How to Learn Data Analytics in 2024: Earn a Professional Certificate That Will Help Prepare You for a Job in 6 Months

We’re liv­ing in the age of data. Every sec­ond, mas­sive amounts of data are being gen­er­at­ed, processed, ana­lyzed and, yes, mon­e­tized. Com­pa­nies, gov­ern­ments, and individuals–they’re all awash in data and try­ing to make sense of it. That makes Data Ana­lyt­ics a valu­able skill for pro­fes­sion­als of all ages.

Enter Google, which has launched a pro­fes­sion­al cer­tifi­cate in Data Ana­lyt­ics–one that will “have you job-ready in less than 6 months.” Offered on the Cours­era plat­form, the Data Ana­lyt­ics Pro­fes­sion­al Cer­tifi­cate con­sists of eight cours­es, includ­ing “Foun­da­tions: Data, Data, Every­where,” “Pre­pare Data for Explo­ration,” “Data Analy­sis with R Pro­gram­ming,” and “Share Data Through the Art of Visu­al­iza­tion.” Over­all this pro­gram “includes over 180 hours of instruc­tion and hun­dreds of prac­tice-based assess­ments, which will help you sim­u­late real-world data ana­lyt­ics sce­nar­ios that are crit­i­cal for suc­cess in the work­place. The con­tent is high­ly inter­ac­tive and exclu­sive­ly devel­oped by Google employ­ees with decades of expe­ri­ence in data ana­lyt­ics.”

Upon com­ple­tion, students–even those who haven’t pur­sued a col­lege degree–can direct­ly apply for jobs (e.g., junior or asso­ciate data ana­lyst, data­base admin­is­tra­tor, etc.) with Google and over 150 U.S. employ­ers, includ­ing Deloitte, Tar­get, and Ver­i­zon. You can start a 7‑day free tri­al and explore the cours­es here. If you con­tin­ue beyond the free tri­al, Google/Coursera will charge $49 USD per month. That trans­lates to about $300 after 6 months, the time esti­mat­ed to com­plete the cer­tifi­cate.

Explore the Data Ana­lyt­ics Cer­tifi­cate by watch­ing the video above. Learn more about the over­all Google career cer­tifi­cate ini­tia­tive here. And find oth­er Google pro­fes­sion­al cer­tifi­cates here.

Note: Open Cul­ture has a part­ner­ship with Cours­era. If read­ers enroll in cer­tain Cours­era cours­es and pro­grams, it helps sup­port Open Cul­ture.

When the US Government Commissioned 7,497 Watercolor Paintings of Every Known Fruit in the World (1886)

A pic­ture is worth 1000 words, espe­cial­ly when you are a late-19th or ear­ly-20th cen­tu­ry hor­ti­cul­tur­ist eager to pro­tect intel­lec­tu­al prop­er­ty rights to new­ly cul­ti­vat­ed vari­eties of fruit.

Or an artis­ti­cal­ly gift­ed woman of the same era, look­ing for a steady, respectable source of income.

In 1886, long before col­or pho­tog­ra­phy was a viable option, the US Depart­ment of Agri­cul­ture engaged approx­i­mate­ly 21, most­ly female illus­tra­tors to cre­ate real­is­tic ren­der­ings of hun­dreds of fruit vari­eties for lith­o­graph­ic repro­duc­tion in USDA arti­cles, reports, and bul­letins.

Accord­ing to the Divi­sion of Pomol­o­gy’s first chief, Hen­ry E. Van Deman, the artists’ man­date was to cap­ture “the nat­ur­al size, shape, and col­or of both the exte­ri­or and inte­ri­or of the fruit, with the leaves and twigs char­ac­ter­is­tic of each.”

If a spec­i­men was going bad, the artist was under strict orders to rep­re­sent the dam­age faith­ful­ly — no pret­ty­ing things up.

As Alice Tan­geri­ni, staff illus­tra­tor and cura­tor for botan­i­cal art in the Smithsonian’s Nation­al Muse­um of Nat­ur­al His­to­ry writes, “botan­i­cal illus­tra­tors and their works serve the sci­en­tist, depict(ing) what a botanist describes, act­ing as the proof­read­er for the sci­en­tif­ic descrip­tion:”

Dig­i­tal pho­tog­ra­phy, although increas­ing­ly used, can­not make judge­ments about the intri­ca­cies of por­tray­ing the plant parts a sci­en­tist may wish to empha­size and a cam­era can­not recon­struct a life­like botan­i­cal spec­i­men from dried, pressed mate­r­i­al… the thought process medi­at­ing that deci­sion of every aspect of the illus­tra­tion lives in the head of the illus­tra­tor.

