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Visual Data Analysis: Why? When? How? - Shared screen with speaker view
Luis Marcelo
22:00
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Steven Hayduk
22:16
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Maria Mannone
22:21
Hello!
Antonio Zea
22:24
Hello, good afternoon.
Jaime Navon
22:24
Hi
Lauren Beresford
22:25
Good afternoon!
Walter Di Mantova
22:29
Good afternoon all from Ann Arbor, Michigan — the smartest place in the US!
Tina Jensen
22:30
Hi. There is no sound?
Angelos Chatzimparmpas
22:35
Hello everyone!
Ibrahim Al Hazwani
22:35
Hello all!
Luan Soares de Souza
22:38
Good afternoon!
Giovanni Minelli
22:38
Hi everyone
Paolo Buono
22:40
Good afternoon. The audio works well
Guzmán González Mateos
22:42
Good afternoon
Jaime Navon
22:48
Good afternoon from Santiago, Chile
Bryan Malone
22:50
I have sound
Mark Ebden
22:50
Tina: I have sound
Johannes Keyser
22:52
Hi everyone; sound is fine for me
Andreas Maniatis
22:52
Good afternoon
Steve Maher
22:53
I can barely hear the speaker
Alvaro Figueira
22:55
Hello
Tina Jensen
22:59
Found it!
Maxim Khailo
22:59
I hear the speaker well
Pedro Miranda
23:04
Hi everyone.
MAUREEN STONE
23:12
Good morning
Gajendra Bamnote
23:14
The audio is ok 👍
Claudia Esteves
23:15
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Amir Mukeri
23:16
Hey guys
Martha Garcia
23:19
Hello everybody. Martha Garcia from Honduras
Chris Johnson
23:19
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Michael Slater
23:26
Good morning! Sound is good. Jeff Heer is doing the intro now and is clear.
Shrirang Ambaji Kulkarni
23:30
Good Morning.
Peter Vojtas
23:31
Audio is OK
Jay Morreale
23:32
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23:33
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taha sözgen
23:38
hi there
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23:43
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Jacob Celestine
23:44
Hi everyone
Pamela Schmidt
23:45
hello from Kansas
Andrew Brasher
23:45
Goof afternoon from Milton Keynes in the UK
Pedro Miranda
23:49
Here the audio is also OK.
Varun Srivastava
23:50
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Mustafa Yalin Aydin
23:51
Hello everyone
Catherine Valencia
23:51
Good afternoon
Steve Maher
23:53
Audio fine. Dang zoom - forgot it kept its own audio output selection.
David Gruen
23:55
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Ali Abboud
23:56
Hi everyone
Joyce Lee
24:00
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Diana Simic
24:01
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Thomas Breth
24:02
hi, Thomas from Vienna in Austria
Hendrik Strobelt
24:05
cool - happy to see a recording happening...
Gustavo Anunciacao
24:09
Heloo everyone. This is Gustavo from Recife, Brazil.
Joyce Lee
24:11
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Cheryl Howard
24:14
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Richard Steiger
24:16
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Mutala Mohammed
24:23
Good evening, its 5pm from Ghana.
Kay Simons
24:23
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Bill Mitchell
24:25
Stanford and UBC -- what a great set of speakers
Daud Mahmud
24:27
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24:30
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Cheryl Howard
24:30
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24:35
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24:35
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Joël Zuber
24:41
Hi from Switzerland 🙂
Marci Urling
24:47
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24:50
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Riya Saha
24:51
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Lawrence Harris
24:51
Vancouver, Canada
Avi Moskoff
24:53
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Levin Magruder
24:53
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Anna Puig
24:55
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Maria Alejandra Quiros Ramirez
24:56
Hi, will the talk be available offline also?
