101–110 of 164 posts
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).
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Did Neveu ever take a detour into physics? There is a well-known QFT model with his name on it. Incidentally, I have a copy of Loeve's old probability textbook - I wonder if it is too out of date to be any use.
Loeve is terrific. The stuff there hasn't changed or been much improved on. Some people regard Loeve's book as French that sounds like English or English written as French. But Loeve students Neveu and Breiman are easier starts. Basically we're talking graduate probability based heavily on measure theory and some on functional analysis. Last I heard of Neveu he was back in France and at least in part working on proba…
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
Good to know. I looked at this Berkeley course (along with some private offerings like General Assembly) and got the feeling that they really weren't worth the investment for a guy with a Math degree, a CS minor, and programming experience going back to childhood. But I think I'd like some kind of formal, credentialed program that would build on my existing linear algebra + software skills (and address the weaknesses…
Depending on what stats you want to do, there are some pretty decent MOOCs. No one is going to claim that Daphne Koller's PGM course is weak in anyway for example[1].
[1] https://www.coursera.org/learn/probabilistic-graphical-model...
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…
To be fair, resampling wasn't the key to our projects, but we were doing a lot of work understanding probability distributions which is not entirely unrelated.
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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 job that involves data in any way is being relabeled a data science job. Most of them are just generating dashboards and posters in Excel or Tableau for people who are data illiterate. I know many people with maths/stats/comp-sci backgrounds who end up in these sorts of jobs. “Just add a bunch of green up arrows and red down arrows, your manager will love it” was advice from a co-worker of mine. Sadly, she was…
What exactly is Data Science? It seems like such an overused term and the value of the subject really gets diluted for me when I see charts in Tableau being offered as examples of "data science". What's the difference between, say, a Master's program in Computer Science where one studies machine learning and a Master's program in Data Science? Am I wrong for thinking the Data Science program weaker?
What exactly is Data Science? Data Science and DevOps are both just labels for things people have been doing under more mundane terms for 40-odd years. Even Machine Learning is just a trendy buzzword for what used to be called Predictive Statistics.
I've never seen any stats text book or course discuss techniques for dealing with large amounts of data to any significant level, but in data science that is a core part of what you do.
I ran production systems before DevOps and after. Again, it's very different - prior to devops, there was no emphasis at all about using software engineering techniques to manage and deploy software. The most you'd get was some scripts maybe kept in source control if you were lucky.
Now I run an AI company, and a key part of the ML we use involves generating structured text files from images. I guess predictive statistics is technically a correct label, but the tools and techniques are so dramatically different that that thinking of them as separate fields is more correct than incorrect.
I'm a UC Berkeley alum. When I was there this was a course taken by humanity majors to learn some programming so that their Resume looks cooler. majority of STEM majors take CS 61A (SICP) or E7 (Programming in MATLAB). Just noting this as a context, this is not the class intended for CS majors; this : https://cs61a.org/ one is.
Earlier quoted context omitted.
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 job that involves data in any way is being relabeled a data science job. Most of them are just generating dashboards and posters in Excel or Tableau for people who are data illiterate. I know many people with maths/stats/comp-sci backgrounds who end up in these sorts of jobs. “Just add a bunch of green up arrows and red down arrows, your manager will love it” was advice from a co-worker of mine. Sadly, she was…
Earlier quoted context omitted.
What exactly is Data Science? Data Science and DevOps are both just labels for things people have been doing under more mundane terms for 40-odd years. Even Machine Learning is just a trendy buzzword for what used to be called Predictive Statistics.
I did stats before data science was a thing, and then ran a data science team afterwards, and it's dramatically different. I've never seen any stats text book or course discuss techniques for dealing with large amounts of data to any significant level, but in data science that is a core part of what you do. I ran production systems before DevOps and after. Again, it's very different - prior to devops, there was no em…
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).
/Cognitive scientist