 …the illus­tra­tor also has an eye for the aes­thet­ics of botan­i­cal illus­tra­tion, know­ing that a draw­ing must cap­ture the inter­est of the view­er to be a viable form of com­mu­ni­ca­tion. Atten­tion to accu­ra­cy is impor­tant, but excel­lence of style and tech­nique used is also pri­ma­ry for an illus­tra­tion to endure as a work of art and sci­ence.

Pri­ma­ry con­trib­u­tors Deb­o­rah Griscom Pass­more, Mary Daisy Arnold, Aman­da Almi­ra New­ton and their col­leagues estab­lished norms for botan­i­cal illus­tra­tion with their paint­ings for the USDA’s Pomo­log­i­cal Water­col­or Col­lec­tion, simul­ta­ne­ous­ly pro­vid­ing much-need­ed visu­al evi­dence for cul­ti­va­tors wish­ing to estab­lish claims to their vari­etals.

(Fruit breed­ers’ rights were for­mal­ly pro­tect­ed with the estab­lish­ment of the Plant Patent Act of 1930, which decreed that any­one who “invent­ed or dis­cov­ered and asex­u­al­ly repro­duced any dis­tinct and new vari­ety of plant” could receive a patent.)

The collection’s 7,497 water­col­ors of real­is­ti­cal­ly-ren­dered fruits cap­ture both the com­mon­place and the exot­ic in mouth­wa­ter­ing detail.

Both aes­thet­i­cal­ly and as a sci­en­tif­ic data­base, the Pomo­log­i­cal Water­col­or Col­lec­tion is the berries — specif­i­cal­ly, Gandy, Chesa­peake, Excel­sior, Man­hat­tan, and Gabara to namecheck but a few types of Fra­garia, aka straw­ber­ries, pre­served there­in.

Oth­er fruits remain less­er known on our shores. The USDA spon­sored glob­al expe­di­tions specif­i­cal­ly to gath­er spec­i­mens such as the ones below.

Queen Vic­to­ria report­ed­ly offered knight­hood to any trav­el­er pre­sent­ing her a man­gos­teen — still a rare treat in the west.  They were banned in the U.S. until 2007 in the inter­est of pro­tect­ing local agri­cul­ture from the threat of stow­away Asian fruit flies.

The thick, square-end­ed Popoulu banana would nev­er be mis­tak­en for a Chiq­ui­ta from the out­side. Accord­ing to The World of Bananas in Hawai’i: Then and Now, its lin­eage dates back tens of thou­sands of years to the Van­u­atu arch­i­pel­ago.

If you cel­e­brate the har­vest fes­ti­val Sukkot, you like­ly encoun­tered an etrog with­in the last month. The noto­ri­ous­ly fid­dly crop has been cul­ti­vat­ed domes­ti­cal­ly since 1980, when a yeshi­va stu­dent in Brook­lyn, seek­ing to keep costs down and ensure that kosher pro­to­cols were main­tained, con­vinced a third-gen­er­a­tion Cal­i­for­nia cit­rus grow­er by the name of Fitzger­ald to give it a go.

Explore and down­load hi-res images from the Pomo­log­i­cal Water­col­or Col­lec­tion here.

Relat­ed Con­tent 

A Col­lec­tion of Vin­tage Fruit Crate Labels Offers a Volup­tuous Vision of the Sun­shine State

In 1886, the US Gov­ern­ment Com­mis­sioned 7,500 Water­col­or Paint­ings of Every Known Fruit in the World: Down­load Them in High Res­o­lu­tion

A Stun­ning, Hand-Illus­trat­ed Book of Mush­rooms Drawn by an Over­looked 19th Cen­tu­ry Female Sci­en­tist

Via Aeon

– Ayun Hal­l­i­day is the Chief Pri­ma­tol­o­gist of the East Vil­lage Inky zine and author, most recent­ly, of Cre­ative, Not Famous: The Small Pota­to Man­i­festo and Cre­ative, Not Famous Activ­i­ty Book. Fol­low her @AyunHalliday.

What Happens When Someone Crochets Stuffed Animals Using Instructions from ChatGPT

Alex Wool­ner knows how to put a degree in Eng­lish to good use.

Past projects include a fem­i­nist type­writer blog, retro­fitting stick­er vend­ing machines to dis­pense poet­ry, and a free res­i­den­cy pro­gram for emerg­ing artists at a mul­ti­dis­ci­pli­nary stu­dio she co-found­ed with play­wright and painter Jason Mont­gomery in East­hamp­ton, Mass­a­chu­setts.

More recent­ly, the poet and inter­na­tion­al edu­ca­tor has com­bined her inter­est in amigu­ru­mi cro­cheted ani­mals and Chat­G­PT, the open source AI chat­bot.

Hav­ing cro­cheted an amigu­ru­mi nar­whal for a nephew ear­li­er this year, she hopped on Chat­G­PT and asked it to cre­ate “a cro­chet pat­tern for a nar­whal stuffed ani­mal using worsted weight yarn.”