Thanh-Tung Tran
24:58
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Suomi PONCE HEREDIA
25:00
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Thiago Santos
25:00
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25:07
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Johannes Keyser
25:08
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Clara Zabala
25:09
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Stephen Elston
25:10
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Chase Stokes
25:19
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Huyen Nguyen
25:35
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Muhua Huang
25:36
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Fatima Rani
25:37
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Abdulhaq Salako
25:37
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Dimitrios Tsallas
25:38
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Philip Schaefer
25:43
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Yaying Zhang
25:44
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David Johnson
25:44
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Clay Helberg
25:46
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Amudha J
25:47
Hello from Bangalore, India
Steven Spielman
25:48
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Kathryn Knight
25:49
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Krunal khatri
25:51
Hello From Boston, MA
Carolin Brandt
25:54
Hallo from Munich, Germany 😊
Yixuan Wang
25:57
Hello from AZ.
Philip Schaefer
25:58
Hi from Aachen, Germany
Hervé MIGNOT
25:58
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Leo Lo
25:59
Hello from Hong Kong!
Gabe Fierro
26:00
Hello from Golden, CO!
MADHURI KUMARI
26:04
Hi from India
Doris Kosminsky
26:07
Hello from Rio de Janeiro
Huyen Nguyen
26:08
Greetings from Lubbock, TX!
Nicolas Kruchten
26:10
Hi from Montréal!
Haoxiang Zhang
26:13
Hello from Kingston, Canada
Jian Tao
26:13
Howdy from Texas!
Kathryn Knight
26:20
Hello from Knoxville, TN (USA)
Ian Goethert
26:24
Hi from Oak Ridge, Tennessee!
Michael Aupetit
26:26
Hi from Doha, Qatar!
Jian Tao
26:28
Howdy from Texas!
Panos Vassiliadis
26:28
Greetings to all from Ioannina, Greece
Rozenn Bouville
26:29
Hi from Rennes, France :-)
Jeanne Reis
26:32
Joining from Boston, Mass - hello everyone!
Erich Pearson
26:40
Hello from Travelers Rest, SC!
Haoxiang Zhang
26:42
Hello from Kingston, Canada
Victor Samuel Perez Diaz
26:44
Hi from Bogotá, Colombia. :-)
Fermin Ornelas
26:51
Hi Everyone, from the desert in Tempe AZ
Vijaya Kamble
26:55
it's perception that matters so visual activity important.
Scott Lett
26:56
Hello everyone from Colorado, USA.
Christopher Byrnes
26:59
Hello from Acton, Massachusetts
Ernst Mayr
27:00
Hi from Munich in Bavaria!
ediz yildirim
27:00
Hi!
Cosmin Ionita
27:04
Hi from Massachusetts, USA!
Jason Leigh
27:05
Aloha from Hawaii
Lester Cowley
27:05
Hello from Lester Cowley in Cape Town, South Africa
Van Vung Pham
27:06
Huntsville, Texas, Hello to everyone!
Lingge Zhang
27:06
hello from phx
Vijaya Kamble
27:07
Hi all!!
Andrew Watson
27:09
No slides? For a talk on visualization?
Lloyd Treinish
27:16
Good afternoon from Yorktown Heights, NY.
Elliot Lilikes
27:17
Hello everybody what a wonderful day
Miguel Walle
27:19
Hi from Tamaulipas, México!
Emmanuel Iarussi
27:20
Hello from Buenos Aires!
Minor Huffman
27:27
Hi All !!!
Bruce Kinney
27:30
Hi from New Hampshire, USA
Bernhard Standl
27:31
Hello everyone
Jeyanth Thanapalasingam
27:31
Hi, from Manipay, Jaffna. Srilanka
Yasin Asadi
27:33
hello everyone
DARIA TSOUPIKOVA
27:43
Greetings from Chicago
Jens Oehlschlägel
27:54
Hi from Munich 💛💙
Luan Soares de Souza
27:56
Hi from São Paulo, Brazil
Danny Brugman
28:02
Greetings from the Netherlands
Yan Timanovsky
28:03
Welcome, everyone! If you would like to submit a question for the Q&A today, please use the Q&A button rather than chat. Thanks!
