data science

“I tend to think that data science exists in the middle of all of these things. So math, statistics, computer science, and the ability to write algorithms, engineering, and then hacking”

“I think that a lot of people who are interested in data science will, Decide that, you know, I gotta, I should go take a bunch of courses and read a bunch of books, and check a series of boxes, you know and I think that's actually the wrong approach. I think t…”

“I feel like the people who I've seen hired in as PhDs often have a sense of entitlement about their, how much they're going to have to be able to work on their own problems at the exclusion of the needs of the company.”

“I think a lot of bad data science happens because people are like, I don't really know how this algorithm works. I'm hoping when I press this button, something good comes out. And look, it converged, so it must be okay.”

“So I don't think it's necessarily that you start with a question. I think you should start with a need. You should first trigger out, what is the problem I'm actually trying to solve before I do anything else?”

“There is a lot more to be gained, especially in data science, for having done projects than having necessarily spent two or three years going in depth on one topic.”

“I think of data science as And the general modelization of everything in sight, including education, including getting a job insurance, health, it's a war. And we're losing. Like we are, this is a war of the people who have money and can go hire data scientist…”

“If a model's not working out, you should always ask yourself, can we just change the business? You know, as opposed to somehow optimally solving this problem, can we just stop doing this thing I'm trying to solve?”

“So at least in my practice of data science, the vast majority of the math we did was quite simple.”

“Data scientists as a job does deserve its own job title because these three things in one professional is new.”

“Your goal is to go get everything a hundred percent right and then publish, and if that takes a couple of years, then you take a couple years. Ah, in our industry, in, you know, in industry, clearly that's not gonna work. Ah, you wanna probably get to, like, 8…”

“If you've, if you're working in a, if you're doing data science and you need a good distance metric, this, look at the earth mover distance. I had never heard of it before, and it's a brilliant, brilliant piece of science that's, of course, decades old.”

“The data science product engineering trifecta was just a very, very powerful combination. And when you were lacking one, it didn't, it was unstable.”

“The thing that makes data science different from machine learning is not just getting epsilon better predictive accuracy on learning, you know, cat's faces from pictures. It's this thing where you interact with somebody from a different discipline, and then so…”

“If you're hiring a bunch of data scientists and then making them dependent on a different organization for engineering is basically Hiring some really expensive people, and then tying their hands behind their back, and you know, that's not a smart thing to do,…”

“Every task that you work on has kind of different different features work better or worse, and I spent, when I first was working as a data scientist, I spent all my time on this feature selection, and as you guys know, you try a hundred things and maybe five o…”

“And, ah, the shortage of data scientists will be solved by a combination of pragmatic education, practical education, and, ah, tools that automate the modeling process and automate data science to levels which are, right now, probably most of us in the room th…”

“So if you have to make a career choice to study data science today, I would rethink that. And I'm serious.”

“If you've actually built something that works, how quickly can you get it out into the business? Because that, that creates the feedback loop that that creates credibility and buy-in to continue sort of investing in the work that's going on.”

“Because it helps us to create this vocabulary of risk. It's a common language that enables us to have practical conversations across entire organization, whether you're a security practitioner or engineer, or you sit on the board or the audit committee to disc…”

“At least in software, enterprise software, the value of data science is, is hugely valuable as a source of differentiation. But it's only valuable if presented along with either a workflow problem or an infrastructure problem.”

“All data science And all AI and all ML is fundamentally vertically specific. Which is the dirty secret about AI and ML powered businesses. They are fundamentally vertical problems.”

“So I guess that it will also get to global manufacturing in the next 10 years, probably.”

“We see ourselves very much as a data company, and we thought that data science in the early days would be a really key part of our strategy, and it is, but the data engineering challenge is so immensely harder that kind of data science kind of, in terms of tea…”

“There's folks who build models to help machines make decisions, and there's folks who, like, build charts and help people, humans make decisions at a much lower frequency.”

“And I think that's sort of a neglected part of data science. Everyone talks about, like machine learning and building all of these data products, but I think lots of the impact, lots of the trust and usage of data actually comes from being able to answer to so…”

“Though ostensibly duality technologies is really about security and cryptography, we're actually very much heart and soul at, a data science company, and how we can enable analytics on data that is protected and sensitive.”

“Data science is not actually a job or a role. It's a literacy”

“I think one of the big changes would be, we'll move from this specialized and specialization to generalization. So some of the things that we consider specialization today, like data engineering, a large portion of what we call data science becomes basic engin…”

“I grew up as a physicist thinking that, you know, physics really won World War II, but now that I'm a data scientist, I realize that it was actually data science that won World War II, but that story was classified for about 75 years, which is the story of how…”

“I see more representation in, in data teams than I do for something, let's say like the, I don't know, hardware level programming. You know, which has just historically been, like, a lot of these, like, deep nitty-gritty computer science fields have been, like…”

“As soon as you centralize something, you will inevitably lose the deep expertise you can get from embedding, but as soon as you embed everyone, you lose that, like, you know, Center of excellence where everyone comes together and you set standards for your dat…”

“It should not be solved by adding more data scientists, because anyway, there's not enough data scientists, and if they don't understand the business, there's no point. It should be solved by finding a way for people from the business that are very much into d…”

“People Kind of go through the same iterations. New people come to the space or possibly maybe it's more the business side get really excited by the prospect of what AI or data science or neural networks, whatever iteration we're in can do. And then we kind of …”