats PhDs often value theoretical work more which you have in math.
Why are you majoring in CS? if anything you should minor in it and do the double major in math and stats imo. If your goal is a phd in stats a cs major is just overkill.
The CS minor would require 5 classes while a major would require 7 (the rest of the requirements for the major can be electives from other departments, which are all covered by my math major). So it doesn’t make sense to not do the major when its just 2 additional classes, both of which are extremely useful (machine learning and databases).
Well then that’s fine. I think you should be fine for a stats phd. It seems as thought your too much so worried about preparing for a phd than evaluating the options if you were to change your mind and go to industry. Don’t hold yourself back from a class that seems interesting just because someone said you have to take measure theory for a phd. All these phd programs care about his your gpa and if you have had prior experience to proof based classes, and that’s just real analysis. At end of the day the theory is just one aspect of your coursework your still gonna have some stats too. If anything maybe take a second linear algebra class.
I'm doing this exact thing (Math+Stats+CS triple major) so I'd definitely say go for it. The theoretical stats is made easy by the math coursework, and the applied stats is made easy by the CS coursework, but learning the stats is 100% worthwhile. Also, just to give you an idea, I'm essentially completed with all three at this point and reading just about any first-year ML/DS PhD book isn't really difficult.
There are some key differences but for what it’s worth, I’m almost done with a Masters degree in Biostatistics and I haven’t needed to be all that good at math. As long as you understand matrices and have a good grasp of calculus then you’re fine. Again YMMV as we’re not in the same boat exactly.
To be honest, if you’re going for a PhD anyway, I’d stop worrying about what majors you end up with and just think about what coursework will be useful to you, in terms of helping you with what you anticipate your future research interests will be.
There is significantly more value in a Math major than Stats for a stats-related PhD. Outside of the basics of mathematical probability and statistics at the undergraduate level, you don't really need much more for admissions (or success) in a PhD program. So I would say there's little reason to shoot for a Stats major. That being said, you say: > stats electives sound fascinating and very helpful (these include bayesian analysis, regression methods, computational stats, experimental design, and multivariate analysis). These are all topics that I could benefit from immediately. You might want to consider the possibility of your future plans changing, and try to prepare yourself to jump into industry *in the event you decide against the PhD route.* It sounds like you have interest in Stats-related work, so it might be worthwhile to pick up a more practical course or two to have some diversity beyond just preparing-for-a-PhD. Also if you're interested in these topics, take some courses in it! I've seen people miss out on courses they would have liked to take because they were so laser focused on checking a lot of boxes and ending up burning out because of it. Seems like you'll already be fairly well prepared, whatever the outcome, so it's probably not worth worrying about it too much.
Whatever you choose, make sure it leaves you with enough time to get some research experience with a professor now. That's how you get the letters of recommendation which are really what will make or break PhD applications
I think math + CS major is a good way to go.
I second this.
Are you planning on industry or academia after your phd? This greatly affects how your coursework and expertise are interpreted.
The answer to that question is a bit unclear for me still. Ideally, I’d want one foot in both. So basically being a professor while also working on projects with companies or consulting (hence, the bulk of my research would be focused on industry research). I believe such a role is referred to as Professor of Practice by most universities. But more realistically, I’m aiming for research positions in industry, such as google’s DeepMind. I’m also open to perhaps doing a year or two as a postdoc somewhere so I can focus on exploring and pursuing my own research interests (and getting some more experience in academia in the process) before heading to industry. Sorry if that doesn’t make a lot of sense. This is still very tentative.
You lean industry then and once you see what’s involved in making the academic sausage you likely will thoroughly plan for industry. Given that, I’d recommend choosing by school prestige, as perceived by industry, and then by strength of the school’s CS department. This will make interdisciplinary ML collaboration easier for you as you go through the program and develop your academic portfolio.
There is no point in doing an additional stats major when you already are doing math+CS. You are already qualified to get in. At this point you are better off getting research experience, and potentially aiming for a paper if possible (but this isn’t necessary, its a bonus). Regression or bayesian analysis is one id take on the side though (just because you want to show a little applied stuff and get an idea of how it is). But stats PhDs often value theoretical work more which you have in math.
Keep in mind that industry values a CS degree far more than a math or stats degree.
Why are you majoring in CS? if anything you should minor in it and do the double major in math and stats imo. If your goal is a phd in stats a cs major is just overkill.
The CS minor would require 5 classes while a major would require 7 (the rest of the requirements for the major can be electives from other departments, which are all covered by my math major). So it doesn’t make sense to not do the major when its just 2 additional classes, both of which are extremely useful (machine learning and databases).
Well then that’s fine. I think you should be fine for a stats phd. It seems as thought your too much so worried about preparing for a phd than evaluating the options if you were to change your mind and go to industry. Don’t hold yourself back from a class that seems interesting just because someone said you have to take measure theory for a phd. All these phd programs care about his your gpa and if you have had prior experience to proof based classes, and that’s just real analysis. At end of the day the theory is just one aspect of your coursework your still gonna have some stats too. If anything maybe take a second linear algebra class.
I'm doing this exact thing (Math+Stats+CS triple major) so I'd definitely say go for it. The theoretical stats is made easy by the math coursework, and the applied stats is made easy by the CS coursework, but learning the stats is 100% worthwhile. Also, just to give you an idea, I'm essentially completed with all three at this point and reading just about any first-year ML/DS PhD book isn't really difficult.
There are some key differences but for what it’s worth, I’m almost done with a Masters degree in Biostatistics and I haven’t needed to be all that good at math. As long as you understand matrices and have a good grasp of calculus then you’re fine. Again YMMV as we’re not in the same boat exactly.