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The original was posted on /r/machinelearning by /u/AdFew4357 on 2024-04-06 20:11:36.

I’m currently an MS stats student. Right now I have been kinda bored of the standard classical statistics I’ve been learning. I initially chose this path because I wanted to set myself up for industry well. I’ve got a data scientist internship for the summer, and I’ve considered working fulltime. However, I do want to pursue research after a few years of work experience, and the question I come back to is whether I want to go for a PhD in Stats or a PhD in CS.

To be frank, my programming skills are very sub par compared to most CS students. My undergraduate was pure math and statistics, and while I did take Python, R, some Java I couldn’t say I’m at the level of a software engineer. I know my math and stats theory well, and can use packages in Python and R to do things effectively, and write functions etc, but if you asked me right now “write a class in Python” I’d probably be stuck cause I never write classes.

I’m no longer really interested in stats PhD programs, because if I were to do a PhD in stats I’d have to spend the first two years doing coursework, which frankly I’m just tired of. I don’t want to spend time proving asymptotic results of the MLE under logit models, or spending a semester learning things like theory of the linear model.

I have an MS in Stats now, and I think I’ve beat stats to death enough.

I found a great deal of interest in an area of deep learning that naturally drew me in coming from a statisticians point of view, which are the advances in time series forecasting.

I have taken time series in stats graduate programs where we learn all the classical methods: arima, sarima, garch, and some nonstationary time series models like state space models. I also have a background in classical nonparametric regression, (statistical learning) as this is the topic of my thesis.

These are very fascinating but I have gotten interested in how CS departments are using deep learning methods to extract information from time series. The old school statistician in me is tired of learning “use the ADF test to verify stationary, fit an arima and sarima model to model this time series, and forecast” and I’m now seeing huge advancements in time series coming from cs departments which I want to be in. Furthermore, since I have also had plenty of experience in applied Bayesian analysis, I think my background on this could also be unique addition. Causal inference is something I’ve dabbled into as well and any aspect of this in DL I’d be interested in giving my input as well.

So for anyone here, was there anyone like me whose background came up through old school statistics, like an MS in Stats, and now made that switch to a PhD in CS to work on more modern topics? I feel my background in fundamental topics like Bayesian inference, time series, statistical learning and causal inference could be something I could add to research in CS.