解決正確的問題 - 如何讓數據發揮影響力？

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解解解解解解解： 解解解解解解解解解解？ Pei-shen Wu, MD [email protected]

Transcript of 解決正確的問題 - 如何讓數據發揮影響力？

Page 1: 解決正確的問題 - 如何讓數據發揮影響力？

解決正確的問題：如何讓數據發揮影響力？Pei-shen Wu, MD

[email protected]

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Outline• 是什麼阻礙了組織運用數據資料的力量？• 怎麼解決？從 practitioner的角度出發• 重新思考數位時代下的學習

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資料分析需求的分佈

因果關係驗證

資料探索、假設建立、相關性探究大多數的分析需求在這

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與資料人員的對話「近期增加了產品品項，但客戶人數不見起色，想問最近用戶的活躍狀況？」「這些用戶的狀況跟去年同期比較如何？」「這些用戶是否有地區差異？」「與這些用戶同班的用戶的狀況？」…

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回應單一個問題需要的功夫SELECT keyID,userID,dot_email,gender,points,user_nickname,username,userEmail,userSendableEmail,userRole,userCity,userSchool,userGrade,joinedTime,userBirthdate,match_score FROM (SELECT pre.keyID AS keyID,pre.userID AS userID,pre.dot_email AS dot_email,pre.gender AS gender,pre.points AS points,pre.user_nickname AS user_nickname,pre.username AS username,pre.userEmail AS userEmail,pre.userSendableEmail AS userSendableEmail,pre.userRole AS userRole,pre.userCity AS userCity,pre.userSchool AS userSchool,pre.userGrade AS userGrade,pre.joinedTime AS joinedTime,pre.userBirthdate AS userBirthdate,post.match_score AS match_score FROM (SELECT * FROM (SELECT pre.keyID AS keyID,pre.userID AS userID,pre.dot_email AS dot_email,pre.gender AS gender,pre.points AS points,pre.user_nickname AS user_nickname,pre.username AS username,post.userEmail AS userEmail,post.userSendableEmail AS userSendableEmail,post.userRole AS userRole,post.userCity AS userCity,post.userSchool AS userSchool,post.userGrade AS userGrade,post.joinedTime AS joinedTime,post.userBirthdate AS userBirthdate FROM (SELECT * FROM (SELECT keyID, userID, dot_email, gender, points, user_nickname, username FROM (SELECT __key__.name AS keyID, user_id AS userID, user_email AS underline_email, current_user.email AS cu_email, user.email AS dot_email, gender, points, user_nickname, username FROM junyi_20161212.UserData_20161212)) AS pre INNER JOIN EACH (SELECT userEmail, userSendableEmail, userId AS userID, userRole, userCity, userSchool, userGrade, joinedTime, userBirthdate FROM FinalTable.UserFinalTmpInfo) AS post ON pre.userID = post.userID)) AS pre INNER JOIN EACH (SELECT keyID,match_score FROM (SELECT keyID, match_score FROM (SELECT keyID,prob1*prob2 AS match_score FROM (SELECT pre.keyID AS keyID,pre.prob1 AS prob1,post.prob2 AS prob2 FROM (SELECT * FROM (SELECT keyID, prob1 FROM (SELECT keyID, match_score AS prob1 FROM (SELECT keyID, match_score FROM (SELECT keyID, match_score_A*cdf AS match_score FROM (SELECT keyID, MAX(output) AS cdf, match_score_A FROM (SELECT keyID, IF(metric >= input,1,0) AS compare, output, match_score_A FROM (SELECT pre.keyID AS keyID,pre.comparekey AS comparekey,pre.metric AS metric,pre.match_score_A AS match_score_A,post.input AS input,post.output AS output FROM (SELECT * FROM (SELECT keyID, 1 AS comparekey, metric, match_score AS match_score_A FROM (SELECT pre.keyID AS keyID,pre.match_score AS match_score,post.dot_email AS dot_email,post.metric AS metric FROM (SELECT * FROM (SELECT teacher_keyID AS keyID, MAX(match_score) AS