One who has mastered knowledge of...
Simple. Data scientists come up with formulas to represent an environment of data. They know how to fine tune the model and know how to respond to unfamiliar results. BI simply plug data into variables and report it to a BU. Source: former data scientist
OP is asking what it takes to create that representation
No they’re not, they’re asking where the separation line is and I clearly presented it with the assumption that a true data scientist knows how to apply the mathematical concepts in the survey to this framework. Picking one of the responses is an insufficient response to the underlying question.
I know some of these words
Surprised Bayesian theory is so high up there. Being truly Bayesian is very hard and almost no one actually does it. Or are those votes coming from people who don't actually know their way around Bayesian stats, but have memorized the conditional probability formula and on rare occasion use naive Bayes?
Without statistical computing and understanding of probability/stochastic processes, there is no data science. You won't be able to solve problems at scale. Linear algebra is a runner-up, but I can imagine someone doing flips to avoid writing it out themselves, and usually succeeding. Everything else is interchangeable, though at least a few boxes should be checked per individual.
Real data scientist make sense from nonsense. Fake data scientist make nonsense from sense.
Quick off topic but related question- do you have to have a pHD to be a data scientist?
No, but good luck getting interviews for really good roles
Yeah, it’s really hard without previous DS roles at top companies or a phd.
How did the voters in this poll develop a solid grasp on the theory behind ML models & NNs mathematical statistics optimization theory stochastic processes probability theory without the necessary prerequisites in linear algebra, diff eqs., and real analysis? It seems contradictory.
I was thinking the same thing. Stats, prob, Lin alg, and optimization will get you pretty far.
Having used it in the past to prove something to yourself doesn't mean you still actively use it. So much is prewritten that you can escape having to actually use any "actual math" on your own directly once you're out of school, even as a researcher. Not saying it won't handicap you, but it's possible to get by
Additionally, it's interesting to observe that the top choices tend to form a cluster of nonparametric techniques (& modern statistics), while people in tech (CS-heavy background?) apparently de-emphasize mathematical analysis and scientific modeling from traditional physics or Wall Street.
You need hard science PhD and python. That’s all. The rest is bs
Probably any reasonable subset of the above would indicate the ability to learn enough of the rest on the job to succeed. Can any real DS comment on the most advanced thing they had used?
I don’t think that it’s about how advanced stuff you use, but about having a deep knowledge of the underlying theory, in order to know when to apply those methods. I think that this is the most underrated skill of a data scientist, and often the hardest. For me, the most advanced thing was to create an algorithm that turned into a paper at a top-3 ML/DL conference.
And just to be clear: most tech companies have different data scientist roles, such as; product and research.