The result might have dis­cour­aged anoth­er quer­ent, but Wool­ner got out her cro­chet hook and sal­lied forth, fol­low­ing Chat­G­PTs instruc­tions to the let­ter, despite a num­ber of red flags indi­cat­ing that the chatbot’s grasp of nar­whal anato­my was high­ly unre­li­able.

Its igno­rance is part of its DNA. As a large lan­guage mod­el, Chat­G­PT is capa­ble of pro­duc­ing pre­dic­tive text based on vast amounts of data in its mem­o­ry bank. But it can’t see images.

As Amit Kat­wala writes in Wired:

It has no idea what a cat looks like or even what cro­chet is. It sim­ply con­nects words that fre­quent­ly appear togeth­er in its train­ing data. The result is super­fi­cial­ly plau­si­ble pas­sages of text that often fall apart when exposed to the scruti­ny of an expert—what’s been called “flu­ent bull­shit.”

It’s also not too hot at math, a skill set knit­ters and cro­cheters bring to bear read­ing pat­terns, which traf­fic in num­bers of rows and stitch­es, indi­cat­ed by abbre­vi­a­tions that real­ly flum­mox a chat­bot.

An exam­ple of begin­ner-lev­el instruc­tions from a free down­load­able pat­tern for a cute amigu­ru­mi shark:

DORSAL FIN (gray yarn)

Rnd 1: in a mr work 3 sc, 2 hdc, 1 sc (6)

Rnd 2: 3 sc, 1 hdc inc, 1 hdc, 1 sc (7)

Rnd 3: 3 sc, 2 hdc, 1 hdc inc, 1 sc (8)

Rnd 4: 3 sc, 1 hdc inc, 3 hdc, 1 sc inc (10)

Rnd 5: 3 sc, 1 hdc, 1 hdc inc, 3 hdc, 1 sc, 1 sc inc (12)

Rnd 6: 3 sc, 6 hdc, 3 sc (12)

Rnd 7: sc even (12); F/O and leave a long strand of yarn to sew the dor­sal fin between rnds # 18–23. Do not stuff the fin.

Pity poor Chat­G­PT, though, like Wool­ner, it tried.

Their col­lab­o­ra­tion became a cause célèbre when Wool­ner debuted the “AI gen­er­at­ed nar­whal cro­chet mon­stros­i­ty” on Tik­Tok, apt­ly com­par­ing the large tusk Chat­G­PT had her posi­tion atop its head to a chef’s toque.

Is that the best AI can do?

A recent This Amer­i­can Life episode details how Sebastien Bubeck, a machine learn­ing researcher at Microsoft, com­mand­ed anoth­er large lan­guage mod­el, GPT‑4, to cre­ate code that TikZ, a vec­tor graph­ics pro­duc­er, could use to “draw” a uni­corn.

This col­lab­o­ra­tive exper­i­ment was per­haps more empir­i­cal­ly suc­cess­ful than the Chat­G­PT amigu­ru­mi pat­terns Wool­ner duti­ful­ly ren­dered in yarn and fiber­fill. This Amer­i­can Life’s David Kesten­baum was suf­fi­cient­ly awed by the result­ing image to haz­ard a guess that “when peo­ple even­tu­al­ly write the his­to­ry of this crazy moment we are in, they may include this uni­corn.”

It’s not good, but it’s a fuck­ing uni­corn. The body is just an oval. It’s got four stu­pid rec­tan­gles for legs. But there are lit­tle squares for hooves. There’s a mane, an oval for the head. And on top of the head, a tiny yel­low tri­an­gle, the horn. This is insane to say, but I felt like I was see­ing inside its head. Like it had pieced togeth­er some idea of what a uni­corn looked like and this was it.

Let’s not poo poo the mer­its of Woolner’s ongo­ing explo­rations though. As one com­menter observed, it seems she’s “found a way to instan­ti­ate the weird messed up arti­facts of AI gen­er­at­ed images in the phys­i­cal uni­verse.”

To which Wool­ner respond­ed that she “will either be spared or be one of the first to per­ish when AI takes over gov­er­nance of us meat sacks.”

 

In the mean­time, she’s con­tin­u­ing to har­ness Chat­G­PT to birth more mon­strous amigu­ru­mi. Ger­ald the Narwhal’s has been joined by a cat, an otter, Nor­ma the Nor­mal Fish, XL the Newt, and Skein Green, a pel­i­can bear­ing get well wish­es for author and sci­ence vlog­ger Hank Green.

When retired math­e­mati­cian Daina Taim­i­na, author of Cro­chet­ing Adven­tures with Hyper­bol­ic Planes, told the Dai­ly Beast that Ger­ald would have resem­bled a nar­whal more close­ly had Wool­ner sup­plied Chat­G­PT with more specifics, Wool­ner agreed to give it anoth­er go.