match_score FROM(SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,pre.teacher_keyID AS teacher_keyID,post.match_score AS match_score FROM (SELECT * FROM (SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,post.teacher_keyID AS teacher_keyID FROM (SELECT * FROM (SELECT student_keyID, classID, student_underline_email FROM FLATTEN((SELECT __key__.name AS student_keyID, user_email AS student_underline_email, student_lists.path AS classID FROM junyi_20161212.UserData_20161212), classID)) AS pre INNER JOIN EACH (SELECT teacher_keyID, classID FROM(SELECT coaches.name AS teacher_keyID, __key__.path AS classID, code AS classcode, name AS classname FROM junyi_20161212.StudentList_20161212)) AS post ON pre.classID = post.classID)) AS pre INNER JOIN EACH (SELECT classID, MAX(match_score) AS match_score FROM (SELECT classID,match_score FROM (SELECT classID, MAX(match_score) AS match_score FROM (SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,pre.teacher_keyID AS teacher_keyID,post.match_score AS match_score FROM (SELECT * FROM (SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,post.teacher_keyID AS teacher_keyID FROM (SELECT * FROM (SELECT student_keyID, classID, student_underline_email FROM FLATTEN((SELECT __key__.name AS student_keyID, user_email AS student_underline_email, student_lists.path AS classID FROM junyi_20161212.UserData_20161212), classID)) AS pre INNER JOIN EACH (SELECT teacher_keyID, classID FROM(SELECT coaches.name AS teacher_keyID, __key__.path AS classID, code AS classcode, name AS classname FROM junyi_20161212.StudentList_20161212)) AS post ON pre.classID = post.classID)) AS pre INNER JOIN EACH (SELECT keyID AS student_keyID, match_score FROM (SELECT student_keyID AS keyID, MAX(match_score) AS match_score FROM(SELECT student_keyID, teacher_keyID, 1 AS match_score FROM (SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,post.teacher_keyID AS teacher_keyID FROM (SELECT * FROM (SELECT student_keyID, classID, student_underline_email FROM FLATTEN((SELECT __key__.name AS student_keyID, user_email AS student_underline_email, student_lists.path AS classID FROM junyi_20161212.UserData_20161212), classID)) AS pre INNER JOIN EACH (SELECT teacher_keyID, classID FROM(SELECT coaches.name AS teacher_keyID, __key__.path AS classID, code AS classcode, name AS classname FROM junyi_20161212.StudentList_20161212)) AS post ON pre.classID = post.classID))) GROUP BY keyID)) AS post ON pre.student_keyID = post.student_keyID)) GROUP BY classID)) GROUP BY classID) AS post ON pre.classID = post.classID)) GROUP BY keyID) AS pre INNER JOIN EACH (SELECT pre.dot_email AS dot_email,pre.metric AS metric,post.keyID AS keyID FROM (SELECT * FROM (SELECT dot_email, SUM(points_earned) AS metric FROM(SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT dot_email,points_earned,timestamp,time_consumed FROM (SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT user.email AS dot_email, points_earned, seconds_watched AS time_consumed, DATE_ADD(time_watched, 8, 'HOUR') AS timestamp FROM junyi_20161212.VideoLog_20161212)),(SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT user.email AS dot_email, points_earned, time_taken AS time_consumed, DATE_ADD(time_done, 8, 'HOUR') AS