Heloisa Arruda
28:12
Hi from São Paulo Brazil
Yasin Asadi
28:13
😃
Nancy Blachman
28:17
Good morning from SF Bay Area
Jimmy Ruska
28:25
tech expert https://www.linkedin.com/in/jimmyrcom/
Van Vung Pham
28:28
Hello everyone! From Huntsville, texas.
Jyovita Christi
28:50
Hello from India
Manar Hosny
29:04
Hello everyone, from London, UK
Harisha Prakash
29:18
Hello from India
Anna Theodosiadou
29:32
Hello from Thessaloniki, Greece!
Christine Ries
29:39
And I started with a Chemistry major....
Klaus Schneider
29:41
Hello from Munich, Germany
Manar Hosny
29:48
Hello everyone, from London, UK
sarala padi
30:02
Hi from USA
Yash Babaria
30:02
Hello from the planet Mars.
Yasin Asadi
30:04
hello from moon!
Alexander Matrunich
30:05
Hello from Tallinn, Estonia!
Orlando Mendez
30:31
hello from Chile
Kaelyn Willingham
30:45
Checking in from Minneapolis, Minnesota
Bogdan Kowalczyk
30:46
See a lot hello, so also from my side: hello from Cracow Poland
Yvonne Pate
31:02
Hello from Atlanta, GA
Mark Underwood
31:07
Port Washington NY #cybersecurity viz
Matt Oddo
31:14
Hello from Vancouver, Salish Sea, Canada
Mathilde Veron
31:31
Hi somewhere next to Paris, France
Henry Helgen
31:32
Hello from Tillamook, OR, USA
Matt Oddo
31:41
Hello from Vancouver, Salish Sea, Canada
Javier Delgado
31:46
Hi! From Salamanca (Spain)
John CHIOLES
31:59
Howdy from North Houston, TX metro.
Stephen Mbah
32:13
Hi Everyone, I'm from Vancouver, WA
guido cusani
32:23
hello from Cambridge uk
Mlungisi Duma
33:01
Hello Everyone! from Johannesburg, South Africa
Paul Marrow
33:28
Hello from the Wirral, UK: data analysis and visualisation for wireless telecoms worldwide
Jeanine Meyer
33:34
Hello from Mt. Kisco, NY (about an hour by train north of Manhattan. I just finished teaching a class.
carolyn watters
33:50
Hello from Victoria BC
Maddie Potts
34:01
Hello from Phoenix, AZ <3
Kaelyn Willingham
34:14
That's funny, I'm a grad student at UMN. Math PhD.
Brandon Hamilton
34:18
https://en.wikipedia.org/wiki/Pl%C3%BCcker_coordinates
carolyn watters
34:19
Hello from Victoria BC
Francisco José Valverde-Albacete
35:10
Hello From Madrid, Spain. Hi, Paul! Long time no read! 😅
Yasin Asadi
35:40
long time no read!😂
Zaheer Baloch
36:04
Hello from Houston, TX, USA.
Colleen Case
36:39
Hello from the pinky of the mitten state.
Yan Timanovsky
37:05
Hi--could you please paste your question in the Q&A? Thanks!
Maria Mannone
37:11
I’m a UMN alumna
Yan Timanovsky
37:54
Thanks!
Alan Flanders
38:26
Hi from Calgary AB. Loving this discussion. Inspiring.
Kiran Mehta
38:43
Enjoying this fireside chat from Pune in India :-)
Yan Timanovsky
39:14
Welcome! Glad you're enjoying.
Murali MS
41:14
Pat Hanharan referenced this talk a couple of mins ago : https://www.youtube.com/watch?v=SKVI-3K8HNw
Krunal khatri
41:47
Thanks Murali
Huyen Nguyen
41:48
^ Thank you!
Chun Schiros
41:48
cant agree more!
