Re: [Edu-sig] probability and statistics demo for kids

[Perry Grossman]

Hi All,

Thanks for the great comments. Sorry for the delay. I have reviewed most of
the materials and will review more. I drafted a presentation plan for next
Friday here:

https://docs.google.com/document/d/1MNIwqjJ2kVS80zy69606TEPLZGy_e-5W0Ae7L5BeaNE/edit?usp=sharing

If you have any more comments let me know.

Perry


On Sat, Feb 24, 2018 at 9:28 PM, kirby urner  wrote:

>
>
> On Sat, Feb 24, 2018 at 5:21 PM, Wes Turner  wrote:
>
>>
>>
>> +1. "Python Data Science Handbook" (by Jake VanderPlas) is available in
>> print and as free Jupyter notebooks:
>> https://github.com/jakevdp/PythonDataScienceHandbook
>>
>> It covers IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn.
>>
>>
>>
>
> ​Yes!  Open on my desk in front of me.
>
> Kirby
>
>
>


-- 
perrygrossman2...@gmail.com
 (617) 383-9061
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Re: [Edu-sig] probability and statistics demo for kids

[kirby urner]

On Sat, Feb 24, 2018 at 5:21 PM, Wes Turner  wrote:

>
>
> +1. "Python Data Science Handbook" (by Jake VanderPlas) is available in
> print and as free Jupyter notebooks:
> https://github.com/jakevdp/PythonDataScienceHandbook
>
> It covers IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn.
>
>
>

​Yes!  Open on my desk in front of me.

Kirby
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Re: [Edu-sig] probability and statistics demo for kids

[Wes Turner]

On Saturday, February 24, 2018, kirby urner  wrote:

>
> ​In terms of Machine Learning more generally, I want to give special
> recognition to Jake VanderPlas, an astronomer who dives deep into
> scikit-learn in some multi-hour Youtube-shared tutorials.
>
> Example:
> https://youtu.be/L7R4HUQ-eQ0
>
> His excellent keynote at Pycon2017:
> https://youtu.be/ZyjCqQEUa8o
>
> Jake does a super-excellent job of showing off the internal consistency of
> the scikit-learn API, where you can basically use the same code while just
> swapping in one classifier or regressor for another.
>
> He also speaks the jargon pretty flawlessly, to my ears at least, in terms
> of what's a feature (label) and what's an observation etc., going into both
> supervised and unsupervised learning scenarios (scikit-learn handles both).
>
> Bravo Jake.
>

+1. "Python Data Science Handbook" (by Jake VanderPlas) is available in
print and as free Jupyter notebooks:
https://github.com/jakevdp/PythonDataScienceHandbook

It covers IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn.




> Allen Downey has great complementary tutorials which go deeper into the
> statistical thinking behind these ML models.  ThinkBayes is fantastic.
>
> It's tempting to just mindlessly throw models at data looking for a best
> fit, and maybe that's all some underpaid cube farmer has time for, but
> VanderPlas, along with Downey, wisely counsels against that.
>
> Stats more than most is a minefield of pitfalls, such as overfitting. If
> your aim is authentic research, then mindless model-slinging will quickly
> come up against its own limitations.  That's the message I keep getting
> from experts in the field.
>
> Kirby
>
> PS:  thanks to Steve Holden, I got to visit the astronomy world up close,
> the form of the Hubble Space Telescope instrumentation team, eager for
> Python knowledge.  These were already programmers, experts with IDL, but
> IDL is not the hard currency Python is, in the wider job market.  For many
> reasons, astronomers can't put all their eggs in one basket.  The Python
> ecosystem has been a godsend.
>
>
>
>
> ​
>
>
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Re: [Edu-sig] probability and statistics demo for kids

[kirby urner]

​In terms of Machine Learning more generally, I want to give special
recognition to Jake VanderPlas, an astronomer who dives deep into
scikit-learn in some multi-hour Youtube-shared tutorials.

Example:
https://youtu.be/L7R4HUQ-eQ0

His excellent keynote at Pycon2017:
https://youtu.be/ZyjCqQEUa8o

Jake does a super-excellent job of showing off the internal consistency of
the scikit-learn API, where you can basically use the same code while just
swapping in one classifier or regressor for another.

He also speaks the jargon pretty flawlessly, to my ears at least, in terms
of what's a feature (label) and what's an observation etc., going into both
supervised and unsupervised learning scenarios (scikit-learn handles both).

Bravo Jake.

Allen Downey has great complementary tutorials which go deeper into the
statistical thinking behind these ML models.  ThinkBayes is fantastic.

It's tempting to just mindlessly throw models at data looking for a best
fit, and maybe that's all some underpaid cube farmer has time for, but
VanderPlas, along with Downey, wisely counsels against that.

Stats more than most is a minefield of pitfalls, such as overfitting. If
your aim is authentic research, then mindless model-slinging will quickly
come up against its own limitations.  That's the message I keep getting
from experts in the field.

Kirby

PS:  thanks to Steve Holden, I got to visit the astronomy world up close,
the form of the Hubble Space Telescope instrumentation team, eager for
Python knowledge.  These were already programmers, experts with IDL, but
IDL is not the hard currency Python is, in the wider job market.  For many
reasons, astronomers can't put all their eggs in one basket.  The Python
ecosystem has been a godsend.