timestamp FROM junyi_20161212.ProblemLog_20161212)))) WHERE timestamp >= TIMESTAMP('2016-09-01') AND timestamp <= TIMESTAMP('2016-12-12')) GROUP BY dot_email) AS pre INNER JOIN EACH (SELECT keyID, dot_email FROM (SELECT __key__.name AS keyID, user.email AS dot_email FROM junyi_20161212.UserData_20161212)) AS post ON pre.dot_email = post.dot_email)) AS post ON pre.keyID = post.keyID))) AS pre INNER JOIN EACH (SELECT input, AVG(output) AS output, 1 AS comparekey FROM ( SELECT input, output/101 AS output FROM ( SELECT input, ROW_NUMBER() OVER() AS output FROM ( FLATTEN(( SELECT QUANTILES(metric,101) AS input FROM (SELECT pre.keyID AS keyID,pre.match_score AS match_score,post.dot_email AS dot_email,post.metric AS metric FROM (SELECT * FROM (SELECT student_keyID AS keyID, MAX(match_score) AS match_score FROM(SELECT student_keyID, teacher_keyID, 1 AS match_score FROM (SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,post.teacher_keyID AS teacher_keyID FROM (SELECT * FROM (SELECT student_keyID, classID, student_underline_email FROM FLATTEN((SELECT __key__.name AS student_keyID, user_email AS student_underline_email, student_lists.path AS classID FROM junyi_20161212.UserData_20161212), classID)) AS pre INNER JOIN EACH (SELECT teacher_keyID, classID FROM(SELECT coaches.name AS teacher_keyID, __key__.path AS classID, code AS classcode, name AS classname FROM junyi_20161212.StudentList_20161212)) AS post ON pre.classID = post.classID))) GROUP BY keyID) AS pre INNER JOIN EACH (SELECT pre.dot_email AS dot_email,pre.metric AS metric,post.keyID AS keyID FROM (SELECT * FROM (SELECT dot_email, SUM(points_earned) AS metric FROM(SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT dot_email,points_earned,timestamp,time_consumed FROM (SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT user.email AS dot_email, points_earned, seconds_watched AS time_consumed, DATE_ADD(time_watched, 8, 'HOUR') AS timestamp FROM junyi_20161212.VideoLog_20161212)),(SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT user.email AS dot_email, points_earned, time_taken AS time_consumed, DATE_ADD(time_done, 8, 'HOUR') AS timestamp FROM junyi_20161212.ProblemLog_20161212)))) WHERE timestamp >= TIMESTAMP('2016-09-01') AND timestamp <= TIMESTAMP('2016-12-12')) GROUP BY dot_email) AS pre INNER JOIN EACH (SELECT keyID, dot_email FROM (SELECT __key__.name AS keyID, user.email AS dot_email FROM junyi_20161212.UserData_20161212)) AS post ON pre.dot_email = post.dot_email)) AS post ON pre.keyID = post.keyID))), input)))) GROUP BY input) AS post ON pre.comparekey = post.comparekey))) WHERE compare == 1 GROUP BY keyID, match_score_A))))) AS pre INNER JOIN EACH (SELECT keyID, prob2 FROM (SELECT keyID, match_score AS prob2 FROM (SELECT keyID, match_score FROM (SELECT keyID, timing_match_denumerator/timing_match_numerator AS match_score FROM (SELECT keyID, SUM(match_condition) OVER (PARTITION BY keyID) AS timing_match_denumerator, COUNT(match_condition) OVER (PARTITION BY keyID) AS timing_match_numerator, FROM (SELECT keyID, date, month, weekday, hour, minute, IF((hour == 9 OR hour == 10 OR hour == 11 OR hour == 12 OR hour == 13 OR hour == 14 OR hour == 15),1,0) AS match_condition FROM (SELECT keyID, date, month,