Brian Haan
41:50
Thanks Murali
L.D. Miller
42:23
Thanks again Murali
Michelle Colder Carras
42:37
What was the name of that book?
Steven Hayduk
42:51
Thank you, Murali
Huyen Nguyen
43:08
Data Feminism by Catherine D'Ignazio and Lauren Klein
Smaranda Sandu
43:08
Data Feminism @Michelle Colder Carras
Michelle Colder Carras
43:17
Thanks!
Romeo Lupascu
43:23
about data, see : https://www.romeolupascu.net/2020/08/23/real-fact/
Christine Ries
43:29
So visualization is making something visual. Question: can you learn more by watching data stream past you - seeing one point at a time - or 'see all the data together as a table'? I think you see anamolie better when you see the data in a table. 'jumps' in process stand out. You can see right away what is big and what is small. With streaming data, it would take memory and take longer to recognize distinctive points.
Barbara Tversky
44:03
The real benefit of visualizations is using the lines, boxes, dots, connections, directions that are in space, on a page or a screen, to think. Lines show relationships, boxes show inclusion, sets—far more accessible and direct than words or numbers
Regina Joseph
45:45
We just published results of our 3-year NSF-funded research comparing predictive accuracy between human-centric system and machine model: http://ssrn.com/abstract=3981732.
Jay Morreale
45:46
https://mitpress.mit.edu/books/data-feminism
hamid pirahesh
45:54
Nowadays, we got PB class DBs commonly used. The question is how to summarize the data, n graphs with effective zoom in zoom out, while summarizing the vertices and edges, and avoid a blizzard. Bring in temporal walk, making a movie of progression in time. We need to have rich semantics. For example, connecting a domain specific ontology to the data graph, and look at it from semantics viewpont.We need AI help to achieve this. Still today, we see our tools are primitive for this task
Sheng Long
46:04
Q: To what extent is data analysis “intuition” vs “insight”?
Joe Geller
47:17
Hi Romeo
Brian Haan
47:18
Convincing people of a particular analysis is where I find data visualization especially helpful/useful
Karl Anderson
47:19
Hi Pat, Thanks for teaching Computer Graphics at UW. I remember the hardware we used was over in Meteorology and Space Science.
Regina Joseph
47:22
Humans won handily vs ML in small data technically complex setting, even using small crowds of laypeople
Jock Mackinlay
47:43
Hi Barbara!
Jock Mackinlay
48:10
Hi Barbara!
Walter Di Mantova
48:27
Regina — fascinating!
Caitlin Buckhaults
48:40
and yet we still can't plan risk effectively without using milimorts.
Michelle Colder Carras
48:48
Monte Carlo was actually what my non-scientist friend recommended
Martin Heinsdorf
49:22
Hello from New York City
Yvonne Pate
50:16
Hello from Atlanta, GA :)
Gary Minden
50:19
In 1980 at The University of Kansas we held a course named, “Scientific Modeling and Data Visualization” sponsored by a geography professor and two design professors. What took you so long?
Maxim Khailo
50:21
I wonder how much of this is cultural. Are there other cultures who are better or more comfortable with uncertainty? Could we do something in our schools to help people be more confortable instead of having a black/white view of the world?
Harold Kimmey
50:23
Newer Hawkeye is now showing the image of the ball hitting the court.
Robert Lerche
50:32
I vividly recall a comment Prof. David Draper made, "statistics is the quantification of uncertainty." https://engineering.ucsc.edu/people/draper
Hock Ng
50:39
I totally agree with your point about the important of causality analytics over the usual predictive/descriptive analytics (pattern matching). What will be your recommendation to represent the stimulant-cause relationship in a generic causal discovery analysis
Vijaya Kamble
51:04
partially
Michelle Colder Carras
51:11
Dang, maybe I do have to go get another degree then…
Colleen Case
52:15
Good point Maxim
Yaying Zhang
52:27
😃 Question to professors: Human as a 3 dimensional creature, only understands 1D, 2D, 3D visuals best. Do you think the best practice / solutions using visualization is (or almost) thoroughly explored? What are the areas yet to be explored in visualization?
