221–230 of 415 posts
Calling it Data Science was a tell. Have you noticed how non-scientific things add "science" to the name to make it sound like it has scientific rigor? Data Science, Political Science, Social Science, Scientology Compare to Physics, Biology, Math.
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Well good luck then, in my experience the most free time I've ever had in my life was during college. I squandered massive amounts of that time doing things completely unrelated to education, and I definitely don't regret doing that. College isn't just about book learning after all. But still, BY FAR, college is the time of my life when I had the most free time to do whatever I wanted.
Middle and high school is where a lot of students learn to stop being curious due to a lack of time. College demands far fewer hours per day, but it can be hard to forget what was taught previously.
Also hard subjects at uni - there is only so much deep thinking you can do per day
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I think you hit the nail on the head there with the survivorship bias and the raised in a bubble comments. Most people are raised in a bubble because children generally can't cope with how messy and complicated the world is. And systems and companies that last a long time can point to how successful they were because of their good decisions while ignoring their equally bad decisions that really should have undone the…
>The older I get, the more I realize how fragile a lot of human systems really are, but I suspect it has always been this way and it won't change significantly any time in my lifetime I agree that human systems have always been fragile, but have long been papered-over by things like "decency", "tradition" and "doing the right thing" and in extreme cases, mobs with pitch-forks. I disagree that it won't change in our l…
Students of history and the arts can get an earlier exposure to this worldview. I think we engineering types can get too focused on technology and imagine everything is innovation and progress. You have to work uphill against your default interests to expose yourself to a longer view and consider that fundamentally modern people with modern minds lived for (many) thousands of years doing almost all the same cognitive things as us, just with different physical props.
Our lungs are constantly in flux as we breathe. But at the same time, we're just breathing and that doesn't really change until our end. I'd say human social systems are much like that.
I have never understood the what a good ML engineer couldn't do and a Data scientist could in _majority_ situations. When you need a decision to be made based on data its just common sense risk analysis added together with basic statistics. I feel some good field training in statistics(Look up Andrew Gelman) a couple of good courses on Linear, Bayesian Regression is all you need, rest is just engineering skill. The d…
I think the qualifying term here is "good". I've worked with a surprising number of MLEs that don't really understand gradient descent or how most models really work under the hood. They certainly couldn't implement most things from scratch if they needed to (neither could most data scientists). I used to think an MLE was a solid engineer who also had a strong quantitative and numerical computing background. The kind…
Application of Computational Stats/ML Models are not all that hard to aquire but essential. I think we need a fundamental rethink of how applied stats/ML is taught to engineers to make them effective. Here are a few things I can think of:
1. Getting a solid understanding of actually coming up with a simple enough model to do the job 2. Do Power Analysis to figure out how many samples we need. Creating datasets with Hard Negatives and overcoming sampling bias. 3. Using things like Multiple Regression to do EDA. i.e. using models as a tool vs the end goal to understand a problem space.
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Nope, nope and nope again. I refute this utterly, as a teaching academic. Contact hours at most universities are around 2-4 hours per week per 15-credit module. To gain a degree, you have to take 120 credits a year, typically two terms of 4 x 15 credit modules, or 8-16 hours of contact per week maximum with the entire summer off. You therefore have at least 24 hours a week to study on your own to bring your working w…
No personal attack taken but your experience and points fail to win me over. The difference probably belies in the rigor of the program. It sounds like you are working in a non-engineering based program. In our engineering programs we had 40 hours of class time + lab time per week. I had a concurrent arts degree at the same time which is was, in comparison, incredibly light workload - though concurrently it took time…
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>Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. One of the things I don't like about statements like this said in a Data Science context, is that they are true outside of Data Science as well. Executives make big decisions, managers make smaller decisions, nobody can evaluate how good/bad they really were for months or years. Engineers build…
Not to get too off topic, but as a 35 year old engineer it seems the world in general has far fewer consequences than I was raised to expect. Everything from businesses with bullshit ideas flourishing at a loss, to January 6 even being possible (politics aside I expected the Capitol Police to crack a lot more skulls than they did once people started smashing windows), to the whole FTX situation and the tepid response…
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Not to get too off topic, but as a 35 year old engineer it seems the world in general has far fewer consequences than I was raised to expect. Everything from businesses with bullshit ideas flourishing at a loss, to January 6 even being possible (politics aside I expected the Capitol Police to crack a lot more skulls than they did once people started smashing windows), to the whole FTX situation and the tepid response…
I think you hit the nail on the head there with the survivorship bias and the raised in a bubble comments. Most people are raised in a bubble because children generally can't cope with how messy and complicated the world is. And systems and companies that last a long time can point to how successful they were because of their good decisions while ignoring their equally bad decisions that really should have undone the…
> Nobody knew or even cared what the difference was between good and bad data science work. Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. This seems to be a problem with the industry as a whole. I'm speaking as a SWE, but I've observed similar things with PMs. I don't think it's impossible or even very hard to appreciate the right things, it…
The biggest, hardest bridge to cross was an appreciation of the importance of metrics. For some reason, getting a business person to grok something as simple as precision/recall/F-beta is a near-impossible task. You can do multiple presentations on it (after having honed those presentations over years with multiple manager audiences), and it never sticks. It's always "what's the accuracy?" It's impossible to do good work when your bosses insist on measuring and therefore optimizing for the wrong thing (which in my experience consulting for multiple Fortune 500 businesses, they always do).
Even worse, many organizations have such broken politics/cultures that the managers can't even tell you the big picture of what the project is trying to accomplish. Once you finally piece it together from the people who know their roles in-depth, it becomes clear that what they're trying to do is totally infeasible. At least that was my experience more than half the time.
If a 23 year old manages to get a data science job at a startup, and then actually delivers the results that the start-up expected of them, the learning experience there is infinitely more valuable than going to Google and using a bunch of tools that don't exist in the real world, on unrealistic timelines because you're not on the ads team and don't need to make money.
You can go learn "best practices" later, but working in a startup is an exercise in pragmatism. You deliver results, or you die.
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>Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. One of the things I don't like about statements like this said in a Data Science context, is that they are true outside of Data Science as well. Executives make big decisions, managers make smaller decisions, nobody can evaluate how good/bad they really were for months or years. Engineers build…
> One of the things I don't like about statements like this said in a Data Science context, is that they are true outside of Data Science as well. Executives make big decisions, managers make smaller decisions, nobody can evaluate how good/bad they really were for months or years. Engineers build something amazing, or build a house of cards, nobody cares as long as the money people are happy, even if the business use…
When it comes to disastrous long term decisions, there's plenty of time to get input from multiple stakeholders. I always remember the armies of companies who went chasing after Hadoop because Big Data was going to transform something or the other. All the stakeholders were on board, from the CEO and CTO to IT and Engineering management. How much money and time got flushed down the toilet trying to implement and extract value from data with Hadoop. They only people who paid the consequences were the employees at Hadoop companies who thought their stock options would be worth something.