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In my experience Data Science looks very little like it used to a few years ago, and the priority skill these days is good strong understanding of the basics and very good sense of judgement. To me, statistics is the absolute number one priority for any data scientist. You need to fully and deeply understand just basic concepts in statistics in order to translate what you see into action and do what you’re really the…
Data science is always a very overloaded term ever since it took off way back in the 2010s. One of, if not the most, durable definition of this that likely can also be the most valuable because it’ll probably land your jobs(even today) is being able to make decisions from looking at data that have an team wide(good IC like job security) scope at the least and company wide scope(very rich). Building that intuition is…
Foundations of Data Science (2020):
Data science is always a very overloaded term ever since it took off way back in the 2010s. One of, if not the most, durable definition of this that likely can also be the most valuable because it’ll probably land your jobs(even today) is being able to make decisions from looking at data that have an team wide(good IC like job security) scope at the least and company wide scope(very rich). Building that intuition is…
The discipline you are looking for is called Statistics
Related book by Blum, Hopcroft, Kannan: Foundations of Data Science (2020): https://home.ttic.edu/~avrim/book.pdf
It is very narrowly focused on the authors' research interest around theoretical results on spectral approximations.
I find their work very interesting but it certainly does not teach you the foundations you need to know to do data science
Data science is always a very overloaded term ever since it took off way back in the 2010s. One of, if not the most, durable definition of this that likely can also be the most valuable because it’ll probably land your jobs(even today) is being able to make decisions from looking at data that have an team wide(good IC like job security) scope at the least and company wide scope(very rich). Building that intuition is…
The discipline you are looking for is called Statistics
It's a pretty funny sentiment.
It's also, unfortunately, reflective of how even talented individuals pass through advanced accreditation programs without locking blades with - or at least entering a general understanding of - fundamentals. Old timey British boarding schools would have called it "Logic" back in the day.
I am reminded once more of the differences in MSFS and Xplane: one uses a statistical table based model based on existing aircraft, and the other uses laminar flow analysis, fluid dynamics, and physics. One of them tells you something about unknown aircraft, and the other most definitely does not.
In my experience Data Science looks very little like it used to a few years ago, and the priority skill these days is good strong understanding of the basics and very good sense of judgement. To me, statistics is the absolute number one priority for any data scientist. You need to fully and deeply understand just basic concepts in statistics in order to translate what you see into action and do what you’re really the…
In my experience Data Science looks very little like it used to a few years ago, and the priority skill these days is good strong understanding of the basics and very good sense of judgement. To me, statistics is the absolute number one priority for any data scientist. You need to fully and deeply understand just basic concepts in statistics in order to translate what you see into action and do what you’re really the…
In your opinion how did it look just a few years ago, and why do you think the field has moved towards a bigger focus on fundamentals?
That being said, domain knowledge is still very important but you just don't need nearly as much tribal knowledge (because RAG or a tuned LLM can wrangle the chaos of your company documentation) or broad technical knowledge (because if you understand stats and PCA congratulations, when the coding agent recommends probabilistic PCA you now get to learn what it is, why its good for your use case (maybe), and get the gist fairly quickly; you no longer need to know about it in the first place. That was the return on investment for breadth of technical knowledge: knowing what to even look into).
I won't say "the field has moved towards a bigger focus on fundamentals" though -- this is all just one internet person's opinion. No one is telling me this is happening I'm just realizing that these are the muscles I am flexing more nowadays and my deep breadth of technical knowledge muscles are not flexing as much any more. Like: you are now inundated with recommendations of various methods from a coding agent: you need to understand them quickly; fundamentals gives you that. At least I think so, but of course, maybe I'm wrong about this due to misunderstanding the actual skills involved in what I'm doing now.