Romeo Lupascu
53:03
hi Joe, :)
Chun Schiros
53:25
ethnical measure of model is depending on the use/purpose of the model
Lloyd Treinish
53:27
Per Pat's comment, the commonly used tracks in hurricane forecasts actually does not show the probability of the track nor intensity from an ensemble of models that does have information about the probability distribution. That visualization shows a range derived from past storms. That's why the track is symmetric with respect to the track and a width that expands monotonically with lead time.
Krunal khatri
53:41
@Yaying Zhang,Use the Q&A tab at the bottom of zoom for questions. I think they're not taking questions from chat.
Romeo Lupascu
54:27
some take on the label 'AI" https://www.romeolupascu.net/2018/05/03/ai-or-aa/
Tati Tchoubar
54:28
“All the models are wrong, but some are useful” G.Box
Chun Schiros
54:34
agree!
Christine Ries
54:47
Biggest problem in using economics for policy - unintended consequences. Ubiquitous.
Chun Schiros
54:48
understand the data before any building of the model
Fermin Ornelas
54:50
the use and abuse of models given the surge of interest in the field it was bound to happen
Miguel Encarnação
54:54
Great point, Tamara!
D. Verne Morland
55:06
NOAA has an excellent visual simulation of sea level rise with which you can explore the effects of low, medium, and high end predictions on specific coastal areas. This enables non-expert users to see what could happen in areas where they live or travel. It can help inspire confidence in what the science is saying.
Vijaya Kamble
55:14
great 👍
Zartaj Majeed
55:51
Saw Fanny Chevalier mention this misleading chart in a new YouTube video - https://www.livescience.com/45083-misleading-gun-death-chart.html
Zartaj Majeed
57:00
Is there any work being done in automated visual analysis along the lines of visualizations fact checking?
Vijaya Kamble
57:19
👍 true
Zartaj Majeed
57:42
Something that would immediately flag these kinds of charts being propagated on social media
Kathryn Knight
57:59
Interesting example of hand-drawn infographics: https://qz.com/906774/w-e-b-du-bois-commissioned-beautiful-hand-drawn-data-visualizations-and-infographics-for-the-paris-world-fair/
Romeo Lupascu
58:14
the "funniest" thing ever in data processing is the notion of "data cleaning", a term too darn close to "data laundering" ... and ignoring the problem of our inability to control the error levels in our data, (another form of GIGO) https://www.romeolupascu.net/2017/12/31/fact-fiction-and-the-truth/
Zartaj Majeed
58:54
or maybe come up with a kind of normalized representation that allows one to compare apples to apples wrt different kinds of visualizations and charts
Yvonne Pate
59:12
Great points!
Fermin Ornelas
59:15
interesting point, RL. It may occur when the researcher is still learning
Vijaya Kamble
59:17
validation
Walter Di Mantova
59:28
From an anthropologist and practical ethnographer and data geek — thanks!!!
Danny Brugman
01:00:21
I personally enjoyed using Julia (https://julialang.org/) for interactive data visualization recently
Miller Abel
01:00:51
Data “cleaning” is even more basic… Canadian post codes like A1B 0C1 are often miskeyed or improperly entered. If a pattern (as in Canadian post codes) can be identified, “dirty” datapoints can be spotted. If no pattern or rule, it is much harder.
Mary Lang
01:01:29
https://monachalabi.com/#
Jay Morreale
01:01:37
I uses Mathematica to analyze and visualize data and build circuits that monitor and control devices. LinkedIn with me at https://www.linkedin.com/in/pbrane/ and let me know how you visualize data.
Erich Pearson
01:01:44
Charles Joseph Minard's Napolean's Russian Campaign comes to mind as a great hand-drawn visualization. 1869.