Two weeks lat­er, the Dai­ly Beast pro­nounced this attempt, nick­named Ger­ard, “even less nar­whal-look­ing than the first. Its body was a mas­sive stuffed tri­an­gle, and its tusk looked like a gum­drop at one end.”

Wool­ner dubbed Ger­ard pos­si­bly the most frus­trat­ing AI-gen­er­at­ed amigu­ru­mi of her acquain­tance, owing to an onslaught of speci­fici­ty on ChatCPT’s part. It over­loaded her with instruc­tions for every indi­vid­ual stitch, some­times call­ing for more stitch­es in a row than exist­ed in the entire pat­tern, then dipped out with­out telling her how to com­plete the body and tail.

As sil­ly as it all may seem, Wool­ner believes her Chat­G­PT amigu­ru­mi col­labs are a healthy mod­el for artists using AI tech­nol­o­gy:

I think if there are ways for peo­ple in the arts to con­tin­ue to cre­ate, but also approach AI as a tool and as a poten­tial col­lab­o­ra­tor, that is real­ly inter­est­ing. Because then we can start to branch out into com­plete­ly dif­fer­ent, new art forms and cre­ative expressions—things that we couldn’t nec­es­sar­i­ly do before or didn’t have the spark or the idea to do can be explored. 

If you, like Hank Green, have fall­en for one of Woolner’s unholy cre­ations, down­load­able pat­terns are avail­able here for $2 a pop.

Those seek­ing alter­na­tives to fiber­fill are advised to stuff their amigu­ru­mi with “aban­doned hopes and dreams” or “all those free tee shirts you get from giv­ing blood and run­ning road races or what­ev­er you do for fun”.

Relat­ed Con­tent 

An Artist Cro­chets a Life-Size, Anatom­i­cal­ly-Cor­rect Skele­ton, Com­plete with Organs

A Bio­sta­tis­ti­cian Uses Cro­chet to Visu­al­ize the Fright­en­ing Infec­tion Rates of the Coro­n­avirus

Make an Adorable Cro­cheted Fred­die Mer­cury; Down­load a Free Cro­chet Pat­tern Online

– Ayun Hal­l­i­day is the Chief Pri­ma­tol­o­gist of the East Vil­lage Inky zine and author, most recent­ly, of Cre­ative, Not Famous: The Small Pota­to Man­i­festo and Cre­ative, Not Famous Activ­i­ty Book. Fol­low her @AyunHalliday.

How Artists Get Famous: A Physicist Reveals How Networks (and Not Just Talent) Contribute to Artistic Success

“The inhab­i­tants of fif­teenth-cen­tu­ry Flo­rence includ­ed Brunelleschi, Ghib­er­ti, Donatel­lo, Masac­cio, Fil­ip­po Lip­pi, Fra Angeli­co, Ver­roc­chio, Bot­ti­cel­li, Leonar­do, and Michelan­ge­lo,” writes tech investor and essay­ist Paul Gra­ham. “Milan at the time was as big as Flo­rence. How many fif­teenth cen­tu­ry Milanese artists can you name?” Once you get think­ing about the ques­tion of “what hap­pened to the Milanese Leonar­do,” it’s hard to stop. So it seems to have been for net­work physi­cist Albert-Lás­zló Barabási, whose work on the dis­tri­b­u­tion of sci­en­tif­ic genius we fea­tured last month here on Open Cul­ture. Gra­ham’s spec­u­la­tion also applied to that line of inquiry, but it applies much more direct­ly to Barabási’s work on artis­tic fame.

“In the con­tem­po­rary art con­text, the val­ue of an art­work is deter­mined by very com­plex net­works,” Barabási explains in the Big Think video above. Fac­tors include “who is the artist, where has that artist exhib­it­ed before, where was that work exhib­it­ed before, who owns it and who owned it before, and how these mul­ti­ple links con­nect to the canon and to art his­to­ry in gen­er­al.” In search of a clear­er under­stand­ing of their rel­a­tive impor­tance and the nature of their inter­ac­tions, he and a team of researchers gath­ered all the rel­e­vant data to pro­duce “a world­wide map of insti­tu­tions, where it turned out that the most cen­tral nodes — the most con­nect­ed nodes — hap­pened to be also the most pres­ti­gious muse­ums: MoMA, Tate, Gagosian Gallery.”