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試圖以三週努力滿足三秒的好奇

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回答速度趕不上問題產生的速度

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資料人員第一線人員

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是否可以更快？

需求提出 規格確認 初步實作 驗證除錯

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Scale in complexity,not size

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IT-centric Business Intelligence

因果關係驗證

資料探索、假設建立、相關性探究

資料人員

第一線人員

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User-centric Business Intelligence

因果關係驗證

資料探索、假設建立、相關性探究

資料人員

第一線人員

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市場上既已存在工具方案 (eg. Tableau)又何必自行研究？

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Complexity is both domain + infrastructure

specific

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How do you encode & implement this part?

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即便是用 Google Bigquery仍是如此SQL 是

Business Logic 的組合語言

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如果可以這樣該有多麼美好？SELECT keyID,userID,dot_email,gender,points,user_nickname,username,userEmail,userSendableEmail,userRole,userCity,userSchool,userGrade,joinedTime,userBirthdate,match_score FROM (SELECT pre.keyID AS keyID,pre.userID AS userID,pre.dot_email AS dot_email,pre.gender AS gender,pre.points AS points,pre.user_nickname AS user_nickname,pre.username AS username,pre.userEmail AS userEmail,pre.userSendableEmail AS userSendableEmail,pre.userRole AS userRole,pre.userCity AS userCity,pre.userSchool AS userSchool,pre.userGrade AS userGrade,pre.joinedTime AS joinedTime,pre.userBirthdate AS userBirthdate,post.match_score AS match_score FROM (SELECT * FROM (SELECT pre.keyID AS keyID,pre.userID AS userID,pre.dot_email AS dot_email,pre.gender AS gender,pre.points AS points,pre.user_nickname AS user_nickname,pre.username AS username,post.userEmail AS userEmail,post.userSendableEmail AS userSendableEmail,post.userRole AS userRole,post.userCity AS userCity,post.userSchool AS userSchool,post.userGrade AS userGrade,post.joinedTime AS joinedTime,post.userBirthdate AS userBirthdate FROM (SELECT * FROM (SELECT keyID, userID, dot_email, gender, points, user_nickname, username FROM (SELECT __key__.name AS keyID, user_id AS userID, user_email AS underline_email, current_user.email AS cu_email, user.email AS dot_email, gender, points, user_nickname, username FROM junyi_20161212.UserData_20161212)) AS pre INNER JOIN EACH (SELECT userEmail, userSendableEmail, userId AS userID, userRole, userCity, userSchool, userGrade, joinedTime, userBirthdate FROM FinalTable.UserFinalTmpInfo) AS post ON pre.userID = post.userID)) AS pre INNER JOIN EACH (SELECT keyID,match_score FROM (SELECT keyID, match_score FROM (SELECT keyID,prob1*prob2 AS match_score FROM (SELECT pre.keyID AS keyID,pre.prob1 AS prob1,post.prob2 AS prob2 FROM (SELECT * FROM (SELECT keyID, prob1 FROM (SELECT keyID, match_score AS prob1 FROM (SELECT keyID, match_score FROM (SELECT keyID, match_score_A*cdf AS match_score FROM (SELECT keyID, MAX(output) AS cdf, match_score_A FROM (SELECT keyID, IF(metric >= input,1,0) AS compare, output, match_score_A FROM (SELECT pre.keyID AS keyID,pre.comparekey AS comparekey,pre.metric AS metric,pre.match_score_A AS match_score_A,post.input AS input,post.output AS output FROM (SELECT * FROM (SELECT keyID, 1 AS comparekey, metric, match_score AS match_score_A FROM (SELECT pre.keyID AS keyID,pre.match_score AS match_score,post.dot_email AS dot_email,post.metric AS metric FROM (SELECT * FROM (SELECT teacher_keyID AS keyID, MAX(match_score) AS match_score FROM(SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,pre.teacher_keyID AS teacher_keyID,post.match_score AS match_score FROM (SELECT * FROM (SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,post.teacher_keyID AS teacher_keyID FROM (SELECT * FROM (SELECT student_keyID, classID, student_underline_email FROM FLATTEN((SELECT __key__.name AS student_keyID, user_email AS student_underline_email, student_lists.path AS classID FROM junyi_20161212.UserData_20161212), classID)) AS pre INNER JOIN EACH (SELECT teacher_keyID, classID FROM(SELECT coaches.name AS teacher_keyID, __key__.path AS classID, code AS classcode, name AS classname FROM junyi_20161212.StudentList_20161212)) AS post ON pre.classID = post.classID)) AS pre INNER JOIN EACH (SELECT classID, MAX(match_score) AS match_score FROM (SELECT classID,match_score FROM (SELECT classID, MAX(match_score) AS match_score FROM (SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,pre.teacher_keyID AS teacher_keyID,post.match_score AS match_score FROM (SELECT * FROM (SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,post.teacher_keyID AS teacher_keyID FROM (SELECT * FROM (SELECT student_keyID, classID, student_underline_email FROM FLATTEN((SELECT __key__.name AS student_keyID, user_email AS student_underline_email, student_lists.path AS classID FROM junyi_20161212.UserData_20161212), classID)) AS pre INNER JOIN EACH (SELECT teacher_keyID, classID FROM(SELECT coaches.name