​
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Re: [Edu-sig] probability and statistics demo for kids

[Wes Turner]

On Wednesday, February 21, 2018, A Jorge Garcia via Edu-sig <
edu-sig@python.org> wrote:

> I tried using Jupyter Notebooks last year with my Calc and preCalc
> students last year. However, I'm using CoCalc.com which is Sage Math
> Cloud gone commercial. It was free to use for a while. However, if you use
> it regularly as I have, you get a big red banner across the screen telling
> you to subscribe for $5 per month per user. Well, I have about 100 students
> and can't afford $500 per month and neither can my school, so we are back
> to using sagecell.sagemath.com for now.
>

How many quota'd Docker container does it take to serve JupyterHub for 100
students?

It may be easier to copy a configured conda env ZIP to each PC?

https://jupyterhub.readthedocs.io/en/latest/




> Regards,
> AJG
>
> Sent from BlueMail 
> On Feb 21, 2018, at 9:03 AM, Perry Grossman 
> wrote:
>>
>> I am thinking of doing a simplified interactive presentation on
>> probability and Bayesian statistics for my kids' elementary school.
>> I think it would probably be best for 6-8th graders, but there might be
>> ways to do this for younger students.
>> I'd like to run some Python code to show probability distributions and
>> statistics.
>>
>> I am thinking of simplified examples from these works:
>>
>> Maybe the dice problem, or the cookie problem here:
>> Allen Downey - Bayesian statistics made simple - PyCon 2016
>> 
>>
>> A friend also suggested doing an analysis of how many cards (e.g.
>> pokemon) that one might need to buy to colleft the whole set.
>>
>> Any suggestions on how to make this manageable approachable for kids?
>>
>> Perry
>>
>>
>> On Feb 20, 2018 12:02 PM,  wrote:
>>
>>> Send Edu-sig mailing list submissions to
>>> edu-sig@python.org
>>>
>>> To subscribe or unsubscribe via the World Wide Web, visit
>>> https://mail.python.org/mailman/listinfo/edu-sig
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>>> Today's Topics:
>>>
>>>1. if I taught high school calculus today... (kirby urner)
>>>
>>>
>>> --
>>>
>>> Message: 1
>>> Date: Mon, 19 Feb 2018 19:50:28 -0800
>>> From: kirby urner 
>>> To: "edu-sig@python.org" 
>>> Subject: [Edu-sig] if I taught high school calculus today...
>>> Message-ID:
>>> >> ail.com>
>>> Content-Type: text/plain; charset="utf-8"
>>>
>>> I was a high school calculus teacher (also algebra, geometry, trig) first
>>> job outta university, stuck with it for two years.
>>>
>>> Fast forward to almost age 60, and I'm teaching coding to middle
>>> schoolers,
>>> thinking it's all still math. [1]
>>>
>>> Shouldn't take a "computer scientist" to cover this stuff... Algorithms
>>> are
>>> algorithms after all.
>>>
>>> Were I to teach calculus today, in light of what I now know, I'd focus on
>>> probability density functions right when we get to integration, as "area
>>> under the probability curve" is precisely how we figure out  chances of
>>> something happening.
>>>
>>> We would use Jupyter Notebooks with SciPy, all free & open source.
>>>
>>> As I recall, our calc curriculum never did much to bridge to statistics,
>>> but in SciPy / NumPy, every continuous probability distribution function
>>> (PDF) comes with a cumulative distribution function (CDF) that's defined
>>> exactly as a definite integral between A and B, and giving the
>>> probability
>>> some x in distribution X falls between A and B.
>>>
>>> Forming a bridge twixt calculus and data science would be another
>>> strategy
>>> for getting scientific calculators to share the road, with more relevant
>>> free tools (always an ulterior motive for me).  I don't think a TI is
>>> able
>>> to do definite integration over a standard normal curve.
>>>
>>> Actually, I see I'm wrong:
>>> http://cfcc.edu/faculty/cmoore/TINormal.htm
>>>
>>> Oh well, back to the drawing board.  I still think a strong tie-in twixt
>>> calc and data science makes a lot of sense at the high school level. With
>>> or without Jupyter Notebooks.
>>>
>>> Kirby
>>>
>>> PS:  right now I'm going through Allen Downey's tutorial on Bayesian
>>> stats
>>> using the above mentioned tools, from Pycon 2016:
>>> https://youtu.be/TpgiFIGXcT4
>>> I attended this conference, but didn't manage to make this tutorial.
>>>
>>> [1]  I've shared this before, still relevant:
>>> https://medium.com/@kirbyurner/is-code-school-the-new-high-s
>>> chool-30a8874170b
>>>
>>> Also this blog post:
>>> 

Re: [Edu-sig] probability and statistics demo for kids

[Wes Turner]

Here's the AP Statistics course page for instructors:
https://apcentral.collegeboard.org/courses/ap-statistics

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


It's probably worth mentioning nbgrader for grading notebooks and nbval for
testing notebooks:
https://github.com/jupyter/nbgrader

https://github.com/computationalmodelling/nbval

On Friday, February 23, 2018, Wes Turner  wrote:

>
>
> On Friday, February 23, 2018, Blake  wrote:
>
>> The programs / code examples you all have proposed look great.
>>
>> Perry, I think your idea to teach Bayesian statistics to 6-8th graders
>> sounds great!
>>
>> Just wanted to chime in on a different angle of this: the relevance of
>> the problem(s) that you address.
>>
>> Here is a video of one of my former high school teachers explaining how
>> he teaches reasoning, skepticism, and using probability in the real world.
>> https://www.youtube.com/watch?v=z2HWE6qQ2kI
>>
>> He gives an example of using Bayes Rule which could be a great example
>> for you to use, Perry. And he shows how you can intuitively, visually
>> understand what Bayes Rule tells us for that example, without having to go
>> through the calculations.
>>
>>
>> At the end of that video, he gives a curriculum overview for a year-long
>> course he has developed, called "Human Reasoning", which is about thinking
>> in the real world. I would love to see more people teach the way he does!
>>
>>
>> Curious if people have other examples of this kind of thing, or have
>> ideas of how to use computer simulations specifically for teaching this
>> real-world-focused perspective on mathematics.
>>
>
> https://www.khanacademy.org/math/statistics-probability
>
> https://github.com/jupyter/jupyter/wiki/a-gallery-of-
> interesting-jupyter-notebooks#machine-learning-statistics-and-probability
>
> http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-
> Methods-for-Hackers/
>
>
>
>>
>>
>> --
>> Blake Elias
>>
>> On Fri, Feb 23, 2018 at 2:44 PM, Wes Turner  wrote:
>>
>>> "Seeing Theory: A visual introduction to probability and statistics"
>>> http://students.brown.edu/seeing-theory/
>>> https://github.com/seeingtheory/Seeing-Theory
>>>
>>> These are JavaScript widgets, so not Python but great visual examples
>>> that could be implemented with ipywidgets and some JS.
>>>
>>> explorable.es has a whole catalog of these:
>>> http://explorabl.es/math/
>>>
>>> Think Stats 2nd edition is free:
>>> http://greenteapress.com/wp/think-stats-2e/
>>>
>>> The source is also free:
>>> https://github.com/AllenDowney/ThinkStats2
>>> https://github.com/AllenDowney/ThinkStats2/blob/master/code/
>>> chap01ex.ipynb
>>> https://nbviewer.jupyter.org/github/AllenDowney/ThinkStats2/
>>> tree/master/code/
>>>
>>>
>>> On Friday, February 23, 2018, kirby urner  wrote:
>>>
 I'm a big fan of Galton Boards:

 https://youtu.be/3m4bxse2JEQ  (lots more on Youtube)

 Python + Dice idea = Simple Code

 http://www.pythonforbeginners.com/code-snippets-source-code/
 game-rolling-the-dice/

 I'd introduce the idea that 1 die = Uniform Probability but 2+ dice =
 Binomial distribution (because there are more ways to roll some numbers,
 e.g. 7 than others, e.g. 12).

 A Python generator for Pascal's Triangle (= Binomial Distribution):

 def pascal():
 row = [1]
 while True:
 yield row
 row = [i+j for i,j in zip([0]+row, row+[0])]


 gen = pascal()

 for _ in range(10):
 print(next(gen))

 [1]
 [1, 1]
 [1, 2, 1]
 [1, 3, 3, 1]
 [1, 4, 6, 4, 1]
 [1, 5, 10, 10, 5, 1]
 [1, 6, 15, 20, 15, 6, 1]
 [1, 7, 21, 35, 35, 21, 7, 1]
 [1, 8, 28, 56, 70, 56, 28, 8, 1]
 [1, 9, 36, 84, 126, 126, 84, 36, 9, 1]

 Kirby


 On Tue, Feb 20, 2018 at 6:12 PM, Perry Grossman <
 perrygrossman2...@gmail.com> wrote:

> I am thinking of doing a simplified interactive presentation on
> probability and Bayesian statistics for my kids' elementary school.
> I think it would probably be best for 6-8th graders, but there might
> be ways to do this for younger students.
> I'd like to run some Python code to show probability distributions and
> statistics.
>
> I am thinking of simplified examples from these works:
>
> Maybe the dice problem, or the cookie problem here:
> Allen Downey - Bayesian statistics made simple - PyCon 2016
> 
>
> A friend also suggested doing an analysis of how many cards (e.g.
> pokemon) that one might need to buy to colleft the whole set.
>
> Any suggestions on how to make this manageable approachable for kids?
>
> Perry
>
> PS:  right now I'm going through Allen Downey's tutorial on Bayesian
>> stats
>> using the above mentioned tools, from 

Re: [Edu-sig] probability and statistics demo for kids

[Wes Turner]

On Friday, February 23, 2018, Blake  wrote:

