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ร้อยเรื่องราวจากข้อมูล / Storytelling with Data
This document discusses storytelling with data and data visualization. It begins with an introduction to the speaker and their background. It then covers topics like data sources, challenges in working with big data, applications of data analysis, examples of data stories, the data analysis process, and a case study analyzing tweets about the TV show Game of Thrones. Throughout there are references to iterating on prototypes and using feedback to improve. The overall message is that telling stories from data takes collecting relevant data, exploring it through multiple iterations, and presenting insights in an engaging way.
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ร้อยเรื่องราวจากข้อมูล / Storytelling with Data
- 1. Krist Wongsuphasawat /@kristw ร้อยเรื่องราวจากข้อมูล STORYTELLING WITH DATA
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- 3. Computer Engineer Chulalongkorn University PhDin Computer Science Information Visualization Univ. of Maryland IBM Microsoft Data Scientist Twitter Krist Wongsuphasawat / @kristw
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- 9. เก็บข้อมูล Time Location Type 12:00Paragon Magikarp 12:05 Siam Dis Magikarp 12:40 CTW Magikarp … … …
- 11. เวลา 00:00 12:00 00:006:0018:00 จำนวนปลา เวลา
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- 16. Number of Napoleon'stroops, Distance, Temperature, Latitude and Longitude, Direction of travel, Location (relative to specific dates) 2 dimensions 6 types of data
- 17. DATA VISUALIZATION Explanatory Communicate knowninformation Exploratory Explore data to reveal insights
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- 19. DATA SOURCES Open data Publiclyavailable Private data owned by organization, not available to public Self-collected data Manual, site scraping, etc. Combination of the above
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- 23. ข้อมูลที่ทวิตเตอร์ Tweets Text, Time, Location,Media User information Age, Country, etc. Follows User interactions Navigation, Views
- 24. MANY FORMS OFDATA Standalone files txt, csv, tsv, json, excel, Google Docs, …, pdf* APIs better quality with more overhead Databases doesn’t necessary mean they are organized Big data bigger pain
- 25. HAVING ALL TWEETS Howpeople think I feel.
- 26. How people thinkI feel. How I really feel. HAVING ALL TWEETS
- 27. CHALLENGES Get relevant Tweets hashtag:#oscars keywords: “goal” (football) Too big Need to aggregate & reduce size Slow Long processing time (hours)
- 28. Hadoop Cluster GETTING BIGDATA Data Storage
- 29. Pig / Scalding(slow) GETTING BIG DATA Hadoop Cluster Data Storage Tool
- 30. Hadoop Cluster Pig /Scalding (slow) GETTING BIG DATA Data Storage Tool
- 31. Pig / Scalding(slow) GETTING BIG DATA Hadoop Cluster Data Storage Tool Your laptop Smaller dataset
- 32. Hadoop Cluster Pig /Scalding (slow) Data Storage Tool Final dataset Tool node.js / python / excel (fast) Your laptop GETTING BIG DATA Smaller dataset
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- 34. APPLICATIONS OF DATA Personalanalytics Anyone Product analytics Product Manager, Engineer Data Journalism News, Magazine, Company’s Public Relations …
- 35. NEW YORK TIMESGRAPHICS http://www.nytimes.com/interactive/2014/08/13/upshot/where-people-in-each-state-were-born.html?abt=0002&abg=0#New_York
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- 37. NEWS New York Times TheGuardian Washington Post Wall Street Journal FiveThirtyEight etc.
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- 49. LOCATION Low density High density byMiguel Rios
- 50. LOCATION Low density High density byMiguel Rios
- 51. LOCATION flickr.com/photos/twitteroffice/8798020541 San Francisco Low density Highdensity by Miguel Rios
- 52. Rebuild the world basedon tweet density twitter.github.io/interactive/andes/ by Nicolas Garcia Belmonte
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- 58. USER + LOCATION: FAN MAP interactive.twitter.com/nfl_followers2014
- 59. USER + LOCATION: FAN MAP interactive.twitter.com/nba_followers
- 60. USER + LOCATION: FAN MAP interactive.twitter.com/premierleague
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- 65. CASE STUDY: GAME OFTHRONES
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- 67. “Problem first, notsolution backward” — Brian Caffo (via Ron Brookmeyer)
- 68. “If all youhave is a hammer, everything looks like a nail.” — Abraham Maslow
- 69. Problem Want to knowwhat the audience talk about a TV show
- 70. Problem Want to knowwhat the audience talk about a TV show from Tweets
- 71. HBO’s Game ofThrones Based on a book series “A Song of Ice and Fire” Medieval Fantasy. Knights, magic and dragons.