AS teacher_keyID, __key__.path AS classID, code AS classcode, name AS classname FROM junyi_20161212.StudentList_20161212)) AS post ON pre.classID = post.classID)) AS pre INNER JOIN EACH (SELECT keyID AS student_keyID, match_score FROM (SELECT student_keyID AS keyID, MAX(match_score) AS match_score FROM(SELECT student_keyID, teacher_keyID, 1 AS match_score FROM (SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,post.teacher_keyID AS teacher_keyID FROM (SELECT * FROM (SELECT student_keyID, classID, student_underline_email FROM FLATTEN((SELECT __key__.name AS student_keyID, user_email AS student_underline_email, student_lists.path AS classID FROM junyi_20161212.UserData_20161212), classID)) AS pre INNER JOIN EACH (SELECT teacher_keyID, classID FROM(SELECT coaches.name AS teacher_keyID, __key__.path AS classID, code AS classcode, name AS classname FROM junyi_20161212.StudentList_20161212)) AS post ON pre.classID = post.classID))) GROUP BY keyID)) AS post ON pre.student_keyID = post.student_keyID)) GROUP BY classID)) GROUP BY classID) AS post ON pre.classID = post.classID)) GROUP BY keyID) AS pre INNER JOIN EACH (SELECT pre.dot_email AS dot_email,pre.metric AS metric,post.keyID AS keyID FROM (SELECT * FROM (SELECT dot_email, SUM(points_earned) AS metric FROM(SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT dot_email,points_earned,timestamp,time_consumed FROM (SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT user.email AS dot_email, points_earned, seconds_watched AS time_consumed, DATE_ADD(time_watched, 8, 'HOUR') AS timestamp FROM junyi_20161212.VideoLog_20161212)),(SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT user.email AS dot_email, points_earned, time_taken AS time_consumed, DATE_ADD(time_done, 8, 'HOUR') AS timestamp FROM junyi_20161212.ProblemLog_20161212)))) WHERE timestamp >= TIMESTAMP('2016-09-01') AND timestamp <= TIMESTAMP('2016-12-12')) GROUP BY dot_email) AS pre INNER JOIN EACH (SELECT keyID, dot_email FROM (SELECT __key__.name AS keyID, user.email AS dot_email FROM junyi_20161212.UserData_20161212)) AS post ON pre.dot_email = post.dot_email)) AS post ON pre.keyID = post.keyID))) AS pre INNER JOIN EACH (SELECT input, AVG(output) AS output, 1 AS comparekey FROM ( SELECT input, output/101 AS output FROM ( SELECT input, ROW_NUMBER() OVER() AS output FROM ( FLATTEN(( SELECT QUANTILES(metric,101) AS input FROM (SELECT pre.keyID AS keyID,pre.match_score AS match_score,post.dot_email AS dot_email,post.metric AS metric FROM (SELECT * FROM (SELECT student_keyID AS keyID, MAX(match_score) AS match_score FROM(SELECT student_keyID, teacher_keyID, 1 AS match_score FROM (SELECT pre.student_keyID AS student_keyID,pre.classID AS classID,pre.student_underline_email AS student_underline_email,post.teacher_keyID AS teacher_keyID FROM (SELECT * FROM (SELECT student_keyID, classID, student_underline_email FROM FLATTEN((SELECT __key__.name AS student_keyID, user_email AS student_underline_email, student_lists.path AS classID FROM junyi_20161212.UserData_20161212), classID)) AS pre INNER JOIN EACH (SELECT teacher_keyID, classID FROM(SELECT coaches.name AS teacher_keyID, __key__.path AS classID, code AS classcode, name AS classname FROM junyi_20161212.StudentList_20161212)) AS post ON pre.classID = post.classID))) GROUP BY keyID) AS pre INNER JOIN EACH (SELECT pre.dot_email AS dot_email,pre.metric AS metric,post.keyID AS keyID FROM (SELECT * FROM (SELECT dot_email, SUM(points_earned) AS metric FROM(SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT dot_email,points_earned,timestamp,time_consumed FROM (SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT user.email AS dot_email, points_earned, seconds_watched AS time_consumed, DATE_ADD(time_watched, 8, 'HOUR') AS timestamp FROM junyi_20161212.VideoLog_20161212)),(SELECT dot_email, points_earned, timestamp, time_consumed FROM (SELECT user.email AS dot_email, points_earned, time_taken AS time_consumed, DATE_ADD(time_done, 8, 'HOUR') AS timestamp FROM junyi_20161212.ProblemLog_20161212)))) WHERE timestamp >= TIMESTAMP('2016-09-01') AND timestamp <= TIMESTAMP('2016-12-12')) GROUP BY dot_email) AS pre INNER JOIN EACH (SELECT keyID, dot_email FROM (SELECT __key__.name AS keyID, user.email AS dot_email FROM junyi_20161212.UserData_20161212)) AS post ON pre.dot_email = post.dot_email)) AS post ON pre.keyID = post.keyID))), input)))) GROUP BY input) AS post ON pre.comparekey = post.comparekey))) WHERE compare == 1 GROUP BY keyID, match_score_A))))) AS pre INNER JOIN EACH (SELECT keyID, prob2 FROM (SELECT keyID, match_score AS prob2 FROM (SELECT keyID, match_score FROM (SELECT keyID, timing_match_denumerator/timing_match_numerator AS match_score FROM (SELECT keyID, SUM(match_condition) OVER (PARTITION BY keyID) AS timing_match_denumerator, COUNT(match_condition) OVER (PARTITION BY keyID) AS timing_match_numerator, FROM (SELECT keyID, date, month, weekday, hour, minute, IF((hour == 9 OR hour == 10 OR hour == 11 OR hour == 12 OR hour == 13 OR hour == 14 OR hour == 15),1,0) AS match_condition FROM (SELECT keyID, date, month,