So far, this may come as no great sur­prise to any­one famil­iar with the art world. But the most inter­est­ing char­ac­ter­is­tic of this net­work map, Barabási says, is that it “allowed us to pre­dict artis­tic suc­cess. That is, if you give me an artist and their first five exhibits, I’d put them on the map and we could fast-for­ward their career to where they’re going to be ten, twen­ty years from now.” In the past, the artists who made it big tend­ed to start their career in some prox­im­i­ty to the map’s cen­tral institutions.“It’s very dif­fi­cult for some­body to enter from the periph­ery. But our research shows that it’s pos­si­ble”: such artists “exhib­it­ed every­where they were will­ing to show their work,” even­tu­al­ly mak­ing influ­en­tial con­nec­tions by these “many ran­dom acts of exhi­bi­tion.”

This research, pub­lished a few years ago in Sci­ence, “con­firms how impor­tant net­works are in art, and how impor­tant it is for an artist to real­ly under­stand the net­works in which their work is embed­ded.” Loca­tion mat­ters a great deal, but that does­n’t con­sign tal­ent to irrel­e­vance. The more tal­ent­ed artists are, “the more and high­er-lev­el insti­tu­tions are will­ing to work with them.” If you’re an artist, “who was will­ing to work with you in your first five exhibits is already a mea­sure of your tal­ent and your future jour­ney in the art world.” But even if you’re not an artist, you under­es­ti­mate simul­ta­ne­ous impor­tance of abil­i­ty and con­nec­tions — and how those two fac­tors inter­act with each oth­er — at your per­il. From art to sci­ence to insur­ance claims adjust­ment to pro­fes­sion­al bowl­ing, every field involves net­works: net­works that, as Barabási’s work has shown us, aren’t always vis­i­ble.

Relat­ed Con­tent:

What Does It Take to Be a Great Artist?: An Aging Painter Reflects on His Cre­ative Process & Why He Will Nev­er Be a Picas­so

An Inter­ac­tive Social Net­work of Abstract Artists: Kandin­sky, Picas­so, Bran­cusi & Many More

21 Artists Give “Advice to the Young:” Vital Lessons from Lau­rie Ander­son, David Byrne, Umber­to Eco, Pat­ti Smith & More

An Ani­mat­ed Bill Mur­ray on the Advan­tages & Dis­ad­van­tages of Fame

Why Ein­stein Was a “Peer­less” Genius, and Hawk­ing Was an “Ordi­nary” Genius: A Sci­en­tist Explains

Based in Seoul, Col­in Marshall writes and broad­casts on cities, lan­guage, and cul­ture. His projects include the Sub­stack newslet­ter Books on Cities, the book The State­less City: a Walk through 21st-Cen­tu­ry Los Ange­les and the video series The City in Cin­e­ma. Fol­low him on Twit­ter at @colinmarshall or on Face­book.

How the Human Population Reached 8 Billion: An Animated Video Covers 300,000 Years of History in Four Minutes

Hav­ing come out less than two weeks ago, the Amer­i­can Muse­um of Nat­ur­al His­to­ry video above incor­po­rates up-to-date infor­ma­tion on the num­ber of human beings on plan­et Earth. But what’s inter­est­ing here isn’t so much the cur­rent glob­al-pop­u­la­tion fig­ure (eight bil­lion, inci­den­tal­ly) as how we reached it. That sto­ry emerges through an ani­mat­ed visu­al­iza­tion that com­press­es a peri­od of 300,000 years — with all its migra­tions, its grow­ing and declin­ing empires, its major trade routes, its tech­no­log­i­cal devel­op­ments, its plagues, and its wars — into about four and a half min­utes.

“Mod­ern humans evolved in Africa about 300,000 years ago,” says the video’s explana­to­ry text. “Around 100,000 years ago, we began migrat­ing around the globe,” a process that shows no signs of stop­ping here in the twen­ty-first cen­tu­ry.

The same can’t be said for the way our num­bers have increased over the past few hun­dred years, at least accord­ing to the pro­jec­tion that “glob­al pop­u­la­tion will peak this cen­tu­ry” around ten bil­lion, due to “aver­age fer­til­i­ty rates falling in near­ly every coun­try.” For some, this is not entire­ly unwel­come, giv­en that “as our pop­u­la­tion grows, so has our use of Earth­’s resources.”

It’s been a while since the devel­oped world has felt a wide­spread fear of over­pop­u­la­tion, which had a cli­mate change-like pow­er to inspire apoc­a­lyp­tic visions in the nine­teen-sev­en­ties. Nowa­days, we’re more like­ly to hear warn­ings of immi­nent glob­al pop­u­la­tion col­lapse, with low-birthrate coun­tries like South Korea, where I live, held up as cau­tion­ary demo­graph­ic exam­ples. From anoth­er per­spec­tive, the pat­terns of human­i­ty’s expan­sion thus far could also be used to illus­trate calls to explore and col­o­nize oth­er plan­ets, not least to secure our species a path to sur­vival should some­thing go seri­ous­ly wrong here on Earth. How­ev­er our pop­u­la­tion graph changes in the future, we can rest assured that we’ll always think of our­selves as liv­ing at one kind of deci­sive moment or anoth­er.

