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Stanford, Harvard data science no more

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Re: Stanford, Harvard data science no more

#51

Post by mturmon »

simplotek wrote: post #40
mturmon wrote: post #38

Earlier quoted context omitted.

You can sidestep calculus by just using the discrete setting rather than a continuous one. If you want to introduce continuous distributions like the Gaussian one, you can just say "area under the curve" if you need to connect the density to a numerical probability. They don't have to know how to do the integral, in the case of a Gaussian, it's just tabulated anyway. I'd argue that you could teach a perfectly reasona…

> If you want to introduce continuous distributions like the Gaussian one, you can just say "area under the curve" if you need to connect the density to a numerical probability. What name do you give to this "area under the curve", or the "rate of change" of this area? They are pretty fundamental concepts with important and basic properties, which affect things like local optima and minimization, and expected value a…

You don’t need integration to define expected value or covariance in the discrete case. TBH I’m not sure if you can get around integration in the general continuous case or not.

If not, you could use some limiting argument to handle the moments of a continuous uniform RV, at least, in terms of the discrete analog.

You don’t need calculus to derive least squares estimators. You can follow the logic in this quora answer [1] to show that (e.g.) the mean is the minimum MSE estimator among constant functions, and that the conditional mean is the minimum MSE estimator among “general” (measurable L2) functions.

This derivation is familiar to many who have studied these concepts. It’s clever, it does not need differentiation, just expectation and logic.

It could be that your studies in probability were done using a certain pedagogical path, and that’s blinding you to the fact that other paths are possible.

[1] https://www.quora.com/Why-is-minimum-mean-square-error-estim...

Re: Stanford, Harvard data science no more

#52

Post by theGnuMe »

simplotek wrote: post #33
drewcoo wrote: post #26

Earlier quoted context omitted.

> Just because ChatGPT can do it, doesn’t mean that it isn’t valuable for a human to learn. No, but it does sort of suggest that, doesn't it? > This is especially true for foundational courses. Sure, but calculus is about memorizing ways to answer problems. We're not talking about real analysis, the course in which students develop the calculus and prove it works.

> Sure, but calculus is about memorizing ways to answer problems. It really isn't. That might be how some people managed to get a passing grade, but clearly they learned nothing and squandered a once in a lifetime opportunity to get to know it.

I disagree.

Re: Stanford, Harvard data science no more

#53

Post by xtiansimon »

comte7092 wrote: post #29
roddylindsay wrote: post #25

A high school "data science" course, if designed properly, will be far more useful to students and beneficial to society than calculus. Every high school student should learn how to grapple with uncertainty, how to evaluate statistical claims and experiments, how to interpret graphs and charts, understand how machine learning models work (at a high level), and internalize concepts like "significance", "error bars", a…

> Every high school student should learn how to grapple with uncertainty, how to evaluate statistical claims and experiments, how to interpret graphs and charts, understand how machine learning models work (at a high level), and internalize concepts like "significance", "error bars", and "expected value." Pet peeve: can we just go back to calling these things statistics? While I agree with you that statistics should…

Wouldn’t a data science curriculum be more multi-disciplinary than a ‘statistics’ course?

Visualization, scripting, data collection, models, simulation. EDx had a great course by Guttag and Grimson. Add to this Scott E Page’s Model Thinking. Add EDx Data Analytics and Learning From UT Arlington. And some Tufte.

I say these because i work in the accounting field and brought scripting to my firm from my own self-study. It’s been a super power for me, and solved several problems which my colleagues had tackled using Excel alone.

I’ve also studied statistics, but found it less generally useful.

Re: Stanford, Harvard data science no more

#54

Post by westurner »

westurner wrote: post #16

Do Stanford or Harvard have a UC BIDS: UC Berkeley Institute of Data Science? > This is the [open] textbook for the Foundations of Data Science class at UC Berkeley: "Computational and Inferential Thinking: The Foundations of Data Science" http://inferentialthinking.com/ (JupyterBook w/ notebooks and MyST Markdown) https://data.berkeley.edu/ : > [#1 Undergrad Data Science program, #2 ranked Graduate Statistics progra…

Data literacy: https://en.wikipedia.org/wiki/Data_literacy :

> Data literacy is distinguished from statistical literacy since it involves understanding what data means, including the ability to read graphs and charts as well as draw conclusions from data.[6] Statistical literacy, on the other hand, refers to the "ability to read and interpret summary statistics in everyday media" such as graphs, tables, statements, surveys, and studies. [6]

Data Literacy and Statistical Literacy are essential for good leadership. For citizens to be capable of Evidence-Based Policy, we need Data Driven Journalism (DDJ) and curricular data science in the public high schools.

https://news.ycombinator.com/item?id=20173228

Re: Stanford, Harvard data science no more

#55

Post by comte7092 »

xtiansimon wrote: post #53
comte7092 wrote: post #29

Earlier quoted context omitted.