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- 73. A King dies. Alot of contenders wage a war to reclaim the throne.
- 74. Minor characters withno claim to the throne set their own plans in action to gain power when all the major characters end up killing each other.
- 75. Brave/Honest/Honorable characters die. Intelligentbut shady characters and characters who know nothing continue to live.
- 76. While humans arebusy killing each other, ice zombies “White walkers” are invading from the North. The only group who seems to care about this is neutral group called the Night’s Watch.
- 77. HBO’s Game ofThrones Based on a book series “A Song of Ice and Fire” Medieval Fantasy. Knights, magic and dragons. Many characters. Anybody can die. 6 seasons (57 episodes) so far Multiple storylines in each episode
- 78. Problem Want to knowwhat the audience talk about a TV show from Tweets
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- 80. Ideas Common words Too muchnoise Characters How o"en each character were mentioned?
- 81. I demand atrial by prototyping. CHAPTER II
- 82. Prototyping Pull sample data fromTwitter API Character recognition and counting naive approach
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- 85. List of names DaenerysTargaryen,Khaleesi Jon Snow Sansa Stark Tyrion Lannister Arya Stark Cersei Lannister Khal Drogo Gregor Clegane,Mountain Margaery Tyrell Joffrey Baratheon Bran Stark Theon Greyjoy Jaime Lannister Brienne Eddard Stark,Ned Stark Ramsay Bolton Sandor Clegane,Hound Ygritte Stannis Baratheon Petyr Baelish,Little Finger Robb Stark Bronn Varys Catelyn Stark Oberyn Martell Daario Naharis Davos Seaworth Jorah Mormont Melisandre Myrcella Baratheon Tywin Lannister Tommen Baratheon Grey Worm Tyene Sand Rickon Stark Missandei Roose Bolton Robert Baratheon Jojen Reed Jeor Mormont Tormund Giantsbane Lysa Arryn Yara Greyjoy,Asha Greyjoy Samwell Tarly,Sam Hodor Victarion Greyjoy High Sparrow Dragon Winter Dothraki
- 86. Sample data Character Count Hodor10000 Jon Snow 5000 Daenerys 4000 Bran Stark 3000 … … *These numbers are made up for presentation, not real data.
- 87. When you playthe game of vis, you iterate or you die. CHAPTER III
- 88. Where to gofrom here?
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- 92. Gain insights froma single episode emotion & connections
- 93. Sample data Character Count JonSnow+Sansa 1000 Tormund+Brienne 500 Bran Stark+Hodor 300 … … Character Count Hodor 10000 Jon Snow 5000 Daenerys 4000 … … INDIVIDUALS CONNECTIONS + top emojis + top emojis *These numbers are made up for presentation, not real data.
- 94. Graph NODES EDGES + topemojis + top emojis Character Count Jon Snow+Sansa 1000 Tormund+Brienne 500 Bran Stark+Hodor 300 … … Character Count Hodor 10000 Jon Snow 5000 Daenerys 4000 … … *These numbers are made up for presentation, not real data.
- 95. Network Visualization Node-link diagram Force-directedlayout http://blockbuilder.org/kristw/762b680690e4b2b2666dfec15838a384
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- 98. + Collision Detection(with clusters) https://bl.ocks.org/mbostock/7881887
- 99. Let’s get otherepisodes.
- 100. (More) data arecoming. CHAPTER IV
- 101. More data 1 episode(1 day) => all episodes (6 years) Rewrite the scripts to get archived data
- 102. How much datado we need? Whole week? 5 days? 2 days? A day? etc.
- 103. How much datado we need?
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- 105. The vis isnot enough.
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- 115. A visualizer alwaysevaluates his work. CHAPTER VI
- 116. “Feedback is thebreakfast of champion.” — Ken Blanchard
- 117. Self & Peer Doesit solve the problem?
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- 122. สรุป Data are aroundus and come from many sources. Open data are valuable. Telling story from data is one possible application. News, Magazine, Company PR. Takes time and iterations with many trials and errors. Start with a problem, collect the data, explore, find a story and present it. Krist Wongsuphasawat / @kristw kristw.yellowpigz.com
- 125. The Reading Room 2Silom Soi 19, Bangkok, Thailand 10500
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