my daughter will end high school (equivalent) next year. Her top career choices for university are:
nurse
statistics
It seems those are quite opposite ends of a spectrum with many regards - but lets leave that discusson out.
Given the current state of affairs of AI … what are your thoughts on studying statistics? … my (not overly investigated) position is in the short run, statisticians (sp?) will have good career opportunities (big data, creating models, all that jazz that goes into AI) … but in the medium/long run, stat/math will be a field easily taken over by AI. → or am I wrong here?
Of course nursing (that is quite a wide area and includes elderly-care) will be not easily replaced by AI …
any thoughts or guidance will be highly appreciated
Statistics are used by two kinds of people: Those who want to prove some particular point, and those who want to find out the truth, whatever it is. The current generation of AIs are very well-suited to the former, but not so much to the latter.
Nursing will probably stay in high demand for her entire life, though many areas still pay nurses terribly. She might need to move to a state that pays well.
Statistics is a job that will see decreased demand, possibly even by the time she earns her degree. On a risk/reward basis sounds like a poor choice.
Both my sisters are/were nurses as is my niece. All had very different jobs. There is a lot to nursing, more than most people think about.
Those jobs seem very different from a personality perspective. Nursing is constantly interacting with lots of different people doing varied tasks and being caring and compassionate. Statistics will be a lot of solitary, focused work. Picking the right kind of job for her personality will also be important. One career seems better for extroverts and the other for introverts. Certainly anyone can do any career, but a career path lasts a long time. If it’s not a good personality fit, then she may not stick with it. But lots of kids change their major in college. Whatever she picks now is just a starting point. If she finds she doesn’t like the coursework, she can pivot to something else.
If she can’t decide between the two fields, she can try to combine them. Local and State health departments require personnel to monitor disease outbreaks and compile statistics. She could do both.
AI isn’t going to take jobs from statisticians any time soon but it’s of course hard to predict future breakthroughs. Yes, it’s currently good at recognizing patterns in data sets if it’s been trained on that type of data, but statisticians already have very robust tools to help them do that. Statistics is about more than pattern recognition.
If your daughter’s interest is in health care (as opposed to more specific patient care), she might be able to do both. Biostatisticians are an important part of medical research and there are lots of nurses in roles that involve working with numbers more than patients.
I agree that those are very different careers. Nursing is unlikely to be overtaken by ai. In fact, there may be some paperwork and record keeping that is eased.
As for statistics, that is math and unless she wants to teach, she needs to decide what kind of career she wants. Data science, engineering, economics, even some jobs like big ag or meteorology are possible. Statistics is a lot more than mean, median, and standard deviation. It can be a powerful tool. But you have to like math…a lot.
As noted by others, there are many kinds of nursing. My SIL (the good one, not the one I’ve complained about) was a public health nurse with a BSc and her work was very different from a hospital nurse. Also, nurses don’t have to feel caring and compassionate, any more than your server at the restaurant has to be genuinely cheerful and glad to see you even though you are a complete stranger. They have tasks to perform and part of that is emotional labour that can be taught and learned however one’s personality is set.
They are not quite as divorced as it might seem. There is the entire field of medical biostatistics, and nurses (at the BSN/RN nurse practitioner level, at least) need to be conversant in the basics of biostatistics. Nursing is a highly diverse field that can stretch from clinical practice (which is what everyone thinks of in terms of ‘nurses’) to medical forensics, research, management, et cetera. There are a lot of frustrations that can come along with a career in nursing that are not due to the area of practice itself but all of the issues with insurance, hospitals and for-profit medical service providers, pressure to cut costs and be more ‘efficient’, i.e. ‘service’ more patients with less time and support, et cetera, which is something to bear in mind, but there will always be a demand for nurses in a wide array of areas. The best way to get a gauge of this is to actual talk with nurses or better yet volunteer at a hospital or get certified as an EMT to see what the practical side of being a front line medical responder is like before diving headfirst into 6+ years of college and nursing school.
