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I'm afraid that this problem will only be exacerbated by the influx of "data scientists" that may know how to implement linear regression and a few machine lear

I'm afraid that this problem will only be exacerbated by the influx of "data scientists" that may know how to implement linear regression and a few machine learning models, but lack the statistical expertise to design experiments, vet assumptions, and verify results. This comment is not meant to disparage anyone who considers themselves a data scientist. However, as someone who has advanced degrees in both statistics and computer science, employers and recruiters outside of R&D roles have shown very little interest in my statistical background aside from my machine learning experience. My experimental design, data handling (not just data cleaning, but data collection) skills, and theoretical understanding are rarely discussed. Statisticians are compensated much less than programmers--maybe deservedly so--but to that extent that I'm compensated the same as a "data scientist" who only studied computer science and didn't study any statistics, I feel like many employers, even those who benefit heavily from them, don't properly compensate statisticians. At my alma mater, the poor wages have really hurt the statistics program as more students have decided to enroll in the "data science" (typically housed within business or computer science departments) programs. I think this is a really unfortunate trend because while those programs teach you how to implement gradient descent, do basic data wrangling in Python and R, make data visualizations, etc. they don't teach experimental design or the statistical theory that drives applied statistics. Perhaps as the supply of well-trained statisticians decreases and demand increases there will be upward wage pressure, but I think it's more likely that unqualified and inexperienced "data scientists" will continue to be shoehorned into these empty roles instead.

Eridrus 8y ago
I'm curious, what are the best examples of where your statistics knowledge was useful? What do you think practicing data scientists should learn to be more effective? Experimental design? Something else?
- o10449366 8y ago
  I could talk about a lot of specific circumstances, but in the interest of brevity I'll say this: The reason I believe a well-rounded statistics education is so valuable is because it ultimately teaches you to think critically about questions and answers. Good experimental design is driven by a solid understanding of theory--what statistical assumptions must be satisfied, which ones can be reasonably assumed or violated, and whether they're realistic to achieve. I don't believe you need a PhD to call yourself a scientist, but I do think one common trait most scientists share is curiosity. To that end, I would encourage practicing data scientists who might not have a formal statistics education to not shy away from statistical theory. A solid theoretical understanding is what guides you when the questions and answers aren't clear--and I think the fundamental shortcoming of many data science programs is that they prepare their students for extremely simplified (and therefore unrealistic) questions with easily obtainable answers relative to what will be encountered in the real world. I apologize for giving you an answer that isn't as coherent as I would like to it be. I tried not to be too verbose, but I think I failed at that anyway. I have a lot of feelings on this topic that I haven't fully articulated to myself yet. I could answer your first question if you're still interested, but as an addendum to my original comment, even though my statistics degree has gained me nothing in terms of career advancement or an increase in salaries or opportunities, its been truly invaluable to me as a programmer and a public speaker and advocate for the critical thinking skills it taught me. I hope others continue to recognize statistics value in academia and are curious of it.

Eridrus 8y ago

I'm curious, what are the best examples of where your statistics knowledge was useful? What do you think practicing data scientists should learn to be more effective? Experimental design? Something else?

o10449366 8y ago
I could talk about a lot of specific circumstances, but in the interest of brevity I'll say this: The reason I believe a well-rounded statistics education is so valuable is because it ultimately teaches you to think critically about questions and answers. Good experimental design is driven by a solid understanding of theory--what statistical assumptions must be satisfied, which ones can be reasonably assumed or violated, and whether they're realistic to achieve. I don't believe you need a PhD to call yourself a scientist, but I do think one common trait most scientists share is curiosity. To that end, I would encourage practicing data scientists who might not have a formal statistics education to not shy away from statistical theory. A solid theoretical understanding is what guides you when the questions and answers aren't clear--and I think the fundamental shortcoming of many data science programs is that they prepare their students for extremely simplified (and therefore unrealistic) questions with easily obtainable answers relative to what will be encountered in the real world. I apologize for giving you an answer that isn't as coherent as I would like to it be. I tried not to be too verbose, but I think I failed at that anyway. I have a lot of feelings on this topic that I haven't fully articulated to myself yet. I could answer your first question if you're still interested, but as an addendum to my original comment, even though my statistics degree has gained me nothing in terms of career advancement or an increase in salaries or opportunities, its been truly invaluable to me as a programmer and a public speaker and advocate for the critical thinking skills it taught me. I hope others continue to recognize statistics value in academia and are curious of it.