> The programs / code examples you all have proposed look great.
>
> Perry, I think your idea to teach Bayesian statistics to 6-8th graders
> sounds great!
>
> Just wanted to chime in on a different angle of this: the relevance of the
> problem(s) that you address.
>
> Here is a video of one of my former high school teachers explaining how he
> teaches reasoning, skepticism, and using probability in the real world.
> https://www.youtube.com/watch?v=z2HWE6qQ2kI
>
> He gives an example of using Bayes Rule which could be a great example for
> you to use, Perry. And he shows how you can intuitively, visually
> understand what Bayes Rule tells us for that example, without having to go
> through the calculations.
>
>
> At the end of that video, he gives a curriculum overview for a year-long
> course he has developed, called "Human Reasoning", which is about thinking
> in the real world. I would love to see more people teach the way he does!
>
>
> Curious if people have other examples of this kind of thing, or have ideas
> of how to use computer simulations specifically for teaching this
> real-world-focused perspective on mathematics.
>

https://www.khanacademy.org/math/statistics-probability

https://github.com/jupyter/jupyter/wiki/a-gallery-of-interesting-jupyter-notebooks#machine-learning-statistics-and-probability

http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/



>
>
> --
> Blake Elias
>
> On Fri, Feb 23, 2018 at 2:44 PM, Wes Turner  wrote:
>
>> "Seeing Theory: A visual introduction to probability and statistics"
>> http://students.brown.edu/seeing-theory/
>> https://github.com/seeingtheory/Seeing-Theory
>>
>> These are JavaScript widgets, so not Python but great visual examples
>> that could be implemented with ipywidgets and some JS.
>>
>> explorable.es has a whole catalog of these:
>> http://explorabl.es/math/
>>
>> Think Stats 2nd edition is free:
>> http://greenteapress.com/wp/think-stats-2e/
>>
>> The source is also free:
>> https://github.com/AllenDowney/ThinkStats2
>> https://github.com/AllenDowney/ThinkStats2/blob/master/code/
>> chap01ex.ipynb
>> https://nbviewer.jupyter.org/github/AllenDowney/ThinkStats2/
>> tree/master/code/
>>
>>
>> On Friday, February 23, 2018, kirby urner  wrote:
>>
>>> I'm a big fan of Galton Boards:
>>>
>>> https://youtu.be/3m4bxse2JEQ  (lots more on Youtube)
>>>
>>> Python + Dice idea = Simple Code
>>>
>>> http://www.pythonforbeginners.com/code-snippets-source-code/
>>> game-rolling-the-dice/
>>>
>>> I'd introduce the idea that 1 die = Uniform Probability but 2+ dice =
>>> Binomial distribution (because there are more ways to roll some numbers,
>>> e.g. 7 than others, e.g. 12).
>>>
>>> A Python generator for Pascal's Triangle (= Binomial Distribution):
>>>
>>> def pascal():
>>> row = [1]
>>> while True:
>>> yield row
>>> row = [i+j for i,j in zip([0]+row, row+[0])]
>>>
>>>
>>> gen = pascal()
>>>
>>> for _ in range(10):
>>> print(next(gen))
>>>
>>> [1]
>>> [1, 1]
>>> [1, 2, 1]
>>> [1, 3, 3, 1]
>>> [1, 4, 6, 4, 1]
>>> [1, 5, 10, 10, 5, 1]
>>> [1, 6, 15, 20, 15, 6, 1]
>>> [1, 7, 21, 35, 35, 21, 7, 1]
>>> [1, 8, 28, 56, 70, 56, 28, 8, 1]
>>> [1, 9, 36, 84, 126, 126, 84, 36, 9, 1]
>>>
>>> Kirby
>>>
>>>
>>> On Tue, Feb 20, 2018 at 6:12 PM, Perry Grossman <
>>> perrygrossman2...@gmail.com> wrote:
>>>
 I am thinking of doing a simplified interactive presentation on
 probability and Bayesian statistics for my kids' elementary school.
 I think it would probably be best for 6-8th graders, but there might be
 ways to do this for younger students.
 I'd like to run some Python code to show probability distributions and
 statistics.

 I am thinking of simplified examples from these works:

 Maybe the dice problem, or the cookie problem here:
 Allen Downey - Bayesian statistics made simple - PyCon 2016
 

 A friend also suggested doing an analysis of how many cards (e.g.
 pokemon) that one might need to buy to colleft the whole set.

 Any suggestions on how to make this manageable approachable for kids?

 Perry

 PS:  right now I'm going through Allen Downey's tutorial on Bayesian
> stats
> using the above mentioned tools, from Pycon 2016:
> https://youtu.be/TpgiFIGXcT4
> I attended this conference, but didn't manage to make this tutorial.
>
> [1]  I've shared this before, still relevant:
> https://medium.com/@kirbyurner/is-code-school-the-new-high-s
> chool-30a8874170b
>
> Also this blog post:
> http://mybizmo.blogspot.com/2018/02/magic-squares.html
> -- next part --
> An HTML attachment was scrubbed...
> URL:  

Re: [Edu-sig] probability and statistics demo for kids

[Blake]

The programs / code examples you all have proposed look great.

Perry, I think your idea to teach Bayesian statistics to 6-8th graders
sounds great!

Just wanted to chime in on a different angle of this: the relevance of the
problem(s) that you address.

