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News: : 'We produced 2 posters and a motion graphic video supporting , for the 1st time at , to communicate the exciting innovations and positive trend of farming corp… , see more









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An example of how is not just an objective display of data: website vs. mobile app. (On the web my $300 deductible is represented as 50% of $1750 out-of-pocket limit, the app more accurately represents my remaining costs to hit the limit.)










Three questions on your * Are your dashboards truly providing insights? * Are you delivering a consistent user experience? * Are your dashboards too slow?l




Social network analysis is the process of investigating social structures through the use of networks and graph theory >> via >> >> Cc: @mikequind







week 7: In general, the percentage of the U.S. federal budget invested in research and development has crashed in the past 40 years, even for defense and health!



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week 7: the U.S. National Science Fundation budget has increased steadily over the years, but has decreased steadily relative to GDP.



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Playing around quickly with last week' . U.S. budget has invested a huge amount of money in defense, and secondly in health in the past forty years or so..



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week 8, focussing on Maths PhDs. New thing this week: trying to make my plots more mobile-friendly. Heavily inspired by course on







The Black Data Project challenged people "redo, reimagine and/or improve (if possible) one of the presented at the World Fair" by W.E.B. Du Bois. See what expert of came up with:

The W.E.B. Du Bois map, a Library of Congress map of Georgia counties, and a JMP table with the data from the Library of Congress map



Our at Harvard, MIT, Yale, Columbia, NYU, Boston College, Dartmouth College, U. of Colorado Boulder, Loyola U. Maryland, Loyola Marymount U., Fordham and Santa Clara U.







credit for for proxity analysis and demographics w/ .. store location data for 7-Eleven, Wawa, Circle K, Sheetz, QuikTrip from Demographics Data from



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Agree, open data is not "the new oil", but some information about oil can found by means of open data providers , and, for example, web app ERC (Economic RESTful Client)



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Impressions embarquées. Impression IV, Dataviz, projection, Lasers ( raye of carbone in Gafsa, Tunis and Nantes in real time, Gallery Bonus, Nantes, France. 30 August 2018. Project developped in real time by the collectif Apo33.
#workinprpgress #dataviz #climat #energy #levivant #souadmani #datastory #apo33 #souvenirsduprésent #anthropoceneart
https://www.instagram.com/p/BtD734RhWmo/?utm_source=ig_tumblr_share&igshid=1geda5zjcfi8d

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Great examples of data visualization from 100+ years ago. Not much of a chance to iterate and adapt the graphic layout when you draw with pencils … I wonder what was the process and roles involved in crafting those: was there a data person and an illustrator, a third person creatively proposing how to illustrate data insights?

Today we turn to my south and look at the ecoregions of the northern countries of South America, specifically Colombia (home to my fiancée’s maternal side), Venezuela, Guyana, Suriname, Ecuador, and parts of Peru and Brazil. While none of these countries have any temperate forests, most have three different tropical/subtropical regions (the equator can do that to your ecoregions), desert, mangrove, and flooded grasslands, and montane grasslands. Unless you’re Suriname, which case all you have is the “tropical and subtropical moist broadleaf forest” with a tiny sliver of tropical grasslands. We can’t all be Colombia and Venezuela.

Farkasfalvi Sándor: A budapesti gyárak üzemi és termelési statisztikája. Budapesti Statisztikai Közlemények 59/4, 1930.

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US gun laws, Thai cave divers, and the price of beer around the world: What 2018 looked like in maps

Maps can tell us a lot about what happened in 2018 — from Meghan Markle and Prince Harry’s wedding to the results of the Midterm elections.

The Graphics Insider team compiled 56 of the maps we created this year that visualize the many ups and downs of 2018.

Follow along through 2018 in maps, from wildfires and baby name trends, to the cost of products around the world and Trump’s tariff war.

An Introduction...

I am embarking on a path to become a data scientist. This blog serves a few purposes:

  • Communication - I’ll be learning a lot over the next few months. It would will probably do me some good to be able to explain the concepts that I’ve learned. 
  • Documentation - Blogs: what are they for, if not for documentation? 
  • Visualization - This ties into communication. While using words to explain things is super useful, a huge part of this is visualizing data and analysis. Some people think better in pictures. I know I’m one of those people.


