I feel your pain. I was lucky enough to be around to enjoy the days when the ONLY place online where people were discussing serious topics (and non-serious ones!) was USENET (and maybe The Well). Google Groups just started to throw a web interface on USENET.
Sadly, once big money entered the Web in the late 90's, every Portal popping up felt like they had to include discussions/message boards into their features.
Yahoo Groups was big in the day and is now essentially dead. Same with Facebook, etc.
Now everything is very fragmented. There are some really great discussions happening out there (metafilter.net is a good example) but they are hard to find.
Reddit might be the closest thing to a groups/USENET replacement that we have.
I'd love to hear about anything else out there that may save civil discourse, but I have my doubts it exists (or will). (although web3 may save us yet - nostr is pretty interesting)
After all, what does it mean when 90% of people spend their time on Instagram and Tiktok?
Thanks, this was exactly the kind of finding i was asking for. So, at least at this stage of life, sounds like majority of your cohort is still employed. I wonder if that will hold up ten years from now.
I guess what I'm really worried about is that many are somehow "forced" into retirement or low paying jobs (ala Walmart greeter). This would seem really unfair, given that on the surface, this kind of age discrimination shouldn't happen. At least the military is up front about how this works with it's "Up or Out" policy.
Thanks for the insights, but what I'm really curious about is what happened to the older devs that DIDN'T go into management? Given that you say "whats left" doesn't include anyone over 45, where did those guys go? Given that there is not enough management jobs for all those guys to go into, what did they DO?
Thanks for this story - always great to hear about a real experience. This is very disturbing. What the heck does "fresh face" mean? Did he have any idea what they meant, or were they just looking to cut costs and hire a more junior guy?
I assume I'll still be working, but when I look around and see how few people at these large, established companies are in their 50s/60s, I get a little nervous to be honest...
Good point, but I'm seeing this phenomena in ALL jobs at these companies - and both have been around for over 30 years. There just aren't alot of older people around - where did they GO?
These aren't SF startups (see above). I see plenty of 30's and quite a few 40's, but where are all the 50's and 60's? These days I don't think many are passing away at that age, right?
Interesting - maybe it varies by industry. What industry are you in? These two companies are in telecom and software. I don't think many people in the US are retiring at 50 though - maybe in Europe ;-)
Thanks for your comments guys, but if this is true, why don't I see more workers in their 40s, 50s, and 60s around? (and I'm not specifically speaking of devs, although the effect seems more drastic there).
And I don't work at a startup - this is the 2nd large company (>20k employees) I've worked at, and in both cases, I would say that the percentage of workers over 50 is less than 10% - and most are managers or senior folks.
My question is simple - where the heck did all these people go? I don't think the mortality rate is that high ;-) and I doubt many people can afford to retire that early.
Just checked out Mitchell's site. Looks like he's been working on a 2nd ed for awhile. he should just open-source the first one since it's so old. But I found he just released all the vids from a ML class he gave in 2011 - wonder if they are as good as Yaser's? http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
Have to agree. And it's very inexpensive because Yaser refused to give-in to academic publishers, who would've charged the typical $70-80, and self-published so he could offer it for less than half the cost.
Not only is the book great, but his lectures are PHENOMENAL. He breaks concepts down in such a careful, accessible way. Its a bit late to join the online course, but you can see all the lectures on YouTube (work.caltech.edu/telecourse.html) or iTunesU (I prefer the latter, using the app on iOS - awesome b/c you can bookmark and record notes at those marks - otherwise I notice these video types of courses are way less useful - no way to review - wish Coursera/Udacity/EdX had that feature.)
Yaser is an awesome guy btw - he's very active on the forum (see the link from the above caltech site - on right hand side). He is very gracious with his time - I'm not a CalTech student, and yet he has answered all my questions and even helped me find a tutor for the course that was a previous student at CalTech (I live in Pasadena). He truly cares - and that comes off in the lectures as well. Enjoy!
Thanks, @gtani, great resource. Yeah didn't mean to imply that ML techniques are free of ANY assumptions, just that several of the popular ones like logistic regression don't have distributional assumptions. (actually, I really want to understand the VC Inequality at some point, as it seems to allow us to make conclusions about out of sample error rates without depending on distributional assumptions)
Great suggestion. I've been amazed to find out that many coders and amateur "data scientists" don't realize that testing the assumptions is an important part of conducting statistical analyses. Part of this may be due to the recent emphasis on machine learning techniques, which tend to be assumption-free (often just assuming independence of cases in the sample).
Great effort, and I certainly hope more coders will get into statistics (most I know are only interested in machine learning). However, I think your definition of 1.3 "Confidence Interval around the Mean" could be improved. You state:
"A confidence interval reflects the set of statistical hypotheses that won't be rejected at a given significance level. So the confidence interval around the mean reflects all possible values of the mean that can't be rejected by the data."
That seems a bit vague and perhaps confusing. Might I suggest something more like this:
"The confidence interval specifies a range (+/- a multiple of the above standard error [SE]) around our estimate of the mean (x-bar) such that: if we repeated our sampling process an infinite number of times (i.e. with the same sample size and forming a new x-bar and SE each time [and therefore, a new confidence interval]), Confidence_Level% of those intervals would contain the population (true) mean."
In addition, I think in this case, at least, there are no assumptions about the data to worry about, given a sufficiently moderate sample size due to the Central Limit Theorem (I'm confident about that in the case of the mean (x-bar), but I'll leave it up to others to correct me if I'm wrong about this applying to the standard error (SE)).