class.students <- con_relationshipByClass(dataset, outputUsers.role = "student")

class.teachers <- con_relationshipByClass(dataset, outputUsers.role = "teacher”, student_UserObj = class.students)

Regular_Preparation_Public.teachers <- UserObj_ANDmerge(con_probability_AgreaterthanB(dataset,UserObj.A = class.teachers,UserObj.B = class.students,metric = "seconds”, video = T, prob = T,window.begin = window.begin, window.end = window.end),

con_activity_timing(dataset, prob = T, video = T,complete.mission = T, assign.mission = T,hour = 9:15, window.begin = window.begin, window.end = window.end))

Regular_Preparation_Public.teachers <- view_User_FinalTable(dataset, Regular_Preparation_Public.teachers, output = "data")

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如果可以這樣該有多麼美好？class.students <- con_relationshipByClass(dataset, outputUsers.role = "student")

class.teachers <- con_relationshipByClass(dataset, outputUsers.role = "teacher”, student_UserObj = class.students)

Regular_Preparation_Public.teachers <- UserObj_ANDmerge(con_probability_AgreaterthanB(dataset,UserObj.A = class.teachers,UserObj.B = class.students,metric = "seconds”, video = T, prob = T,window.begin = window.begin, window.end = window.end),

con_activity_timing(dataset, prob = T, video = T,complete.mission = T, assign.mission = T,hour = 9:15, window.begin = window.begin, window.end = window.end))

Regular_Preparation_Public.teachers <- view_User_FinalTable(dataset, Regular_Preparation_Public.teachers, output = "data")

有班級的使用者( 學生 ) 所屬的老師

ViewFunction

比較 A 與 B課堂時間影片跟習題的秒數回傳 A

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以邏輯模組來操作已建置的 SQL

有班級的使用者( 學生 ) 所屬的老師

ViewFunction

比較 A 與 B課堂時間影片跟習題的秒數回傳 A

AB

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邏輯模組的其他好處• Reusable code• Focus on business logic itself• Enabling very complex queries = Allowing rapid hypothesis generation and alignment with data

• Makes validation & debugging easier

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Having data is not enough

Page 23: 解決正確的問題 - 如何讓數據發揮影響力？

Too much data is

Counter-productive

Lost in strange trends (not big pictures)and on numbers that lead to No action

Page 24: 解決正確的問題 - 如何讓數據發揮影響力？
Page 25: 解決正確的問題 - 如何讓數據發揮影響力？

It makes no senseif you can’t do or

no need to do anything about it.

Page 26: 解決正確的問題 - 如何讓數據發揮影響力？

7 天前來過，每次平均消費 3000NTD總共來過 4 次Recency = 7

Frequency = 4Monetary Value = 3000

Page 27: 解決正確的問題 - 如何讓數據發揮影響力？

Monetary Value

Frequency

常來 + 每次來花很多

少來 + 做不到業績

偶爾來一旦來就花很多

常來但每次來都很省

Page 28: 解決正確的問題 - 如何讓數據發揮影響力？

Monetary Value

Frequency

常來 + 每次來花很多

少來 + 做不到業績

偶爾來一旦來就花很多

常來但每次來都很省

來店有禮 吸引你常來蒞臨

Page 29: 解決正確的問題 - 如何讓數據發揮影響力？

Monetary Value

Frequency

常來 + 每次來花很多

少來 + 做不到業績

偶爾來一旦來就花很多

常來但每次來都很省

買滿千送百 給你湊數的誘因

Page 30: 解決正確的問題 - 如何讓數據發揮影響力？

同一件商品，若可以辨識你是價格不敏感者 賣高價品價格敏感者 打折賣

= “Actions we can take”

Page 31: 解決正確的問題 - 如何讓數據發揮影響力？

其他可以用來辨識「價格敏感度」的資料 ?

Page 32: 解決正確的問題 - 如何讓數據發揮影響力？

收入？學生？資產狀況小孩人數？Coupon的使用？是否是當地人？品牌忠誠度？市場熟悉程度？同類型產品，買哪種價位的？

Page 33: 解決正確的問題 - 如何讓數據發揮影響力？

Ignore coupons Offer high price

MaximizeRevenue

Theory: 消費行為受 Price-sensitivity 影響

“Data” “Action” “Outcome”

Page 34: 解決正確的問題 - 如何讓數據發揮影響力？

要倒回來想：主詞是能做什麼(Action)才能產生想要的

Outcome?

Data Action Outcome

Page 35: 解決正確的問題 - 如何讓數據發揮影響力？

Asking a relevant questionis domain specific skill

Page 36: 解決正確的問題 - 如何讓數據發揮影響力？
Page 37: 解決正確的問題 - 如何讓數據發揮影響力？

Take Action = P( 生理數據 ) 非專業人士：

Page 38: 解決正確的問題 - 如何讓數據發揮影響力？

Take Action = P( 生理數據 | 病史 ) 專業人士：

Page 39: 解決正確的問題 - 如何讓數據發揮影響力？

專業人士：P( HR<60 | 不規律呼吸 , 高血壓) = 腦壓上昇

醫療文獻稱這三者合併出現為 Cushing’s reflex

Page 40: 解決正確的問題 - 如何讓數據發揮影響力？

善用資料改善線上教育 slide (https://goo.gl/Yd6ajF)

Page 41: 解決正確的問題 - 如何讓數據發揮影響力？

善用資料改善線上教育 slide (https://goo.gl/Yd6ajF)