Anyway, here it is. My blog. Enjoy it (or don’t, whatever. Just do you, man.)

I missed the boat

After the disappointment of the last draw.  The last draw was a draw wherein I chose a big loser set of numbers, but I chose that set based on its comparative value next to a set that guessed 3 out of 6 numbers.  So I decided to tweak a few parameters and try seeding the results every time.  As a result, I started seeing 5, 11,  …   and 4, 11, …. every time…. or with some of my more intense settings it got more abstract like “Wed 21 Nov 201832 1 32442049 43 34  35″

what am I supposed to do with that? If I parse it on my own.. maybe I get 24 and 42  which would count as two right.

Another one looked like this:  “Wed 21 Nov 201832 1 334422491444  22″ again.. if I had accepted it as a possibility I might have picked 24 and 42 again.. but 2 right is pretty common… I want at least three.  And last time the 3 right option was spaced and articulated exactly as I had expected.

This is all to say.  I got tangled up in a mess of second-guessing and self-recrimination and managed not to make a guess for tonight’s draw.

last weeks winning numbers made a fascinating shape:

The most recent draw numbers looked like this when they were drawn:

Back to witches hats… hmmmm?

Sorry to go dark for a bit.  Between celebrating my birth and fretting over our elections I was duly distracted from number guessing.

One of the things that has consistently frustrated me in the process of trying to guess numbers has been the idea of repetition.  It seems logical that once a number is picked out of a large enough range, that alone would be enough to make it less likely the next time.  Of course, if a number has a very low total count and needs to catch up to the other numbers in the set, that might provide an exception.  Before you remind me that randomness ensures that none of these concerns really matter, I do understand that.  However, the lottery encourages us to look at statistical probability.  To this end, I  try to look at whatever elements I can to statistically justify a number’s occurrence.

I am still using the character based neural network to select numbers, which I think is somewhat flawed because I think it sometimes will choose a number that has been occurring often because it has been occurring frequently in the data set, rather than because it hasn’t been used in a while.   

Tonight’s number predicts a duplicate in the number 4.  When I look historically, 4 was last picked in July, and then again about a month earlier.  Its current total number of times picked is on the upper end of the average. So if it is picked twice in a row, it would be surprising but still fairly negligible in terms of it’s effect on things.  Shape-wise when 4 was picked on Wednesday it was in my high probability category.  


All of this is to say the shape above seems to reveal a problem in the drawing algorithm, but I post it anyway because I like it. Tonight’s numbers are: 

4, 11, 17, 23, 41, 44

2

Here we go.  I don’t think this is a shoe.  Might be back to a witch’s hat, or … who knows.  Witches can help with spells and such, so maybe this is a random attempt at a witch’s hat in much the same way there have been near misses at high heeled shoes?  The numbers for Wednesday October 17, 2018 for the UK Lotto as predicted by char-rnn, the lua/torch based recursive neural network.

Let’s win! Right?

2

The top image is the set of numbers that won on Saturday.  The bottom is what the recursive neural network guessed.  Not a single number is the same, but there is some similarity in the relative age of the numbers. It’s truly as if the lotto is trying to help in the project of drawing a high heeled shoe (if not the [project of dismantling patriarchy).  If that first number was a little younger it might have been uncanny how much the shapes lined up  Or is it actually really easy to draw a great many styles of high heel using the ages of lottery numbers as your guide?  I will, against all odds, maintain my optimism that after all these years of computationally analyzing random numbers…  I am on the right track.   Stick with me.  It’ll be fun.   

It’s boring if I just let the numbers ride, so I’m going to run the rnn again, and see how we do.  Stay tuned for another machine based guess later today.

Ok.. I kept the hook or the pipe or whatever you wanna call it.. for two draws.  The image above is the latest…   Kind of like a pipe ?  I don’t know.  I’m starting to think that lottery is a lot less figurative than I first imagined.  At least in England it is.  So I’m going to look at some of the output of the algorithm I have, and see what kind of shape that makes.  Stay tuned.. might be announced very shortly.