I would caution about making any plans based upon what ‘AI’ will do to industries, especially in the current hype-fueled atmosphere where people keep asserting that ‘ChatGPT’ and other LLM and generative models are going to achieve AGI-status and become autonomous agents who will do everything faster, cheaper, and more reliable than humans, which is pretty much contrary to what we’ve seen to date. Even if some level of that fevered dream comes to fruition, there will still be a need for people to manage and direct ‘AI’ systems because they are actually quite poor at giving accurate results to meet specific requirements. (I also wouldn’t get too invested into ‘prompt engineering’ even though some computer science departments appear to be offering this as a degree program or minor; it’s kind of like going all in on becoming a Java master circa 1999 because the JVM is the inexorable future of the Internet, hai!)
In terms of careers in statistics, there are two broad domains of direct applied statistics; data science, which is the application of statistical methods (typically Bayesian) to large and diverse data sets to tease out patterns and make predictions of vaguely defined trends, and actuarial science, which is concerned with making detailed predictions or projections based upon (largely frequentist) statistical models for business fields like insurance, finance, disaster prediction, reliability engineering, et cetera. Statistics also underpins many areas of science like climate modeling, biology, genetics, and evolutionary science, thermochemistry and statistical mechanics (naturally), quantum physics, et cetera, as well as being the fundamental basis for the modern approach to ‘AI’ and machine learning so it is a useful area to have a working knowledge in. This is in addition to the academic field of research and pedagogy of statistics, i.e. teaching of mathematics specifically pertaining to statistical analysis and interpretation.
Data science has long used machine learning methods, which is a type of AI (but not an LLM-based chatbot) to pull trends out of complex data, and that is even bleeding over into more traditional actuarial science and other areas of scientific research, and as noted above even though many people seem to believe that ‘AI’ is just going to take over all of these areas and humans will just be able to sit around eating olives and drinking wine (or sitting in the gutter wondering what happened to their jobs) there is actually a need for people who understand ‘AI’, the reality is that there will be a need for people who understand the workings of AI and are able to guide and tune it to giving useful answers and be able to assess the reliability and functionality of AI-based systems. So I would argue that having a firm basis in statistics will provide the tools for having a long and robust career, and being able to shift focus and learn new applications even in a world where you assume that AI will take over at doing all of the scut work of science and engineering. A good knowledge of the correct application of statistics will also allow one to assess and cut through the bullshit of a lot of supposedly quantified claims about science, medicine, fiscal policy, et cetera so it is just a generally valuable life skill in a world increasingly flooded with bullshit.
Good luck to your daughter in whatever she chooses to do.
The term you are looking for is epidemiology (which is essentially data science specifically applied to disease pathology and contagion), and it is indeed a valuable skill albeit one that is increasingly underappreciated and poorly compensated for the level of educational attainment. Many registered nurses (RN) do get additional degrees in public health (MPH) with a focus on epidemiology and support governmental health departments or research labs which are focused on public health and disease trends.
Nursing is a huge range and starting in the nursing program with a focus, possibly a minor, in statistics, sets her up extremely well for the power jobs in the field. Yes in term of research, public health, and disease monitoring, but also to have a chair at the table with the bean counters, possibly the chair, impacting meaningfully from the inside of healthcare systems.
The big comment I’d add though is that it is easier to leave a college nursing program than to join it. It has a progression of class work and finishing on time (assuming you can successfully transfer in) if you didn’t start there is very difficult.
The other direction less difficult. She can take a prerequisite math class in the beginning of a nursing program and be well positioned to move over into the math department. And still possibly end up moving from that to a Masters in Public Health or Medical Administration and be the boss of the doctors and nurses eventually!
AI will do the crunching but there will still be need for those who understand what the results do and not mean and why.
She might also want to consider the different lifestyles. Many of the nurses I’ve known work irregular hours, like four 10-hour days or three 12s, and maybe different days week to week. Stats are more like 9 to 5, and sometimes work from home.
