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56

I believe actually the opposite of your conclusion is true. In The Disposable Academic, several pointers are given about the low wage premium in applied math, math, and computer science for PhD holders over master's degree holders. In part, this is because companies are realizing that master's degree holders usually have just as much theoretical depth, ...


47

Disclaimer: I have a Ph.D. and work in machine learning. Having said that, I think other than becoming an academic, you don't need a Ph.D. to work in any field. Getting a Ph.D. helps you develop certain research skills, but You don't need those research skills for most jobs. You can acquire those skills without getting a Ph.D. degree. Martin Wolf, the ...


22

I'm answering as someone who routinely evaluates and hires data scientists. As a person transitioning from academic study into a private sector career, you're not going to get hired on the strength of any specific skills you have. The world of academic study in statistics, and the domain of any given company's set of problems are far too vast to hire on ...


19

Disclaimer: I do not have a PhD in CS, nor do I work in machine learning; I am generalizing from other knowledge and experience. I think there are several good answers here, but, in my honest opinion, they do not yet quite make the main issue explicit. I will attempt to do so, but recognize that I don't think I'm saying something radically different. ...


14

My 2 cents: No, I don't think so. A PhD per se does not entitle one to be be better for data mining or ML. Take kaggle's own Jeremy Howard. I would even go as far as saying that a PhD says not much about any qualification as there is a huge variability in quality of programs. Perhaps the only thing a PhD proves is for the holder to have a high tolerance of ...


14

Disclaimer: I'm a recruiter and have been since 1982 so I understand your question very well. Let me break it down this way. Your resume is a screening out device. Companies get tons of resumes so they're reading resumes with one question in mind, "Why don't I want to talk to this person?" That reduces their pile to a few candidates who hold the best chances ...


14

I agree with most that has been said here, but I want to introduce a few practical issues that arise when applying for jobs in finance. Often you will see ads stating that a PhD in statistics or mathematics is required to apply for a particular trading or quantitative developer position. I know there are some particular reasons for this. Mind, I'm not saying ...


9

I started out on a Ph.D. track. About halfway through I realized that I wanted to be out working in industry more than I wanted to be working towards a Ph.D. But that's me.. A PhD is a bit of a grind, and as with everything else that's hard in life, keeping one's motivation throughout is a function of the intrinsic value you derive from the pursuit. In ...


6

Whether a job requires a PhD or not depends on level of responsibility and the perception of the employer and/or his clients. I do not think there is a discipline that requires a PhD. Certainly data mining can be learned and an employee can do productive work without a PhD. This depends more on the person, his or her ability to learn quickly and adapt as ...


5

I have a PhD in statistics, where I specialised in Bayesian theory. These days I am doing work as lead statistician on RCT research in health. This involves RCT planning and execution, analysis of data, and reporting outcomes of trials. On the basis of my experience, this is what I think: For the vast majority of work doing statistical programming in ...


5

I recently finished my PhD, and I have some thoughts in regard to writing papers. I have to mention that I worked in the Dutch system where we have 4 years to complete our PhD, and I got a normal salary, competetive with industry starting salaries. The norm in the Dutch system is to write around 4 papers in those 4 years. How many paper one can write also ...


4

This totally depends on the job at hand. In my experience (I have a PhD), there are 3 types of jobs. First, as it has been said, most industry jobs these days are oriented towards applied machine learning, i.e. apply-tweak of existing ML algorithms to the domain-specific problem in question. These are by far the most common ML jobs and a Masters degree is ...


4

If your interest is in skills that are "marketable," I would say learn about a variety of modeling techniques (GLMs, survival models both continuous and discrete, random forests, boosted trees) with an emphasis on prediction over estimation. Mathematical statistics can occasionally get too bogged down in estimation under parametric models, trying to answer ...


4

I have a masters degree in Applied Statistics and worked in Europe as a Data Miner. When I came to the UK nobody had even heard of data mining let alone studied for such a degree. Now it is common place and employers feel that a Phd is necessary for this job. However, it is the statistical knowledge and the modelling aspect which is important for this job. ...


4

I don't think the coursework is intended to be there as busywork in case you prove the Riemann hypothesis on your first day. More likely, the faculty has made the decision that it wants to get all of its graduate students up to some minimal level of mathematical/statistical competence prior to research training, and a substantial program of graduate-level ...


3

As someone who spent their post-doctoral career in industry, I'd say this. Matthew Drury's response is first rate. dsaxton's remarks on prediction vs estimation are also good. Learn to program using whatever will help you get through grad school with speed. Get good at it. Once you are very fluent in one language, other ones are easy to pick up and you can ...


3

when you say quantitative psychology, what do you mean? That is a tremendously varied field. It ranges from things like computational psychology to doing anovas. In my opinion, the best way to support your career in academia is to create a strong publication record in your desired field of interest. What I mean by this is that if your field of interest is ...


3

Take a look at the math section at http://econphd.econwiki.com/books.htm. I would also add the two measure theory books from the econometrics section.


3

Leah Welty, Emerging Trends, 2013 Davidian, Cutting Edge: Emerging trends in biostatistics, 2012 Modern Issues and Methods in Biostatistics, Springer, 2011


2

People who look down PhD training either don't know what a PhD means at all, or just intentionally make untrue comments; most masters training cannot compare with PhD training by any means. the intensity and rigor in PhD training requires unimaginable dedication, self-discipline, learning ability under great pressure, and solid skill sets..., a PhD title ...


2

I dont think that Phd is required for any machine learning positions. A good masters and an inquistive mind with mathematical curiosity is all what it needs. A Phd biases your approach towards your specialization which is undesirable. I work on core Machine learning algorithms, and codes most of them in the way i want. And i have seen a lot of Phd people ...


2

Most of the current answers are oriented "data science", which is definitely a highly employable area. As the original poster mentioned a particular interest in stochastic processes and time series, another area of mathematical statistics* that may be relevant is state-space estimation. This is used to estimate models where the system evolves due to ...


1

To echo and further emphasize the statement of Yair Daon in the comments: If your passion is developing/improving/analyzing methodology, it is important that your thesis will sufficiently reflect that. If your supervisor's expertise is in an application (e.g., psychology), s/he might not be interested in you putting your time into such methodology-heavy ...


1

My recomendation: Start here: Measure Theory Made Ridiculously Simple. Then buy and read Burrill. I know its old, but its super inexpensive on Amazon and a really good read. It covers basics of Real Analysis and Probability Theory.


1

I wouldn't suggest something radically new, but as a professional data-scavenger myself, I would like to emphasis a few points. All marketable skills are not just only bundle of single isolated skills, but they are a whole synchronized package. And by package, I mean, A set of practical skills, with extremely high proficiency. Like you can form meaningful ...


1

For your level, I highly recommend the Machine Learning course on Coursera taught by the reputable Andrew Ng from Stanford. It can be found here: https://www.coursera.org/course/ml This course starts at a pretty basic level (linear regression) and works it's way up to things like clustering and anomally detection. Don't expect to be an expert after this ...


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