Here is a video of one of my former high school teachers explaining how he
teaches reasoning, skepticism, and using probability in the real world.
https://www.youtube.com/watch?v=z2HWE6qQ2kI

He gives an example of using Bayes Rule which could be a great example for
you to use, Perry. And he shows how you can intuitively, visually
understand what Bayes Rule tells us for that example, without having to go
through the calculations.


At the end of that video, he gives a curriculum overview for a year-long
course he has developed, called "Human Reasoning", which is about thinking
in the real world. I would love to see more people teach the way he does!


Curious if people have other examples of this kind of thing, or have ideas
of how to use computer simulations specifically for teaching this
real-world-focused perspective on mathematics.


--
Blake Elias

On Fri, Feb 23, 2018 at 2:44 PM, Wes Turner  wrote:

> "Seeing Theory: A visual introduction to probability and statistics"
> http://students.brown.edu/seeing-theory/
> https://github.com/seeingtheory/Seeing-Theory
>
> These are JavaScript widgets, so not Python but great visual examples that
> could be implemented with ipywidgets and some JS.
>
> explorable.es has a whole catalog of these:
> http://explorabl.es/math/
>
> Think Stats 2nd edition is free:
> http://greenteapress.com/wp/think-stats-2e/
>
> The source is also free:
> https://github.com/AllenDowney/ThinkStats2
> https://github.com/AllenDowney/ThinkStats2/blob/master/code/chap01ex.ipynb
> https://nbviewer.jupyter.org/github/AllenDowney/
> ThinkStats2/tree/master/code/
>
>
> On Friday, February 23, 2018, kirby urner  wrote:
>
>> I'm a big fan of Galton Boards:
>>
>> https://youtu.be/3m4bxse2JEQ  (lots more on Youtube)
>>
>> Python + Dice idea = Simple Code
>>
>> http://www.pythonforbeginners.com/code-snippets-source-code/
>> game-rolling-the-dice/
>>
>> I'd introduce the idea that 1 die = Uniform Probability but 2+ dice =
>> Binomial distribution (because there are more ways to roll some numbers,
>> e.g. 7 than others, e.g. 12).
>>
>> A Python generator for Pascal's Triangle (= Binomial Distribution):
>>
>> def pascal():
>> row = [1]
>> while True:
>> yield row
>> row = [i+j for i,j in zip([0]+row, row+[0])]
>>
>>
>> gen = pascal()
>>
>> for _ in range(10):
>> print(next(gen))
>>
>> [1]
>> [1, 1]
>> [1, 2, 1]
>> [1, 3, 3, 1]
>> [1, 4, 6, 4, 1]
>> [1, 5, 10, 10, 5, 1]
>> [1, 6, 15, 20, 15, 6, 1]
>> [1, 7, 21, 35, 35, 21, 7, 1]
>> [1, 8, 28, 56, 70, 56, 28, 8, 1]
>> [1, 9, 36, 84, 126, 126, 84, 36, 9, 1]
>>
>> Kirby
>>
>>
>> On Tue, Feb 20, 2018 at 6:12 PM, Perry Grossman <
>> perrygrossman2...@gmail.com> wrote:
>>
>>> I am thinking of doing a simplified interactive presentation on
>>> probability and Bayesian statistics for my kids' elementary school.
>>> I think it would probably be best for 6-8th graders, but there might be
>>> ways to do this for younger students.
>>> I'd like to run some Python code to show probability distributions and
>>> statistics.
>>>
>>> I am thinking of simplified examples from these works:
>>>
>>> Maybe the dice problem, or the cookie problem here:
>>> Allen Downey - Bayesian statistics made simple - PyCon 2016
>>> 
>>>
>>> A friend also suggested doing an analysis of how many cards (e.g.
>>> pokemon) that one might need to buy to colleft the whole set.
>>>
>>> Any suggestions on how to make this manageable approachable for kids?
>>>
>>> Perry
>>>
>>> PS:  right now I'm going through Allen Downey's tutorial on Bayesian
 stats
 using the above mentioned tools, from Pycon 2016:
 https://youtu.be/TpgiFIGXcT4
 I attended this conference, but didn't manage to make this tutorial.

 [1]  I've shared this before, still relevant:
 https://medium.com/@kirbyurner/is-code-school-the-new-high-s
 chool-30a8874170b

 Also this blog post:
 http://mybizmo.blogspot.com/2018/02/magic-squares.html
 -- next part --
 An HTML attachment was scrubbed...
 URL: 

 --

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 ***

>>>
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>>> Edu-sig mailing list
>>> Edu-sig@python.org
>>> 

[Edu-sig] probability and statistics demo for kids

[Wes Turner]

"Seeing Theory: A visual introduction to probability and statistics"
http://students.brown.edu/seeing-theory/
https://github.com/seeingtheory/Seeing-Theory

These are JavaScript widgets, so not Python but great visual examples that
could be implemented with ipywidgets and some JS.

explorable.es has a whole catalog of these:
http://explorabl.es/math/

Think Stats 2nd edition is free:
http://greenteapress.com/wp/think-stats-2e/

The source is also free:
https://github.com/AllenDowney/ThinkStats2
https://github.com/AllenDowney/ThinkStats2/blob/master/code/chap01ex.ipynb
https://nbviewer.jupyter.org/github/AllenDowney/ThinkStats2/tree/master/code/


