2013.11.04 Data Journalism Introduction

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Presentation of Data journalism at NCCU / Taiwan

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  • 1.#DDJ 101 OPENCAMPUS / 2013/11/04

2. #DDJ vs vs (+ ) vs (Computer assisted Reporting) #DDJ WorkFlow #DDJ 3. 1013 4. 101http://www.cw.com.tw/PicChannelPage/pic_article_cw52701.jsp4 5. 101http://www.cw.com.tw/PicChannelPage/pic_article_cw52502.jsp5 6. 101 6http://www.coolloud.org.tw/node/73858 7. 101http://blog.xuite.net/yehjo01/blog/66492530%E9%8C%A2%E5%BE%9E%E5%93%AA%E8%A3%A1%E4% BE%86%3E7 8. 101 8( 9. 101http://www.economist.com/blogs/graphicdetail/2 013/11/daily-chart 10. 10110C02 11. 11 101 12. 12 101 13. 13 101 14. http://opendata.zeit.de/atomreaktoren/#/en/14 101 15. 101 16. 101Data + Journalism + 17. 101 / 17 18. 101Data Driven Journalism #DDJ 19. 101 (2010) 19 20. 101http://www.theguardian.com/news/datablog/2011/sep/2 6/data-journalism-guardian#_20The first Guardian data journalism: May 5, 1821 21. 101211854 22. 1012010.07Wikileaks 91,731 2001.04 2009.12 22 23. 23 101 24. http://www.theguardian.com/news/datablog/2010/jul/27/ wikileaks-afghanistan-data-datajournalism#_24 101 25. http://mirror.wikileaks.info/wiki/Afghan_War _Diary,_2004-2010/25 101 26. 26 101 27. 101 27And WHY? 28. 28 101 29. 101 29 30. 30 101 31. 31 101 32. 10132 33. 101 33 34. 101 vs 34 35. 101 vs 35 36. 101 36 37. 101 .. 37 38. 101 .. 38(Simons ROGERS - Facts are sacredThe power of data) 39. 10139Big Data 40. 101 40 41. 101 41 42. 10142#DDJ Workflow 43. 101 44. 101 Html / CSV / SQL / KML 2010.07.25 91,731 Afghan war diary 2010.07.25 91,731 2004.01 2009.12http://mirror.wikileaks.info/wiki/Afghan_War _Diary,_2004-2010/ 45. 45 101 46. 101 46 (dataset) 47. http://onlinejournalismblog.wordpress.com/2011/07/07/theinverted-pyramid-of-data-journalism/47 101 48. 101 Simon ROGERS Before a dataset results in a data journalism story, theres a whole process of sifting and finessing and generally sorting the data out. The split is roughly 70% tidying up the data, 30% doing the fun stuff of visualising and presenting it. 49. 101 50. 101 NPO / NGO (SNS) 51. 101 52. 101 53. 101 Mashup 54. 101 55. 101 55 56. 101 56 57. 101http://ansnuclearcafe.org/2011/11/20/79th-carnival-of-nuclearbloggers/storyteller/57 58. 101http://www.ted.com/talks/hans_rosling_shows_the_best_stats_yo u_ve_ever_seen.html58Hans Rosling: The best stats you've ever seen 59. 101 59 60. 101 ( ) 60 61. 101 Data visualization () () () 61 62. 101 : Many EyesGoogle ChartsTableau PublicGoogle Fusion TablesHigh Charts62 63. 101 - : Bubble Cloud Many Eyesd3 / Protovis63 64. 101 - : Tree Map Many EyesGoogle Chartsd3 / Protovis64 65. 101 dot distribution map : GeoCommonsGoogle Fusion TablePolymapsTileMillGoogle Maps APId3 / Protovis65 66. 101 : ExcelTimeline JSTimeFlow66 67. 101 / : WordleMany Eyesd367 68. 101 68 69. 101 (News Apps)http://projects.propublica.org/schools/states/ny69The Opportunity Gap 70. 70 101http://www.nytimes.com/interactive/2010/11/13/weekinr eview/deficits-graphic.html?_r=0 71. 10171 72. 72 101 73. http://schoolofdata.org/data-expeditions/guide-for-guides/73 101 74. 101 2Fundamentals Statistics Programming Machine Learning 5. / Text Mining / Natural Language Processing 6. Data Visualization7. 8. 9. 10. 11.Big Data Data Ingestion Data Wrangling Toolbox) 741. 2. 3. 4. 75. 10175 76. 76 101 77. 10177 78. 78 101 79. 101 correlationvs 79causation 80. 10180 81. 101 81Excel / 82. 101 82.. 83. 101 +83 84. 101 84 85. OPENCAMPUS / Contact me : http://www.opendata.tw http://fb.me/opendata.tw http://fb.me/groups/Open.Campus/ whisky@opendata.tw Twitter : @silmaris