Francisco Gomes
01:02:00
Michelle, there’s an interesting article on uncertainty and trust here:
Francisco Gomes
01:02:02
https://www.pnas.org/doi/full/10.1073/pnas.1913678117
Vijaya Kamble
01:02:38
😀
Michelle Colder Carras
01:03:08
Thanks, @Francisco!
Samreen Ahmed
01:03:33
How about text data ?how do we visualize them when its non english .
Eric Minch
01:03:35
Would be good to hear more about principles and less about specific tools.
Kay Simons
01:03:36
Coming from GIS this is really interesting discussion
Jimmy Ruska
01:03:38
tech expert https://www.linkedin.com/in/jimmyrcom/
David Benavides
01:03:45
Great answers.
David Benavides
01:03:47
thanks
Walter Di Mantova
01:03:48
I’m visualizing your cat
Abdulhaq Salako
01:03:54
haha
Alan Stewart
01:04:02
KITTY!
Romeo Lupascu
01:04:03
(data cleaning) how do you know what the information "source" intended?all data "cleaning" are based on some form of "guessing" we call that with funny names like "iner-extrapolation" but in reality is god darn guessing. And all guessed data introduce uncertainty in the final "product" uncertainty no one can tell what is it. Not scarred enough yet?
Michael Chupa
01:04:21
A cat is more difficult to visualize than, say, a teapot.
Vijaya Kamble
01:04:53
cat visualization
Walter Di Mantova
01:05:44
data analysis can help with creating cat-e-gories
Peter van Emde Boas
01:05:52
See https://www.illc.uva.nl/cms/Research/Publications/Dissertations/DS-2002-03.text.pdf foe a detaled analysis of the Minard chart.
Erich Pearson
01:06:14
Thanks, Peter!
Ogadinma Njoku
01:07:07
Good idea but does it encompass all the graphs Algorithms?
Romeo Lupascu
01:07:14
one of the worse scenario Hollywood made id "normal" is "inventing" data in pictures of low resolution to make them "understandable" by "inventing data!" the so well known word "enhance". And people love it... Then you feed all that in a model and all hel break loose .... Not scared enough yet? https://www.romeolupascu.net/2016/10/23/fake-fiction-and-bs/
Matt Oddo
01:07:18
Data cleaning example in nature: Cells convert DNA into RNA, and then RNA into proteins.
Jock Mackinlay
01:07:49
Jeff, thanks for mentioning me.
Natarajan Krishnaswami
01:07:50
Is that the Semiotics of Graphics author?
Jock Mackinlay
01:08:14
And Bertin
Natarajan Krishnaswami
01:08:16
*Semiology
Mark Whiting
01:08:41
Kids get engaged in visual analytics very early in their (rather sophisticated!) computer games. There should be more interaction between the vis community and the game developers to explore some of these questions!
Romeo Lupascu
01:09:15
making visualization "to please" the recipient is dangerous if by doing so we obscure the big picture and enhance the target audience biases
Andrew Brasher
01:10:21
@roman Lupascu not all data cleaning is guesswork. For example, we can be certain that in age data records for people, entries containing negative ages are wrong. However, you're right in that many other data may be less easy to be certain about.
Romeo Lupascu
01:10:48
so how are you "certain"?
Eduard Jacob
01:10:53
I greet you from the Netherlands Roermond
Romeo Lupascu
01:11:05
what gives you that guarantee?
Eduard Jacob
01:11:35
My email is eduard.jacob1@gmail.com
Jock Mackinlay
01:11:49
One pitfall is that people focus on a single view - analysis is the journey through many views
Sireesha Avvari
01:12:06
Domain knowledge is important for effective data cleaning.
Bob Booth
01:12:22
And importance of attention to what you get... The gorilla on the basketball court.