On Friday, February 23, 2018, kirby urner  wrote:

> I'm a big fan of Galton Boards:
>
> https://youtu.be/3m4bxse2JEQ  (lots more on Youtube)
>
> Python + Dice idea = Simple Code
>
> http://www.pythonforbeginners.com/code-snippets-source-code/
> game-rolling-the-dice/
>
> I'd introduce the idea that 1 die = Uniform Probability but 2+ dice =
> Binomial distribution (because there are more ways to roll some numbers,
> e.g. 7 than others, e.g. 12).
>
> A Python generator for Pascal's Triangle (= Binomial Distribution):
>
> def pascal():
> row = [1]
> while True:
> yield row
> row = [i+j for i,j in zip([0]+row, row+[0])]
>
>
> gen = pascal()
>
> for _ in range(10):
> print(next(gen))
>
> [1]
> [1, 1]
> [1, 2, 1]
> [1, 3, 3, 1]
> [1, 4, 6, 4, 1]
> [1, 5, 10, 10, 5, 1]
> [1, 6, 15, 20, 15, 6, 1]
> [1, 7, 21, 35, 35, 21, 7, 1]
> [1, 8, 28, 56, 70, 56, 28, 8, 1]
> [1, 9, 36, 84, 126, 126, 84, 36, 9, 1]
>
> Kirby
>
>
> On Tue, Feb 20, 2018 at 6:12 PM, Perry Grossman <
> perrygrossman2...@gmail.com> wrote:
>
>> I am thinking of doing a simplified interactive presentation on
>> probability and Bayesian statistics for my kids' elementary school.
>> I think it would probably be best for 6-8th graders, but there might be
>> ways to do this for younger students.
>> I'd like to run some Python code to show probability distributions and
>> statistics.
>>
>> I am thinking of simplified examples from these works:
>>
>> Maybe the dice problem, or the cookie problem here:
>> Allen Downey - Bayesian statistics made simple - PyCon 2016
>> 
>>
>> A friend also suggested doing an analysis of how many cards (e.g.
>> pokemon) that one might need to buy to colleft the whole set.
>>
>> Any suggestions on how to make this manageable approachable for kids?
>>
>> Perry
>>
>> PS:  right now I'm going through Allen Downey's tutorial on Bayesian stats
>>> using the above mentioned tools, from Pycon 2016:
>>> https://youtu.be/TpgiFIGXcT4
>>> I attended this conference, but didn't manage to make this tutorial.
>>>
>>> [1]  I've shared this before, still relevant:
>>> https://medium.com/@kirbyurner/is-code-school-the-new-high-s
>>> chool-30a8874170b
>>>
>>> Also this blog post:
>>> http://mybizmo.blogspot.com/2018/02/magic-squares.html
>>> -- next part --
>>> An HTML attachment was scrubbed...
>>> URL: >> 19/d9e2f965/attachment-0001.html>
>>>
>>> --
>>>
>>> Subject: Digest Footer
>>>
>>> ___
>>> Edu-sig mailing list
>>> Edu-sig@python.org
>>> https://mail.python.org/mailman/listinfo/edu-sig
>>>
>>>
>>> --
>>>
>>> End of Edu-sig Digest, Vol 174, Issue 1
>>> ***
>>>
>>
>> ___
>> Edu-sig mailing list
>> Edu-sig@python.org
>> https://mail.python.org/mailman/listinfo/edu-sig
>>
>>
>
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Re: [Edu-sig] probability and statistics demo for kids

[kirby urner]

I'm a big fan of Galton Boards:

https://youtu.be/3m4bxse2JEQ  (lots more on Youtube)

Python + Dice idea = Simple Code

http://www.pythonforbeginners.com/code-snippets-source-code/game-rolling-the-dice/

I'd introduce the idea that 1 die = Uniform Probability but 2+ dice =
Binomial distribution (because there are more ways to roll some numbers,
e.g. 7 than others, e.g. 12).

A Python generator for Pascal's Triangle (= Binomial Distribution):

def pascal():
row = [1]
while True:
yield row
row = [i+j for i,j in zip([0]+row, row+[0])]


gen = pascal()

for _ in range(10):
print(next(gen))

[1]
[1, 1]
[1, 2, 1]
[1, 3, 3, 1]
[1, 4, 6, 4, 1]
[1, 5, 10, 10, 5, 1]
[1, 6, 15, 20, 15, 6, 1]
[1, 7, 21, 35, 35, 21, 7, 1]
[1, 8, 28, 56, 70, 56, 28, 8, 1]
[1, 9, 36, 84, 126, 126, 84, 36, 9, 1]

Kirby


On Tue, Feb 20, 2018 at 6:12 PM, Perry Grossman  wrote:

> I am thinking of doing a simplified interactive presentation on
> probability and Bayesian statistics for my kids' elementary school.
> I think it would probably be best for 6-8th graders, but there might be
> ways to do this for younger students.
> I'd like to run some Python code to show probability distributions and
> statistics.
>
> I am thinking of simplified examples from these works:
>
> Maybe the dice problem, or the cookie problem here:
> Allen Downey - Bayesian statistics made simple - PyCon 2016
> 
>
> A friend also suggested doing an analysis of how many cards (e.g. pokemon)
> that one might need to buy to colleft the whole set.
>
> Any suggestions on how to make this manageable approachable for kids?
>
> Perry
>
> PS:  right now I'm going through Allen Downey's tutorial on Bayesian stats
>> using the above mentioned tools, from Pycon 2016:
>> https://youtu.be/TpgiFIGXcT4
>> I attended this conference, but didn't manage to make this tutorial.
>>
>> [1]  I've shared this before, still relevant:
>> https://medium.com/@kirbyurner/is-code-school-the-new-high-s
>> chool-30a8874170b
>>
>> Also this blog post:
>> http://mybizmo.blogspot.com/2018/02/magic-squares.html
>> -- next part --
>> An HTML attachment was scrubbed...
>> URL: > 19/d9e2f965/attachment-0001.html>
>>
>> --
>>
>> Subject: Digest Footer
>>
>> ___
>> Edu-sig mailing list
>> Edu-sig@python.org
>> https://mail.python.org/mailman/listinfo/edu-sig
>>
>>
>> --
>>
>> End of Edu-sig Digest, Vol 174, Issue 1
>> ***
>>
>
> ___
> Edu-sig mailing list
> Edu-sig@python.org
> https://mail.python.org/mailman/listinfo/edu-sig
>
>
___
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Re: [Edu-sig] probability and statistics demo for kids

[A Jorge Garcia via Edu-sig]

I tried using Jupyter Notebooks last year with my Calc and preCalc students 
last year. However, I'm using CoCalc.com which is Sage Math Cloud gone 
commercial. It was free to use for a while. However, if you use it regularly as 
I have, you get a big red banner across the screen telling you to subscribe for 
$5 per month per user. Well, I have about 100 students and can't afford $500 
per month and neither can my school, so we are back to using 
sagecell.sagemath.com for now.

Regards,
AJG

⁣Sent from BlueMail ​

On Feb 21, 2018, 9:03 AM, at 9:03 AM, Perry Grossman 
 wrote:
>I am thinking of doing a simplified interactive presentation on
>probability
>and Bayesian statistics for my kids' elementary school.
>I think it would probably be best for 6-8th graders, but there might be
>ways to do this for younger students.
>I'd like to run some Python code to show probability distributions and
>statistics.
>
>I am thinking of simplified examples from these works:
>
>Maybe the dice problem, or the cookie problem here:
>Allen Downey - Bayesian statistics made simple - PyCon 2016
>
>
>A friend also suggested doing an analysis of how many cards (e.g.
>pokemon)
>that one might need to buy to colleft the whole set.
>
>Any suggestions on how to make this manageable approachable for kids?
>
>Perry
>
>
>On Feb 20, 2018 12:02 PM,  wrote:
>
>> Send Edu-sig mailing list submissions to
>> edu-sig@python.org
>>
>> To subscribe or unsubscribe via the World Wide Web, visit
>> https://mail.python.org/mailman/listinfo/edu-sig
>> or, via email, send a message with subject or body 'help' to
>> edu-sig-requ...@python.org
>>
>> You can reach the person managing the list at
>> edu-sig-ow...@python.org
>>
>> When replying, please edit your Subject line so it is more specific
>> than "Re: Contents of Edu-sig digest..."
>>
>>
>> Today's Topics:
>>
>>1. if I taught high school calculus today... (kirby urner)
>>
>>
>>
>--
>>
>> Message: 1
>> Date: Mon, 19 Feb 2018 19:50:28 -0800
>> From: kirby urner 
>> To: "edu-sig@python.org" 
>> Subject: [Edu-sig] if I taught high school calculus today...
>> Message-ID:
>> > ail.com>
>> Content-Type: text/plain; charset="utf-8"
>>
>> I was a high school calculus teacher (also algebra, geometry, trig)
>first
>> job outta university, stuck with it for two years.
>>
>> Fast forward to almost age 60, and I'm teaching coding to middle
>schoolers,
>> thinking it's all still math. [1]
>>
>> Shouldn't take a "computer scientist" to cover this stuff...
>Algorithms are
>> algorithms after all.
>>
>> Were I to teach calculus today, in light of what I now know, I'd
>focus on
>> probability density functions right when we get to integration, as
>"area
>> under the probability curve" is precisely how we figure out  chances
>of
>> something happening.
>>
>> We would use Jupyter Notebooks with SciPy, all free & open source.
>>
>> As I recall, our calc curriculum never did much to bridge to
>statistics,
>> but in SciPy / NumPy, every continuous probability distribution
>function
>> (PDF) comes with a cumulative distribution function (CDF) that's
>defined
>> exactly as a definite integral between A and B, and giving the
>probability
>> some x in distribution X falls between A and B.
>>
>> Forming a bridge twixt calculus and data science would be another
>strategy
>> for getting scientific calculators to share the road, with more
>relevant
>> free tools (always an ulterior motive for me).  I don't think a TI is
>able
>> to do definite integration over a standard normal curve.
>>
>> Actually, I see I'm wrong:
>> http://cfcc.edu/faculty/cmoore/TINormal.htm
>>
>> Oh well, back to the drawing board.  I still think a strong tie-in
>twixt
>> calc and data science makes a lot of sense at the high school level.
>With
>> or without Jupyter Notebooks.
>>
>> Kirby
>>
>> PS:  right now I'm going through Allen Downey's tutorial on Bayesian
>stats
>> using the above mentioned tools, from Pycon 2016:
>> https://youtu.be/TpgiFIGXcT4
>> I attended this conference, but didn't manage to make this tutorial.
>>
>> [1]  I've shared this before, still relevant:
>> https://medium.com/@kirbyurner/is-code-school-the-new-high-
>> school-30a8874170b
>>
>> Also this blog post:
>> http://mybizmo.blogspot.com/2018/02/magic-squares.html
>> -- next part --
>> An HTML attachment was scrubbed...
>> URL: > 19/d9e2f965/attachment-0001.html>
>>
>> --
>>
>> Subject: Digest Footer
>>
>> ___
>> Edu-sig mailing list
>> Edu-sig@python.org
>> https://mail.python.org/mailman/listinfo/edu-sig
>>
>>
>> 

