[Spatial Temporal Data: PhD vs. Industry]

Wow, great information, really allows me to narrow down my course selection for next year to best accomplish my goals. So glad that I joined GradCafe and posted the question!

[Spatial Temporal Data: PhD vs. Industry]

Thank you @bayessays and @Stat Postdoc Soon Faculty, both very helpful! It is nice to have realistic expectations prior to applying but it sounds like the best thing to do is simply confirm with the programs that I actually get admitted to (if that happens lol) and then make a decision.

 Not to completely change subject, but I noticed schools in UK have much shorter program lengths (3-4 years) and seem to expect candidates already having masters degrees. It seems like this assumption automatically shaves a year off the program (whereas in the US it seems to vary), I wonder if there is a tradeoff associated with this benefit?

[Spatial Temporal Data: PhD vs. Industry]

13 minutes ago, Stat Postdoc Soon Faculty said:

I don't have firsthand experience with this. But I co-authored a paper on spatio-temporal statistics with a friend who got a Masters in Statistics and then earned his PhD in Agricultural & Biological Engineering with a concentration on spatio-temporal statistics. He is doing very well now, working as a Senior AI Quantitative Researcher at the Climate Corporation (he also interned there as a geospatial statistician the summer before he went to go work there full-time). Since he had a Masters in Stat already, he was able to complete his PhD in four years. I'm sure that you would be well-positioned to do so as well if you can bypass having to repeat Masters-level courses.

I would look at the job requirements for jobs that interest you. If most of the job descriptions say "PhD preferred," then it might be worthwhile to go straight to the PhD. If you decide to go into industry in your final years, you can always do another internship the summer before you graduate. I would also assess how important it is for you personally to earn a PhD. If this is very important to you, you should go straight into it from your Masters. If you're on the fence, it might be better to go make some money.

I have (naively) not considered programs other than Statistics, so that is a helpful comment.  

Just making sure I understand the part on finishing in 4 years since I am still learning; PhD sequence is roughly (1) Take Masters Courses (2) Pass qualifying exams (3) Enter Candidacy - Research/ take PhD courses (4) Comprehensive Exams (5) Thesis defense. Your saying that with adequate preparation I can validate (1), (2) thus reducing the entire length of the program (like your friend did)? My guess is that this is program specific but speaking generally probably best to load up on electives that prepare me for the qualifying exam(s) (Applied Stats, Stat Theory, complimentary math classes - real analysis, linear algebra, etc.)?

 

[Spatial Temporal Data: PhD vs. Industry]

I am interested in pursuing a PhD in Statistics to specifically research Spatio-Temporal Data or working in industry working with spatial data (finishing my first year at a Masters in Statistics at Top 5 Stat program & interning at top GIS company this Summer).

I am trying to see if anyone can share their experiences in this field (Spatial Temporal Modeling) about going the PhD route or straight to industry (government, tech, or other)? 

Also,  after searching the web, I came up with these prof's/ uni's as a starting point for reading literature: Chris Wikle (U. of Missouri), Debashis Mondal (Oregon State), and Mevin Hooten (Colorado State). Any others to mention as I begin reading papers this Summer to better compare/ contrast the two routes?

Thanks! Any and all advice is appreciated!