Page 42: 解決正確的問題 - 如何讓數據發揮影響力？

https://www.junyiacademy.org/

Page 43: 解決正確的問題 - 如何讓數據發揮影響力？

https://www.junyiacademy.org/

Page 44: 解決正確的問題 - 如何讓數據發揮影響力？

https://www.junyiacademy.org/

Page 45: 解決正確的問題 - 如何讓數據發揮影響力？

善用資料改善線上教育 slide (https://goo.gl/Yd6ajF)

Page 46: 解決正確的問題 - 如何讓數據發揮影響力？

老師及所屬學生的使用行為分析 翻轉教學成就孩子Theory: 好的 practice是可以被複製跟運用的

“Data” “Action” “Outcome”

找出領頭羊以便觀摩

Page 47: 解決正確的問題 - 如何讓數據發揮影響力？
Page 48: 解決正確的問題 - 如何讓數據發揮影響力？

醫師 老師病史詢問、各式檢查執行治療規劃治療方針衛教

評估先備知識安排學習情境、教材評估學習成效給予指導、輔導跟支持選擇教學策略

Page 49: 解決正確的問題 - 如何讓數據發揮影響力？

Data Action Outcome

What is the theory?What cause-effect is already known?

Page 50: 解決正確的問題 - 如何讓數據發揮影響力？

必須深入有關學習的理論才能有更多可能性

Page 51: 解決正確的問題 - 如何讓數據發揮影響力？

學生學習的行為 成就孩子

Learning theories

“Data” “Action” “Outcome”

協助挑選合適的教學策略

Page 52: 解決正確的問題 - 如何讓數據發揮影響力？

三種主流的觀點BehaviorismCognitivismConstructivism

數據最容易應用的思維

Page 53: 解決正確的問題 - 如何讓數據發揮影響力？

Behaviorism 行為主義刺激 行為 後果

Page 54: 解決正確的問題 - 如何讓數據發揮影響力？

刺激 行為 後果古典制約 Classical Conditioning

Page 55: 解決正確的問題 - 如何讓數據發揮影響力？

刺激 行為 後果操作制約 Operant Conditioning

Page 56: 解決正確的問題 - 如何讓數據發揮影響力？
Page 57: 解決正確的問題 - 如何讓數據發揮影響力？

行為主義的觀點Feedback loops + 合適的 Reward搭配可以系統化的影響學習行為

Page 58: 解決正確的問題 - 如何讓數據發揮影響力？

Behaviorism 行為主義• 被動的接受知識• 遺忘是因為久了沒用• 學習就是–行為機率變化 : P(刺激 ) = 行為

• Generalization是因為刺激源類似• 強調practice、 cues、 hints、 rewards …

Page 59: 解決正確的問題 - 如何讓數據發揮影響力？

行為主義的侷限認知資源低的學習 = 很強大、很有效–寫選擇題、填空、背誦、配對比對

認知資源高的學習 = 解釋力很差–學會能賴以工作的外語–做別人沒做過的事、 ill-defined problems–創作、申論題

Page 60: 解決正確的問題 - 如何讓數據發揮影響力？

What can we learn from other theories?

Cognitivism 認知主義–內在 knowledge state變化 State & Path analysis

學習者必須主動的把知識 encode到記憶中–強調新知識跟舊知識的關係 倒帶行為

Page 61: 解決正確的問題 - 如何讓數據發揮影響力？

0:10 – 0:60Peak at 0:30

2:00-2:20

3:50 -

Page 62: 解決正確的問題 - 如何讓數據發揮影響力？

「質數判別方法」影片劇本• 找出 1 to 10哪些是質數？– [0:10] 1 不是質數，也不是合數– [0:25] 合數就是除了質數以外的數– [0:40] 0也不是質數，也不是合數– [0:50] 2 是質數唯一的偶數，其他質數都是奇數

• [2:00] 再次強調，只有 2 是偶數的質數• [2:10] 13 是否為質數？• [3:50] 33 是否為質數？• [4:10] 一個數一定可以被自己跟 1整除• [4:30] 33 可以被 3整除，所以不是質數

Page 63: 解決正確的問題 - 如何讓數據發揮影響力？

把影片內容依主題切成九段

Page 64: 解決正確的問題 - 如何讓數據發揮影響力？

從頭看到尾，只看一次 只看前面的觀念，不做 13,33的練習

前面的觀念看兩次後，才做 13,33練習 重複從頭到尾看很多次

Page 65: 解決正確的問題 - 如何讓數據發揮影響力？

能對教材「倒帶」「暫停」「跳過」是少數能呈現學生對自己的學習做選擇的結果

Page 66: 解決正確的問題 - 如何讓數據發揮影響力？

學生所做的選擇反映他如何建構他自己的知識

Page 67: 解決正確的問題 - 如何讓數據發揮影響力？

Issues not addressed by Behaviorism

Context 情境Intrinsic motivation(內在動機 )–Competence = 成就感–Autonomy = 自我控制感，可以自己做選擇–Relatedness = 社群、夥伴、團隊

