<p>Greetings guys! Im a little new to this forum and so here goes </p>
<p>Im currently an undergrad studying statistics with equal focus on theoretical and applied. While I really like the subject, I also like computer science (from the introductory bit Ive been exposed to) and math (applied a bit more so than pure). </p>
<p>Of course, I haven taken some math and comp-sci but its been mostly just stat. I wanted to know how flexibly I could applied to a masters program in applied math or computer science (computer science, not computer engineering, to clarify), and how is this different from applying to a PhD in these fields? (I do like research and will have done a sort of undergrad thesis hopefully in Stats if this adds any clarification to the PhD question.) </p>
<p>I would be extremely grateful for any insight - thanks a bunch! :) </p>
<p>To provide a better picture of my background, heres the coursework I will have done by the end of college: </p>
<p>Statistics Core:
- Probability Theory (including grad-level)
- Statistical Inference (including grad-level)
Applied Stats:
- Linear Models, Generalized Linear Models
- Causal Inference
- Bayesian Computing
Theoretical Stats:
- Stochastic Processes
Electives I have not decided between: Multivariate Analysis or Statistical Computing/Visualization or Data Science </p>
<p>Mathematics:
Pure: Real Analysis, Linear Algebra (proof-based of course)
Applied: Differential Equations </p>
<p>Computer Science:
Just the introductory material </p>
<p>Again, thanks a lot for your time!</p>