Mario Romero
01:12:42
Question: I have many colleagues from non-visualization ateas who approach my research group with visualization needs. Most times their needs can be satisfied with established methods, like parallel coordinates
Sireesha Avvari
01:12:44
Domain knowledge plays a key role in data cleaning
Romeo Lupascu
01:12:49
truth is there is nothing "certain" out there, we always have to deal with uncertainty and yet how many data points you've seen with uncertainty levels attached to them? (I never seen one)
Michael Chupa
01:13:01
Banish ROYGBIV from all visualizations…
Mario Romero
01:13:19
... how do you balance helping these colleagues with doing research?
Michael Chupa
01:14:09
Recall Lloyd Treinish & Pravda color design tool from IBM DX
Miller Abel
01:14:52
@Sireesha, yes! Domain knowledge is key. What looks suspicious in the data?
Sireesha Avvari
01:15:29
Understanding what the data actually represents is important. We can get the needful inputs from SMEs. And again at the end of the day there is no “objective” truth over there we are aiming to reach. We try to reduce as much noise as possible to get the signal we are interested in
Miguel Encarnação
01:16:05
How do you make business users care/ask for about "better" visualizations - especially beyond visualization just for reporting purposes?
Romeo Lupascu
01:16:11
a "bad typed" data is still data, right, just deleting (ignoring it) does not make data "cleaner" it only changes the type of uncertainty you are dealing with so that process is a misnomer when is called "cleaning". But if we convey the idea of "clean" to people that do not understand but use data (a cop for example) ugly things can happen
Brandon Hamilton
01:16:12
What’s the most satisfying/delightful visualization in your respective careers?
Jeanne Reis
01:16:56
I have the same question Brandon - copying into the Q & A section now
Caitlin Buckhaults
01:17:00
Thank you so much for a fascinating chat, I could listen to this all day.
Fatimah Alqahtani
01:17:29
it is what we want to present
Fatimah Alqahtani
01:18:01
to give a value and real information
Mario Romero
01:18:11
thanks for this wonderful conversation!
Walter Di Mantova
01:18:49
This has been seriously interesting — very thought-provoking!
Jock Mackinlay
01:18:56
Hans Rohling was very skilled at connecting data to the world
Mark Underwood
01:19:29
1. Ontology. 2. Data. Not the other way around.
Tho Tiahrt
01:19:41
Romeo Lupascu: 'truth is there is nothing "certain" out there' - Are you certain about that?
Miguel Encarnação
01:19:45
*Hans Rossling ;)
Richard Steiger
01:19:48
Mark: totally agree!
Miguel Encarnação
01:20:10
*Hans Rossling ;)
Natarajan Krishnaswami
01:20:11
@jock mackinlay: for sure! related, I had the good fortune to work with some ex-gapminder folks while working on public statistics at google
Jock Mackinlay
01:20:42
Thank you!
Evgheni Polisciuc
01:20:42
👏
Yasin Asadi
01:20:44
thankss
Michelle Colder Carras
01:20:46
Wonderful presentation! Thanks!
Fatimah Alqahtani
01:20:46
Thank you so much for you
Yixuan Wang
01:20:47
Thanks!
Sheng Long
01:20:47
http://[::]:8000/
Lynd Bacon
01:20:48
Great stuff! Thanks!
Victor Samuel Perez Diaz
01:20:49
Thank you!
Chris Johnson
01:20:50
Thanks
Natarajan Krishnaswami
01:20:51
Thanks! Wonderful talk!
Jay Morreale
01:20:51
Thank you
Andreas Kriegler
01:20:51
Thanks!
Minwoo Lee
01:20:51
Thank you!
Kathryn Knight
01:20:52
Thank you! Loved this
Huan Liu
01:20:52
Thanks
Maria Mannone
01:20:52
Thank you!!
Yalong Yang
01:20:53
Thank you!
Abdulhaq Salako
01:20:53
thanks
Jeanne Reis
01:20:53
Thank you!
ediz yildirim
01:20:53
Thanks. Later!
David Koppel
01:20:55
Thank you so much for this!
Luis Marcelo
01:20:55
Thank you
Joël Zuber
01:20:55
Thanks! Have a nice day.