> Every high school student should learn how to grapple with uncertainty, how to evaluate statistical claims and experiments, how to interpret graphs and charts, understand how machine learning models work (at a high level), and internalize concepts like "significance", "error bars", and "expected value." Pet peeve: can we just go back to calling these things statistics? While I agree with you that statistics should…

Wouldn’t a data science curriculum be more multi-disciplinary than a ‘statistics’ course? Visualization, scripting, data collection, models, simulation. EDx had a great course by Guttag and Grimson. Add to this Scott E Page’s Model Thinking. Add EDx Data Analytics and Learning From UT Arlington. And some Tufte. I say these because i work in the accounting field and brought scripting to my firm from my own self-study.…

>Wouldn’t a data science curriculum be more multi-disciplinary than a ‘statistics’ course?

I would say yes, however, the items listed in the comment I quoted fall squarely within the realm of statistics. I don’t have a problem with calling a curriculum of statistics + data manipulation tools “data science” but that’s not what’s realistically being covered in these high school programs.

Re: Stanford, Harvard data science no more

#56

Post by simplotek »

theGnuMe wrote: post #52
simplotek wrote: post #33

Earlier quoted context omitted.

> Sure, but calculus is about memorizing ways to answer problems. It really isn't. That might be how some people managed to get a passing grade, but clearly they learned nothing and squandered a once in a lifetime opportunity to get to know it.

I disagree.

> I disagree.

Ok, thanks for your contribution.

Re: Stanford, Harvard data science no more

#57

Post by xtiansimon »

comte7092 wrote: post #55
xtiansimon wrote: post #53

Earlier quoted context omitted.

Wouldn’t a data science curriculum be more multi-disciplinary than a ‘statistics’ course? Visualization, scripting, data collection, models, simulation. EDx had a great course by Guttag and Grimson. Add to this Scott E Page’s Model Thinking. Add EDx Data Analytics and Learning From UT Arlington. And some Tufte. I say these because i work in the accounting field and brought scripting to my firm from my own self-study.…

>Wouldn’t a data science curriculum be more multi-disciplinary than a ‘statistics’ course? I would say yes, however, the items listed in the comment I quoted fall squarely within the realm of statistics. I don’t have a problem with calling a curriculum of statistics + data manipulation tools “data science” but that’s not what’s realistically being covered in these high school programs.

> concepts like "significance", "error bars", and "expected value."

Yes. I see what you're responding to--these are squarely in the statistics domain.

> not what’s realistically being covered in these high school programs.

Yes. Where the rubber meets the road. Who exiting from higher education now will have the skills to teach this imagined hybrid course? Realistically, they have to be vetted and hired by the mathematics department and satisfy some state and/or federal standards of education, which are currently staffed by educators who themselves are following standards of their office.

I was responding to the OP's premise:

> "data science" course, if designed properly, will be far more useful to students and beneficial to society than calculus.

Whether or not that objective is "realistic" given the current boundaries perscribed for high school education is another matter.

There is hope; there are modern thinkers in education out there. I referenced the UT Arlington course students and instructors referred to as DALMOOC (google it). I took this course thinking it was another data science course, and found a course taught by teachers for teachers. I hung in because their ideas were so fresh and interesting.

DALMOOC's ambition was to train teachers to encourage students to use social media to communicate their learning results, and in turn produce the data that the teachers were being traind in the course to analyze using social media analysis techniques. DALMOOC professors encouraged participants to generate social media responses to DALMOOC coursework. Very modern. Not sure how long before professors like George Siemens, whose brainchild DALMOOC was, get into state and federal positions of authority and influence to see their modern ideas at the high school level.

https://en.wikipedia.org/wiki/George_Siemens

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