Data Storytelling & Visualization, Study notes of Storytelling

This note gets an introduction to the foundation of data visualization.

Typology: Study notes

2025/2026

Available from 01/01/2026

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Network Visualizalons.. — show, ComecHors and relalonships, bet” entities j}- gommon char}: : 7 -Node-link diayiam , Network graph A el mee a cid TARE Ss Social RETSoVE 4 \ : os | Mie) avo ples Connections or computer ie 25 HeTwork : ¥ “ FRY ¥ bt \|4 Mullidimensional yvisualizaHon | — show patterns jn data with mulHple.. variable - Common enart + Scart plot, ‘ue “et heat map im sree ig nv 4 Bc sides Aipunl a o ric Pe et on bi examvle> Sale: vs pour VS anh poke f pA NS AEN 5 SMA TAL biedie 4. _ Scatker cas para d Lb et oN Page No. Date - Eee $_Geospahial Nisualizahon = show data based on geogra phica | —<—<—=— — locahon —"“common charts. Chorople*h "map | Mert map - Example! CovtP-19 Cases shown on a map = color are wed: jo. indicate the pohany & \\- Pracking Good Pthics in data visualizahon - ethics in cata VisualizaHon means snowing dara honestly Users are not misled and clearly So that 5 = principles of ethical data ~wisud\izeahon 4) Accuracy & ‘honest es Clarity te Sim plici hy) 3) Faivneis and obtechv iy A) Privacy 4 Anist s) Inclusiveness —& Accessibility sacs (Ae nokre\4 only on color & use labei shape ) a, Ww || TInek¥eckhve visuals and Wow to [myprve ynem ; = poor viSUats ‘can imisleecl UWers: and cause wWsyRa decision | ; ies = cemmon mistakes 4 improvernen} ee 1) Wrong chart type fo 4p = uAINA pie chart with many values * V-=we bar chayr ingteak Ie Ae rey ody tet ty, ; I — misleading scales : : are Zev 7 °XYe YZanis “not gtarhng XY Y-ayis ‘wm ter tTupe of contrast 4) Size coniray }+ —\arye objectS appear more, as if “2 ° ex ta/ hig har shows leesee 2 SM POKTANH? | ‘ Wiahest Bales 1 ; 4: 2) Color contrat —briqght color ow Aull backaround Siac atltension |: Buea : ma eA, wax Wightign’s qn ngnres” ~ \ess, amon poy ») shape Py iy p — Aiyterent shapes represents aierenk careyories ATO fics izplmsrg a et bk se ka en:- pons ale Inianyle, = Female | : eet aoiectinusty (lr w oy Dak Stee | oe topienen. cortrathiy 90), hence 5) OY Tabet “inten Honatiy Wie gas pinto fel ‘od A ex'- Callout boy = eit, i highlights _ peale value dult dey tf sah) . ae appt Miva yinl: = dEMaed) Yor) custom 4t advanced wisuals - Ex:-* scathey plot created wang pythan ale is ill Data Misualization eee } Success BTSs . = guccess Shoriex show how orgqnizahons wed daka visualization ty make, bette decision and achieve veal. results. 1) Wetdiix =—we awhbourd anaes viewer behavior :=Helps oadecide iyshat show ou produce - yesuit: higher wer engagement | = €xX.- yecommending movies: based on aaa history 2) Google. joo fia = A. —we inltrachye eds ee aa usey ae trend -tmpnwve preduch. and. ad. performance .. —ex'-qoogle jrend visualizing search popularity . f ls ' y . = : a) Araseannives Ce low ti = wen visu), andlylics Toy, a 4 inventory = Oph mize | eroduch ; yecommenda Hons — Ex'- charts —— buying Paterns Samet voller, smcmnestanes- hen — _ “te i 14 Heathers Sector hboard drack diseaye spread } decisjon- maling ae pa AG hboard Date ® || Yonovation’ ‘qndi Trends in Data Visualization | = Tynbvatont in dara visualization Focus on making data smarter , toskey 4 move Inkeracve Using MEW technolo aiek ‘) At and Me Tnleqraton = gu tomad cally Rad patterns gnd trends = Dekct evwrs) and gnomalies — provide predichive | wight = support matural \anquaqe Quer ek = yeduces Manual worle > - Ext= AE tol predicts, Future sales ane! highlight unwue dips AS lek ee) Auqrtaiad “Pealiky (AL) - Adds digital dala fp the real world - Tntormalon appears os overlays Sex. oa —_ showin \ive gxocle prices edly 5) F iehial “peality WE) — crealkes Q Fully virtual 3D enyivonmen} -wenrt (can interact wilh ao tas cot sts scventiek ape aah yoy a chat