Romeo Lupascu
01:20:55
thanks all, good chat, please share it
Lingge Zhang
01:20:55
thank you
Ali Raza
01:20:55
Thank you ALL!!!
Julia De Geest
01:20:55
Thank you!
Avi Moskoff
01:20:55
Thank you!
David Hinley
01:20:55
Thank you
Walter Di Mantova
01:20:55
Thanks!
Christopher Olson
01:20:55
will there be a online readable transcript?
Fatima Rani
01:20:56
thanks
Sheng Long
01:20:56
Thank you!!
Chenguang Xu
01:20:58
Thanks.
Jerry Goldin
01:20:58
Thank you!
Richard Steiger
01:20:58
Many thanks!
Miguel Encarnação
01:20:58
Thanks - great talk!
Regina Joseph
01:20:59
Thank you!
Hung-Fu Chang
01:20:59
Thank you.
Lloyd Treinish
01:21:00
Thank you. I really enjoyed the discussion today.
Philip Schaefer
01:21:01
thanks!
Mustafa Yalin Aydin
01:21:01
Have agood day to everyone from Canada
Clay Helberg
01:21:01
Thank you
Jerry Campbell
01:21:01
Thanks
Carson Leung
01:21:01
Thanks :)
T Harris
01:21:01
thanks!
Gerardo Horvilleur
01:21:02
Great ideas, thank you!
Mario Romero
01:21:02
how many people joined?
Gustavo Anunciacao
01:21:02
Thank you
Van Nguyen
01:21:02
Thank you!
Yvonne Pate
01:21:03
Thank you! EXCELLENT!
Joe Bane
01:21:03
Thank you!
Orlando Mendez
01:21:03
awesome talk, thanks all ;-)
Ali Abboud
01:21:03
Thank you
taha sözgen
01:21:03
Thank you all from Turkey
Anqa Akram
01:21:03
This was a cool one. thanks
Angelos Chatzimparmpas
01:21:06
Bye bye!
Kezang Yuden
01:21:07
Thank you
Corey Abshire
01:21:08
Thanks!
Carrie Leach
01:21:08
Thank you!
Steven Spielman
01:21:09
Thank you!
Thanh-Tung Tran
01:21:09
thanks
Kevin Waugh
01:21:11
Thank you, great session.
Courtney Dozier
01:21:11
Thank you!
Panos Vassiliadis
01:21:12
Thank you!
Brian Berg
01:21:12
Thanks! Hi, Tamara, from Asilomar.
Waldo Rochow
01:21:12
👏
Pedro Miranda
01:21:13
Thanks!
Javier Delgado
01:21:13
Thanks!
Van Vung Pham
01:21:14
Thank you!
Bob Bailey
01:21:15
many thanks!
Mario Romero
01:21:15
thanks!!
Michael Slater
01:21:15
Loved this fireside chat! Thank you! :-)
Judith Lippold
01:21:16
Thank you all!
Angelos Chatzimparmpas
01:21:17
Thank you!
Frank Schulz
01:21:17
Thank you 👏
Athanasios (Thanasis) Zoumpekas
01:21:18
Thank you!
Karl Anderson
01:21:18
Thank you!
Maria Cristina F. Oliveira
01:21:19
Many thanks!
Zhengyu Zhu
01:21:19
Thanks
Lauren Beresford
01:21:19
Thank you!
Jennifer Frazier
01:21:19
Thank you!
Huyen Nguyen
01:21:19
Thank you!
Leo Lo
01:21:20
Thank you!
YING LIU
01:21:21
Many thanks
Zak Sharif
01:21:23
Thank you so much! that was wonderful
Daniel Gilbertson
01:21:24
thanks
Aravind Punugu
01:21:24
thanks
Kate van Lopik
01:21:24
Thank you
Lawrence Araa Odong
01:21:25
thanks
Kenneth Berry
01:21:26
TY