31–40 of 164 posts
People at Berkeley view this class as kind of a joke. The average grade is insanely high and the topics are covered in much less depth than just the normal intro cs or stats classes. https://www.berkeleytime.com/grades/?course1=7765-all-all
Okay, here's a view of what appears to be part of the course: We have a course (right a school application of stuff taught in school!) with two teachers, that is, two sections of the course, each section with its own teacher and its own students. At the end of the two courses, that is, the two sections, we want to compare the teachers. So we give the same test to all of the students from both courses. Suppose one sec…
Earlier quoted context omitted.
Boot camps are stupid. Go to community college. It's ridiculously cheap, and the credits are worth something.
If you can learn skills that get you a job from a boot camp they’re not stupid. The fact that Lambda School and App Academy don’t get paid unless you get a job and they still exist suggests rather strongly that they get people jobs.
It seems very short sighted; community college only takes 2 years. Your career lifetime is what, 45 years? The ROI is insane, why would you shortchange yourself?
Earlier quoted context omitted.
Boot camps are stupid. Go to community college. It's ridiculously cheap, and the credits are worth something.
How good a community college is often dependent on how wealthy its county is. Secondly, a data science course may be offered, but only during fall semester and everyone else wants to sign up for it. Also bootcamps can compress the curriculum from two years worth of junior college into 8-12 weeks. For someone who never enjoyed being in school, I'll take the bootcamp.
Right, and I got a doctorate by listening to an audio tape.
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I have also found this interesting. What I don't understand is that the amount of data science jobs are no where near the levels that people make it seem. I am not sure where all these people will end up working if they want to be a data scientist. There is not a need to hire huge teams of data scientists like you might for dev roles, it doesn't scale the same way.
Every graduate that doesn't find a high paying job in the field is an excellent candidate for the next level of education. After you have given a university hundreds of thousands of dollars and a decade-plus of your life, you will then be ready to teach the next crop of students.
1. You really think Berkeley's top-ranked PhD program is recruiting people who couldn't find jobs? No. Not only can 100% of successful top-tier PhD applicants find jobs, 100% of them are strong candidates for the top echelon on entry-level jobs.
If you disagree, go look up the people in Berkeley's CS PhD program. Point out a single person you think didn't turn down mid-100s job offers to attend Berkeley.
Getting into one of these top-5-to-10 PhD programs is no small thing...
2. MOOCs aren't PhD programs and in nearly all cases aren't designed to feed into PhD programs.
3. Finally, at least in CS, at least for the moment, educators are in extremely high demand. And again, at least for CS, that demand isn't being manufactured by the academy.
Okay, here's a view of what appears to be part of the course: We have a course (right a school application of stuff taught in school!) with two teachers, that is, two sections of the course, each section with its own teacher and its own students. At the end of the two courses, that is, the two sections, we want to compare the teachers. So we give the same test to all of the students from both courses. Suppose one sec…
There's a lot of value in your posts. Mathematizing problems, when successful, brings elegant solutions with well understood properties. Hence, I don't understand the downvotes you are usually getting. I'm a pure CS / logician by training, but I've spent a few years trying to expand my expertise into probability theory and stochastic processes. Lots of your advice resonates with me. My MSc advisor recommended I shoul…
Neveu is elegant beyond belief. I keep my copy close. I was aimed at Neveu by a star student of E. Cinlar, long at Princeton and before that at Northwestern -- long editor in chief of Mathematics of Operations Research. Neveu was a student of M. Loeve at Berkeley. So was the current darling of machine learning, L. Breiman, because of his Classification and Regression Trees (CART). Breiman's Probability as published by SIAM is generally easier reading than Neveu.
For stochastic processes, there are several relatively different directions to go.
Martingale theory is gorgeous, astounding, amazing, with one of the most powerful inequalities in math, the astounding, tough to believe, martingale convergence theorem, and likely the shortest proof of the strong law of large numbers.
Then can do Markov processes more generally. The discrete state space version is important and not too difficult -- Cinlar has a nice introductory text.
A high end direction for Markov processes is potential theory. There are claims that that is the math for exotic options on Wall Street, but I doubt that there have ever been any applications.
There is a big role for second order stationary stochastic processes in electronic engineering. I ran into that for processing ocean wave data for the US Navy. Here the fast Fourier transform added a lot of interest.
And there's more.
Generally long Russia, France, and Japan seemed to have emphasized stochastic processes more than the US. But by now I suspect that the US is well caught up.
I'd have a tough time believing that very many people with money to hire know enough about high end stochastic processes, or even just Neveu, to hire in those fields. US national security may be about the only hope, that is, outside of academics.
Yes it appears that some of the quantum field theorists in physics are interested in path integrals.
Uh, I'm disorganized here: There is the field of stochastic optimal control!
As usual for advanced applied math, my suggestion is, outside of academics or US national security, find a valuable application and start a business to make money. That is, don't expect to be hired.
Earlier quoted context omitted.
Every graduate that doesn't find a high paying job in the field is an excellent candidate for the next level of education. After you have given a university hundreds of thousands of dollars and a decade-plus of your life, you will then be ready to teach the next crop of students.
> Every graduate that doesn't find a high paying job in the field is an excellent candidate for the next level of education. 1. You really think Berkeley's top-ranked PhD program is recruiting people who couldn't find jobs? No. Not only can 100% of successful top-tier PhD applicants find jobs, 100% of them are strong candidates for the top echelon on entry-level jobs. If you disagree, go look up the people in Berkele…
I find it curious that there are so many courses for data-science related subjects, which superficially seem to cover the same material, and relatively few courses covering more traditional CS topics such as computer systems, networks, OS. I suppose it has to do with the market, but also feels like colleges are skating to where the puck is, rather than where it will be (or perhaps, where it could be).
MIT open courseware has a bunch of classes related to traditional CS topics. Also just searching universities you can find some. Data Science is the new hip class and it’s just very aggressively marketed. The other one being marketed heavily is intro to coding .
Okay, here's a view of what appears to be part of the course: We have a course (right a school application of stuff taught in school!) with two teachers, that is, two sections of the course, each section with its own teacher and its own students. At the end of the two courses, that is, the two sections, we want to compare the teachers. So we give the same test to all of the students from both courses. Suppose one sec…
Can you or someone else TL;DR that for us ADHD folks?
The idea of resampling is just dirt simple; read it again, just the first paragraphs, and you'll get it.
I find it curious that there are so many courses for data-science related subjects, which superficially seem to cover the same material, and relatively few courses covering more traditional CS topics such as computer systems, networks, OS. I suppose it has to do with the market, but also feels like colleges are skating to where the puck is, rather than where it will be (or perhaps, where it could be).