Relat­ed con­tent:

Hans Rosling Uses Ikea Props to Explain World of 7 Bil­lion Peo­ple

How Humans Migrat­ed Across The Globe Over 200,000 Years: An Ani­mat­ed Look

Buck­min­ster Fuller Cre­ates an Ani­mat­ed Visu­al­iza­tion of Human Pop­u­la­tion Growth from 1000 B.C.E. to 1965

Col­or­ful Ani­ma­tion Visu­al­izes 200 Years of Immi­gra­tion to the U.S. (1820-Present)

Who Is the World’s Most Typ­i­cal Per­son?

Based in Seoul, Col­in Marshall writes and broad­casts on cities, lan­guage, and cul­ture. His projects include the Sub­stack newslet­ter Books on Cities, the book The State­less City: a Walk through 21st-Cen­tu­ry Los Ange­les and the video series The City in Cin­e­ma. Fol­low him on Twit­ter at @colinmarshall or on Face­book.

Why Einstein Was a “Peerless” Genius, and Hawking Was an “Ordinary” Genius: A Scientist Explains

Genius sells. Pub­lish­ers of biogra­phies and stu­dios behind Oscar-win­ning dra­mas can tell you that. So can net­work sci­en­tist Albert-Lás­zló Barabási, who has actu­al­ly con­duct­ed research into the nature of genius. “What real­ly deter­mines the ‘genius’ label?” he asks in the Big Think video above. When he and his col­lab­o­ra­tors “com­pared all genius­es to their sci­en­tif­ic peers, we real­ized that there are real­ly two very dif­fer­ent class­es: ordi­nary genius and peer­less genius.” Con­sid­er­ing the lat­ter, Barabási points to the per­haps unsur­pris­ing exam­ple of Albert Ein­stein.

“When we looked at the sci­en­tists work­ing at the same time, rough­ly in the same areas of physics that he did,” Barabási explains, “there was no one who would have a com­pa­ra­ble pro­duc­tiv­i­ty or sci­en­tif­ic impact to him. He was tru­ly alone.” Illus­trat­ing the class of “ordi­nary genius” is a fig­ure almost as well-known as Ein­stein: Stephen Hawk­ing. “To our sur­prise, we real­ized, there were about six oth­er sci­en­tists who worked in rough­ly the same area, and had com­pa­ra­ble, often big­ger impacts than Stephen Hawk­ing had” — and yet only he was pub­licly labeled a “genius.”

“The ‘genius’ label is a con­struct that soci­ety assigns to excep­tion­al accom­plish­ment, but excep­tion­al accom­plish­ment is not suf­fi­cient to get the genius label.” Through­out his­to­ry, “remark­able indi­vid­u­als were always born in the vicin­i­ty of big cul­tur­al cen­ters, and every­thing that is out­side of the cul­tur­al cen­ters was typ­i­cal­ly a desert of excep­tion­al accom­plish­ments.” Today, as ven­ture cap­i­tal­ist and essay­ist Paul Gra­ham once wrote, “a thou­sand Leonar­dos and a thou­sand Michelan­ge­los walk among us. If DNA ruled, we should be greet­ed dai­ly by artis­tic mar­vels. We aren’t, and the rea­son is that to make Leonar­do you need more than his innate abil­i­ty. You also need Flo­rence in 1450.”

What would it take to dis­cov­er the “hid­den genius­es” who may have been born into unpro­pi­tious cir­cum­stances? This is one con­cern behind Barabási’s inquiry into the nature of sci­en­tif­ic promi­nence. The ques­tion of “how does the qual­i­ty of the idea that I picked, and the ulti­mate suc­cess, and my abil­i­ty as a sci­en­tist con­nect to each oth­er” led him to devel­op the “Q fac­tor,” the mea­sure of “our abil­i­ty to turn ideas into dis­cov­er­ies.” His analy­sis of the data shows that, through­out a sci­en­tist’s career, the Q fac­tor remains more or less sta­ble. Apply­ing it to big data “could help us to dis­cov­er those that real­ly had the accom­plish­ment and deserve the genius label and put them in the right place.” If he’s cor­rect, we can expect a bumper crop of books and movies on a whole new wave of genius­es in the years to come.

