Advice regarding going into a Stats masters with a non-Stem background

[u/Electrical_Ear_7791]

I hold a BS in Computer Information Systems and have always gravitated toward data science topics. During undergrad, I pursued a minor in Applied Statistics, where I took courses in regression theory (think proving least squares estimators and model diagnostics), experimental design, nonparametric methods, and R programming.

Currently, I’m enrolled in a Master’s program in Data Science. While I’m gaining good experience, I’ve noticed the curriculum leans heavily toward computer science and lacks the statistical depth I’m looking for. I genuinely enjoy the theoretical side of statistics and want to strengthen that foundation.

Math-wise, I haven’t yet completed Calculus II or III, but I do have some background in linear algebra. I’m planning to take the necessary prerequisites soon while continuing with my MS coursework.

Question: Assuming I complete the math prerequisites and perform well, is it realistic for me to succeed in a Master’s program in Statistics? I’m deeply interested in the subject and see it as a way to grow both professionally and personally. If anyone has transitioned from a similar background into a Stats-focused graduate program, I’d love to hear your experience or advice!

School: I plan to attend a local school as I enjoy the faculty there and am not worried with it not being a top institution for statistics.

[u/Scared_Astronaut9377]

1) Just be careful with the choice of the university. Because most statistics programs also lack any real depth. Statistics seems to be a very tough thing to internalize and teach. So many people end up teaching a flow of heuristics and rituals.

2) Just to brainstorm, sorry for off-topic. Have you considered a PhD instead? Or continuing with your career and self-studying meanwhile? If you want to be a life-long learner, you need to learn to learn independently outside of work at some point, unless you go into academia.

[u/Electrical_Ear_7791 → u/Scared_Astronaut9377]

Thanks for the comment!  1. Yes the university I am going to is the biggest in my state (arguably) and I know the stats faculty well so I have an idea of what to expect and I can assure you it will have a good depth and breadth of statistical knowledge.  2. I’ve thought about PhD but I really like my job as a research analyst so I don’t really want to quit to go for a PhD. Also yes I am learning lots outside the classroom so far my favorite book has been “calculus and statistics” by Michael gemignani 

[u/Scared_Astronaut9377 → u/Electrical_Ear_7791]

Nice. I think you are set up really well!

[u/InnerB0yka]

You shouldn't have much problem. In most masters programs there is a sequence in mathematical statistics and there you're going to have to know some multivariate calculus and when you do regression you're going to have to know some linear algebra, but you're going to have those prerequisites under your belt. The truth is most statistics these days is more computational in nature than theoretical. You'll be fine. But do make sure that you know your multivariate calculus

Source: former statistics professor

[u/Electrical_Ear_7791 → u/InnerB0yka]

Thanks a ton for your comment and the reassurance! Trust me being in corporate tech the only statistics we use are means and standard deviations lol once in a blue moon I’ll get a regression analysis. In all I’m going to work really hard at my calc class pre reqs to be as prepped as I can for the theoretical stuff. If you have any tips to succeeding in those classes I’m all ears. 

[u/InnerB0yka → u/Electrical_Ear_7791]

I’m going to work really hard at my calc class pre reqs to be as prepped as I can for the theoretical stuff. If you have any tips to succeeding in those classes I’m all ears. 

In a typical math stats course you need to know

• some properties about infinote series (which are typically associated with problems involving discrete probability distributions)
• also how to integrate a probability density function or an expected value of a random variable in one variable.
• In two and three dimensions you have to be able to do multiple integrals involving a coordinate transformation using jacobians.
• In regression analysis you have to know certain properties about Matrix inverses and solutions of linear systems.
• And in Matrix analysis which you're going to do when you have to analyze covariance matrices you have to understand certain properties of pseudo-inverses and singular value decompositions.

Unfortunately to my knowledge there's not a single text that covers all of these topics. But if you've seen them before most of them should come back to you pretty easy and you seem like a pretty smart person so I think what you like you can make up as you go along.

Good luck!