As Stranger pointed out, if she likes math, there’s also a pathway there to go more directly into AI/ML, which is where all the lucrative jobs are right now. But it’s also a cutthroat industry with a lot of competition.
I would frankly not recommend that anyone go into AI research with the goal of getting those “lucrative jobs…right now”. People are getting paid the same kind of superstar salaries that ‘quants’ were getting in the finance and investment banking industry in the pre-2008 financial crash, and the bottom is almost certainly going to fall out of that job market as they hype evaporates and companies like OpenAI turn out to not have any kind of viable business which supports their absurd overvaluation. Which is not to say that there isn’t value in learning about machine learning methods and the mathematics and methods behind generative AI modeling if you like that force its own sake (and again, it is heavily based upon statistics as well as linear algebra) or at ‘AI’ is going to turn into complete vaporware and go away; just don’t expect that there are going to be jobs with seven figure salaries for new college grads.
My first reaction to the OP was to imagine a Venn diagram, with Florence Nightingale (“an English social reformer, statistician and the founder of modern nursing”) prominently in the middle as a pioneering figure in both fields.
I didn’t even think of that when writing my first post but yes, Florence Nightingale was basically the founder of data science and quantitative visualization, and essentially (along with physician John Snow’s work on hygiene and tracing the source of cholera outbreaks) one of the principals of what we now term epidemiology and public health. Modern, evidence-based medical science and pharmacology is inherently underpinned by statistical methods to asses the efficacy of medical treatments and public health interventions.
I can’t speak much about statistics, but I know a lot of nurses. There’s a nurse shortage in the United States at the moment and traveling RNs are making $100hr in my area. Being a traveling nurse is a challenging role because you’re changing to a new medical facility every week or so, and you don’t spend a lot of time at home. Not a great choice for a married person but a young person without a lot of ties can make a great deal of money in a short amount of time. If wages remain similar your daughter would be well on her way to buying a house after working in this role for a few years.
I also know several nurses who have used their degree as a stepping stone to a more prestigious career. Some went back to school to become a mid-level medical provider (Nurse Practitioner), a step below a MD. Others transitioned into an administrative role (Chief Nursing Officer).
If it was my daughter I think I’d gently encourage the nursing degree.
My Niece has her Doctorate in Nursing and is a Professor at a NYC University. She also is a researcher for emergency preparedness. I’m vague on that part.
I’d encourage your daughter to use AI—like the ChatGPT Agent model—as a tool to help her explore her options. Think of it as her 24/7 career counselor who doesn’t need coffee breaks. If I’d had access to this kind of thing back when I was a 16-17yo kid trying to pick an undergrad program, I might not have wandered into mine like a drunk choosing a tattoo at 2 a.m.
If she’s torn between nursing and statistics, AI can help her get a much clearer picture of what those paths actually look like. She could ask:
• “What are the pros and cons of a nursing degree vs. a statistics degree?”
• “What kind of person tends to thrive in each field?”
• “What’s the typical workday like for a nurse? For a statistician?”
• “What are the burnout rates in each profession?”
• “What’s the average salary—and how fast can I move out of my parents’ house?”
• “Will I be saving lives or analyzing them?”
• “How much of my future involves spreadsheets versus bodily fluids?”
The great thing about AI is it gives fast, reasonably detailed answers—and it might even introduce her to career options she didn’t know existed. It won’t tell her what to do (unless she uses it like a magic 8-ball), but it will help her ask smarter questions and make her better informed.
Moderating: Remember the rule of don’t answer questions with “Google It”. This is far too close to that. He or his daughter could of course ask an “AI”, that doesn’t need to be said. I’m hiding your post and instructing you not to do this again.
Especially to informed speculation on how AI developments may impact the future career paths of those majoring in statistics.
My WAG remains that there will remain a need for those who know what are the right questions to ask and how to critically evaluate the answers. I suspect AI will be a powerful tool for good statisticians, not a replacement for them. But yeah @puzzlegal ’s guess would have a better basis!