How far in data science can you go without a PhD or even solid math background?
I was never even taught linear algebra but I've been doing DS R&D (basically very fancy feature engineering) based...

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i run a 60m analytics practice with a ba :)


on and improving on academic papers, for a client problem.
I've been able to get away without the deep math stuff cause I'm a good coder and I can quickly understand the intuition (and algorithms too) of a lot of ML so long as I stay away from the actual mechanics of the math.
I get lost when we go as deep as stuff like the difference between the different ways PCA is calculated (which, I know, isn't that deep) or how exactly deep learning *really* works.

I've been able to do good work but I think I have some serious imposter syndrome when looking at exit opportunities for AI/ML


👆🏻 this.

You can’t really understand the full functionality behind an algo without the math. I was told in my data science career that you should never use an algo unless you understand the math because they can too easily be misused especially if they are preprogrammed in some capacity.

But I will say that a PhD is not the requirement to understand the math.


The math in applied ML/DS is not very advanced, mostly college freshman level calculus, stats, and lin alg. You can very easily make up for it with a couple of online courses. Source: was math major in college and did MS in CS/ML.


The PhD is only *necessary* for a small fraction of employees when you really need something to work and can't afford a pilot. When you need theoretical justification with guaranteed error bounds etc. Or when you have a true research team which has a need to employ theorists.

In practice, businesses don't run this way. A pilot is enough for almost all situations and pure research is extremely risky as monetization goes. Most don't have a need for PhDs who can casually use terms like "Hilbert space." Learning to read the rest of the math and even experiment is most often attained via mathy masters (usually) or bachelors. Don't have to be a physicist or applied math major either, softer ones like econ or analytics will do. Pure CS is a tossup since someone could've "skipped the math" in that, though there are advantages for cleaner code and deployment there.


Problem formulation and related statistically valid data preparation is generally a business analytics / product management problem and does not require a PhD. When it does, just any PhD won't do: don't hire a physicist to work on agricultural products when you need that level of domain expertise.

You're right that doctors have a tendency to expect special treatment more though, which is a reason against hiring if anything.


True. Not required. There are now math degrees focused on understanding algorithms, and many computer/robotics engineering degrees delve into them enough to gain from self study.


There's all these schools like Galvanize, Flatiron School, and a few others that claim data science specialty. However, they say you do not need a Ph.D. to apply but the audition interview processes are rigorous enough to require it. I required data science for my program because at the end of my masters I knew I'd need it. Those extra data science schools are appealing if I ever get an honest permanent employer that's not attempting to throw a bunch of ego at me to afford it. They might go beyond current skills. I like how many of them bring in employers and allow pay as you earn while they do their own recruitment. They have different associated employers too. Galvanize is directly related to Elon Musk businesses and it's either Flatiron or Thinkful that is directly related to both Boeing and Northrup Grumman. While I'd like my AI monitored and controlled dam to come to fruition, the fish population counts and some of the diving tourist attractions in Spain mean they are a great start for sharing knowledge with Turkey about how to administer their upcoming dam, so instead of those routes for the ML, I agree with Elon that it's time to start working on some AI policy, including finding avenues for case law. In that regard, I don't need it except for SQL or Python inquiries into the case law, which I still don't need THOSE schools for it since I know that on my own studies :). I have a few more case law agendas to work through. Dam, that guy is now looking like an ego jerk, I am really starting to like Elon, despite long family histories. I'm way better than all that, lol.

I couldn't agree more about the area-specific stuff. I have had multiple experiences with law firms, big and small, and in pro bono affairs. I've had courses in law, administrative justice, and regulations. I also have experience preparing permits, writing permitting manuals, and writing policy. My Ph.D. is in Business Management with a leadership specialization, so it says leadership on the degree. My expertise is needed for data extraction, evaluation, reconciliation, interpretation, and strategizing. So, creating a new department at a law firm, starting a metrics program, and generating strategy to more smoothly grow a law firm. Its also good for politics and designing prism for locating additions to case law to use for developing policy to transfer into politics. However, if the goal is just for reporting dashboards unless you need to interpret for anomalies then investigate the anomalies, it's probably less expensive to hire someone with fewer credentials. However, if it takes multiple people with fewer credentials to do what you need and a Ph.D. can do it themselves and possibly more, it names zero sense to go the extra labor route since that ends up way more expensive.

All that being said, I wanted to add ->. I am not good at plant identification beyond Brazilian nuts and orchids, a squirrel is a squirrel unless it flies, cows can have as many shaped spots as they they, they are just a cow, but horses, you know what??? That's a love story :)

You’re fine for any type of strategy role but depends on what you want as a exit

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