Page 68: 解決正確的問題 - 如何讓數據發揮影響力？

使用者 long-term engagement 關鍵1.社群的存在2.內在動機驅動3.Experience本身會 evolve over time ，而非短期的 funnel 或 habit loops

Page 69: 解決正確的問題 - 如何讓數據發揮影響力？

創造一個會隨時間逐漸 unfold 的體驗一個會隨使用者的投入與技能成長改變的體驗


期中報告 - 大腦總指揮

期中報告 - 大腦總指揮

大數據場景運用 - video.openedu.tw5-4)大數據的各類... · 2 1 4 3 大數據行業發展概況 如何運用大數據 典型案例分享 q & a contents

大數據場景運用 - video.openedu.tw5-4)大數據的各類... · 2 1 4 3 大數據行業發展概況 如何運用大數據 典型案例分享 q & a contents

UCP500 ─ 單據 壹、單據簽發 貳、運送單據 參、保險單據 肆、其他單據. 壹、單據簽發 一、含糊的單據簽發人用語 (§20) 二、未規定單據簽發人或內容

UCP500 ─ 單據 壹、單據簽發 貳、運送單據 參、保險單據 肆、其他單據. 壹、單據簽發 一、含糊的單據簽發人用語 (§20) 二、未規定單據簽發人或內容

～ 讓愛，飛越國界 ～

～ 讓愛，飛越國界 ～

貿易單據製作 101-1

貿易單據製作 101-1

讓 WEKA 活在 .net 上

讓 WEKA 活在 .net 上

收據日期金額 收據姓名 收據編號 F16010112 五O棗 F16010001 … · 收據日期金額 收據姓名 收據編號 2016/1/1 1,000 待查 f16010112 2016/1/4 6,267 五o棗

收據日期金額 收據姓名 收據編號 F16010112 五O棗 F16010001 … · 收據日期金額 收據姓名 收據編號 2016/1/1 1,000 待查 f16010112 2016/1/4 6,267 五o棗

讓 app 一上架，就 紅 !

讓 app 一上架，就 紅 !

國家衛生指揮中心 用科技數據守護臺灣 · 用科技數據守護臺灣 2003年臺灣經歷嚴重急性呼吸道症候群（Severe Acute Respiratory Syndrome，以下簡稱SARS）

國家衛生指揮中心 用科技數據守護臺灣 · 用科技數據守護臺灣 2003年臺灣經歷嚴重急性呼吸道症候群（Severe Acute Respiratory Syndrome，以下簡稱SARS）

貿易單據製作 99-1

貿易單據製作 99-1

失智友善社區 - health.tainan.gov.tw75ACEBBE... · 重是失智者仍保有的智慧與能力 ，努力協助其發揮最大功能，讓 失智者過著有尊嚴的生活。

失智友善社區 - health.tainan.gov.tw75ACEBBE... · 重是失智者仍保有的智慧與能力 ，努力協助其發揮最大功能，讓 失智者過著有尊嚴的生活。

マネージャー挨拶 指揮者挨拶 常任指揮者紹介 ハイ …1 目次 マネージャー挨拶 指揮者挨拶 常任指揮者紹介 ハイマートの1年 前期演奏会で扱う曲紹介

マネージャー挨拶 指揮者挨拶 常任指揮者紹介 ハイ …1 目次 マネージャー挨拶 指揮者挨拶 常任指揮者紹介 ハイマートの1年 前期演奏会で扱う曲紹介

讓 HTML5 走進 IPTV Framework

讓 HTML5 走進 IPTV Framework

事件指揮系統 Incident Command System

事件指揮系統 Incident Command System

貿易單據製作 101-2

貿易單據製作 101-2

也讓校友總會更加壯大。 - ntue.edu.tw · 教育創新，讓自己的教學生動 有趣，讓學生樂於學習；四、 認識這強調發揮創造能力的大 環境時代，使自己的創造能力

也讓校友總會更加壯大。 - ntue.edu.tw · 教育創新，讓自己的教學生動 有趣，讓學生樂於學習；四、 認識這強調發揮創造能力的大 環境時代，使自己的創造能力

〈 讓孩子快樂長大 …〉

〈 讓孩子快樂長大 …〉

數據分析報告 (05-06)

數據分析報告 (05-06)

票據權利之轉讓 ( 背書 )

票據權利之轉讓 ( 背書 )

Qgis 據點資料編輯

Qgis 據點資料編輯