Relat­ed con­tent:

What Char­ac­ter Traits Do Genius­es Share in Com­mon?: From Isaac New­ton to Richard Feyn­man

“The Most Intel­li­gent Pho­to Ever Tak­en”: The 1927 Solvay Coun­cil Con­fer­ence, Fea­tur­ing Ein­stein, Bohr, Curie, Heisen­berg, Schrödinger & More

This is What Richard Feynman’s PhD The­sis Looks Like: A Video Intro­duc­tion

Neil deGrasse Tyson on the Stag­ger­ing Genius of Isaac New­ton

Explore the Largest Online Archive Explor­ing the Genius of Leonard da Vin­ci

“The Matil­da Effect”: How Pio­neer­ing Women Sci­en­tists Have Been Denied Recog­ni­tion and Writ­ten Out of Sci­ence His­to­ry

Based in Seoul, Col­in Marshall writes and broad­casts on cities, lan­guage, and cul­ture. His projects include the Sub­stack newslet­ter Books on Cities, the book The State­less City: a Walk through 21st-Cen­tu­ry Los Ange­les and the video series The City in Cin­e­ma. Fol­low him on Twit­ter at @colinmarshall or on Face­book.

The Pioneering Data Visualizations of William Playfair, Who Invented the Line, Bar, and Pie Charts (Circa 1786)

“If you see a pie chart pro­ject­ed twelve feet high in front of you, you know you’re in the hands of an idiot.” These words have stuck with me since I heard them spo­ken by Edward Tufte, one of the most respect­ed liv­ing author­i­ties on data visu­al­iza­tion. The lat­ter-day sins of pie-chart-mak­ers (espe­cial­ly those who make them in Pow­er­Point) are many and var­ied, but the orig­i­nal sin of the pie chart itself is that of fun­da­men­tal­ly mis­rep­re­sent­ing one-dimen­sion­al infor­ma­tion — a com­pa­ny bud­get, a city’s pop­u­la­tion demo­graph­ics — in two-dimen­sion­al form.

Yet the pie chart was cre­at­ed by a mas­ter, indeed the first mas­ter, of infor­ma­tion design, the late-eigh­teenth- and ear­ly-nine­teenth-cen­tu­ry Scot­tish econ­o­mist William Play­fair. Tufte includes Play­fair’s first pie chart, an illus­tra­tion of the land hold­ings of var­i­ous nations and empires cir­ca 1800, in his book The Visu­al Dis­play of Quan­ti­ta­tive Infor­ma­tion.

“The cir­cle rep­re­sents the area of each coun­try,” Tufte explains. “The line on the left, the pop­u­la­tion in mil­lions read on the ver­ti­cal scales; the line on the right, the rev­enue (tax­es) col­lect­ed in mil­lions of pounds ster­ling read also on the ver­ti­cal scale.” The dot­ted lines between them show, in Play­fair’s words, whether “the coun­try is bur­dened with heavy tax­es or oth­er­wise” in pro­por­tion to its pop­u­la­tion.

Play­fair was exper­i­ment­ing with data visu­al­iza­tion long before his inven­tion of the pie chart. He also came up with the more truth­ful bar chart, his­to­ry’s first exam­ple of which appeared in his Com­mer­cial and Polit­i­cal Atlas of 1786. That same book also con­tains the strik­ing graph above, of Eng­land’s “exports and imports to and from Den­mark and Nor­way from 1700 to 1780,” whose lines cre­ate fields that make the bal­ance of trade leg­i­ble at a glance. A much lat­er exam­ple of the line graph, anoth­er form Play­fair is cred­it­ed with invent­ing, appears just below, “exhibit­ing the rev­enues, expen­di­ture, debt, price of stocks and bread from 1770 to 1824,” a peri­od span­ning the Amer­i­can and French Rev­o­lu­tions as well as the Napoleon­ic Wars.

It’s safe to say that Play­fair lived in inter­est­ing times, and even with­in that con­text lived an unusu­al­ly inter­est­ing life. Dur­ing Great Britain’s wars with France, he served his coun­try as a secret agent, even com­ing up with a plan to coun­ter­feit assig­nats, a French cur­ren­cy at the time, in order to desta­bi­lize the ene­my’s econ­o­my. “Their assig­nats are their mon­ey,” he wrote in 1793, “and it is bet­ter to destroy this paper found­ed upon an iniq­ui­tous extor­tion and a vil­lain­ous decep­tion than to shed the blood of men.” Two years after the plan went into effect, the assig­nat was worth­less and France’s ship of state had more or less run aground. Play­fair’s mea­sures may seem extreme, but then, you don’t win a war with pie charts.

Relat­ed con­tent:

The Five Graphs That Changed the World: See Ground­break­ing Data Visu­al­iza­tions by Flo­rence Nightin­gale, W. E. B. DuBois & Beyond

The Art of Data Visu­al­iza­tion: How to Tell Com­plex Sto­ries Through Smart Design

Flo­rence Nightin­gale Saved Lives by Cre­at­ing Rev­o­lu­tion­ary Visu­al­iza­tions of Sta­tis­tics (1855)

Kurt Von­negut Dia­grams the Shape of All Sto­ries: From Kafka’s “Meta­mor­pho­sis” to “Cin­derel­la”

The 1855 Map That Rev­o­lu­tion­ized Dis­ease Pre­ven­tion & Data Visu­al­iza­tion: Dis­cov­er John Snow’s Broad Street Pump Map

W. E. B. Du Bois Cre­ates Rev­o­lu­tion­ary, Artis­tic Data Visu­al­iza­tions Show­ing the Eco­nom­ic Plight of African-Amer­i­cans (1900)

Based in Seoul, Col­in Marshall writes and broad­casts on cities, lan­guage, and cul­ture. His projects include the Sub­stack newslet­ter Books on Cities, the book The State­less City: a Walk through 21st-Cen­tu­ry Los Ange­les and the video series The City in Cin­e­ma. Fol­low him on Twit­ter at @colinmarshall or on Face­book.