[Edu-sig] probability and statistics demo for kids

[Perry Grossman]

I am thinking of doing a simplified interactive presentation on probability
and Bayesian statistics for my kids' elementary school.
I think it would probably be best for 6-8th graders, but there might be
ways to do this for younger students.
I'd like to run some Python code to show probability distributions and
statistics.

I am thinking of simplified examples from these works:

Maybe the dice problem, or the cookie problem here:
Allen Downey - Bayesian statistics made simple - PyCon 2016


A friend also suggested doing an analysis of how many cards (e.g. pokemon)
that one might need to buy to colleft the whole set.

Any suggestions on how to make this manageable approachable for kids?

Perry


On Feb 20, 2018 12:02 PM,  wrote:

> Send Edu-sig mailing list submissions to
> edu-sig@python.org
>
> To subscribe or unsubscribe via the World Wide Web, visit
> https://mail.python.org/mailman/listinfo/edu-sig
> or, via email, send a message with subject or body 'help' to
> edu-sig-requ...@python.org
>
> You can reach the person managing the list at
> edu-sig-ow...@python.org
>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of Edu-sig digest..."
>
>
> Today's Topics:
>
>1. if I taught high school calculus today... (kirby urner)
>
>
> --
>
> Message: 1
> Date: Mon, 19 Feb 2018 19:50:28 -0800
> From: kirby urner 
> To: "edu-sig@python.org" 
> Subject: [Edu-sig] if I taught high school calculus today...
> Message-ID:
>  ail.com>
> Content-Type: text/plain; charset="utf-8"
>
> I was a high school calculus teacher (also algebra, geometry, trig) first
> job outta university, stuck with it for two years.
>
> Fast forward to almost age 60, and I'm teaching coding to middle schoolers,
> thinking it's all still math. [1]
>
> Shouldn't take a "computer scientist" to cover this stuff... Algorithms are
> algorithms after all.
>
> Were I to teach calculus today, in light of what I now know, I'd focus on
> probability density functions right when we get to integration, as "area
> under the probability curve" is precisely how we figure out  chances of
> something happening.
>
> We would use Jupyter Notebooks with SciPy, all free & open source.
>
> As I recall, our calc curriculum never did much to bridge to statistics,
> but in SciPy / NumPy, every continuous probability distribution function
> (PDF) comes with a cumulative distribution function (CDF) that's defined
> exactly as a definite integral between A and B, and giving the probability
> some x in distribution X falls between A and B.
>
> Forming a bridge twixt calculus and data science would be another strategy
> for getting scientific calculators to share the road, with more relevant
> free tools (always an ulterior motive for me).  I don't think a TI is able
> to do definite integration over a standard normal curve.
>
> Actually, I see I'm wrong:
> http://cfcc.edu/faculty/cmoore/TINormal.htm
>
> Oh well, back to the drawing board.  I still think a strong tie-in twixt
> calc and data science makes a lot of sense at the high school level. With
> or without Jupyter Notebooks.
>
> Kirby
>
> PS:  right now I'm going through Allen Downey's tutorial on Bayesian stats
> using the above mentioned tools, from Pycon 2016:
> https://youtu.be/TpgiFIGXcT4
> I attended this conference, but didn't manage to make this tutorial.
>
> [1]  I've shared this before, still relevant:
> https://medium.com/@kirbyurner/is-code-school-the-new-high-
> school-30a8874170b
>
> Also this blog post:
> http://mybizmo.blogspot.com/2018/02/magic-squares.html
> -- next part --
> An HTML attachment was scrubbed...
> URL:  19/d9e2f965/attachment-0001.html>
>
> --
>
> Subject: Digest Footer
>
> ___
> Edu-sig mailing list
> Edu-sig@python.org
> https://mail.python.org/mailman/listinfo/edu-sig
>
>
> --
>
> End of Edu-sig Digest, Vol 174, Issue 1
> ***
>
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