The Five Graphs That Changed the World: See Groundbreaking Data Visualizations by Florence Nightingale, W. E. B. DuBois & Beyond

Almost two and a half cen­turies after its first pub­li­ca­tion, Adam Smith’s An Inquiry into the Nature and Caus­es of the Wealth of Nations is much bet­ter known as sim­ply The Wealth of Nations. Had he writ­ten it today, the text itself, which runs between a for­mi­da­ble 500–700 pages in most edi­tions, would also be con­sid­er­ably short­er. It’s not just that writ­ers in Smith’s day went in for length per se (though many now read as if they did), but that graphs had­n’t been invent­ed yet. Much of what he’d dis­cov­ered about the nature of eco­nom­ics could have been expressed more con­cise­ly — and much more clear­ly — in pic­tures rather than words.

As it hap­pens, the kind of infor­ma­tion­al graphs we know best today would be invent­ed by Smith’s fel­low Scot William Play­fair in 1786, just a decade after The Wealth of Nations came out. “Data visu­al­iza­tion is every­where today, but when Play­fair first cre­at­ed them over 200 years ago, using shapes to rep­re­sent num­bers was large­ly sneered at,” says Adam Ruther­ford in the Roy­al Soci­ety video above.

“How could draw­ings tru­ly rep­re­sent sol­id sci­en­tif­ic data? But now, data visu­al­iza­tion has become an art form of its own.” There fol­low “five graphs that changed the world,” begin­ning with the map of water pumps that physi­cian John Snow used to deter­mine the cause of a cholera epi­dem­ic in 1850s Lon­don, pre­vi­ous­ly fea­tured here on Open Cul­ture.

We’ve also post­ed W. E. B. Du Bois’ “hand­made charts show­cas­ing the edu­ca­tion­al, social, and busi­ness accom­plish­ments of black Amer­i­cans in the 35 years since slav­ery had been offi­cial­ly abol­ished.” The oth­er world-chang­ing graphs here include Flo­rence Nightin­gale’s “cox­comb” that showed how unsan­i­tary hos­pi­tal con­di­tions killed more sol­diers dur­ing the Crimean War than did actu­al fight­ing; the so-called Kallikak Fam­i­ly Tree, a fraud­u­lent visu­al case for remov­ing the “fee­ble-mind­ed” from soci­ety; and Ed Hawkins’ more recent red-and-blue “warm­ing stripes” designed to present the effects of cli­mate change to a non-sci­en­tif­ic audi­ence. Using just blocks of col­or, with nei­ther num­bers nor text, Hawkins’ bold graph harks back to an ear­li­er gold­en era of data visu­al­iza­tion: after Play­fair, but before Pow­er­Point.

via Aeon

Relat­ed con­tent:

The Art of Data Visu­al­iza­tion: How to Tell Com­plex Sto­ries Through Smart Design

Flo­rence Nightin­gale Saved Lives by Cre­at­ing Rev­o­lu­tion­ary Visu­al­iza­tions of Sta­tis­tics (1855)

Kurt Von­negut Dia­grams the Shape of All Sto­ries: From Kafka’s “Meta­mor­pho­sis” to “Cin­derel­la”

A Pro­por­tion­al Visu­al­iza­tion of the World’s Most Pop­u­lar Lan­guages

The 1855 Map That Rev­o­lu­tion­ized Dis­ease Pre­ven­tion & Data Visu­al­iza­tion: Dis­cov­er John Snow’s Broad Street Pump Map

The His­to­ry of Phi­los­o­phy Visu­al­ized

W. E. B. Du Bois Cre­ates Rev­o­lu­tion­ary, Artis­tic Data Visu­al­iza­tions Show­ing the Eco­nom­ic Plight of African-Amer­i­cans (1900)

Based in Seoul, Col­in Marshall writes and broad­casts on cities, lan­guage, and cul­ture. His projects include the Sub­stack newslet­ter Books on Cities, the book The State­less City: a Walk through 21st-Cen­tu­ry Los Ange­les and the video series The City in Cin­e­ma. Fol­low him on Twit­ter at @colinmarshall or on Face­book.

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