1. 大數據行銷研究 Big Data Marketing Research
Tamkang University
社群運算與大數據分析 (Social Computing and Big Data Analytics)
1051BDMR08
MIS EMBA (M2262) (8638)
Thu, 12,13,14 (19:20-22:10) (D409)
Min-Yuh Day
戴敏育
Assistant Professor
專任助理教授
Dept. of Information Management, Tamkang University
淡江大學 資訊管理學系
tku.edu.tw/myday/

2. 課程大綱 (Syllabus)
週次 (Week) 日期 (Date) 內容 (Subject/Topics) /09/16 中秋節 (調整放假一天) (Mid-Autumn Festival Holiday)(Day off) /09/23 大數據行銷研究課程介紹 (Course Orientation for Big Data Marketing Research) /09/30 資料科學與大數據行銷 (Data Science and Big Data Marketing) /10/07 大數據行銷分析與研究 (Big Data Marketing Analytics and Research) /10/14 測量構念 (Measuring the Construct) /10/21 測量與量表 (Measurement and Scaling)

3. 課程大綱 (Syllabus)
週次 (Week) 日期 (Date) 內容 (Subject/Topics) /10/28 大數據行銷個案分析 I (Case Study on Big Data Marketing I) /11/04 探索性因素分析 (Exploratory Factor Analysis) /11/11 確認性因素分析 (Confirmatory Factor Analysis) /11/18 期中報告 (Midterm Presentation) /11/25 社群運算與大數據分析 (Social Computing and Big Data Analytics) /12/02 社會網路分析 (Social Network Analysis)

4. 課程大綱 (Syllabus)
週次 (Week) 日期 (Date) 內容 (Subject/Topics) /12/09 大數據行銷個案分析 II (Case Study on Big Data Marketing II) /12/16 社會網絡分析量測與實務 (Measurements and Practices of Social Network Analysis) /12/23 大數據情感分析 (Big Data Sentiment Analysis) /12/30 金融科技行銷研究 (FinTech Marketing Research) /01/06 期末報告 I (Term Project Presentation I) /01/13 期末報告 II (Term Project Presentation II)

5. Outline
Social Computing
Big Data Analysis

6. 教育部資通人才培育計畫 社群運算與巨量資料 課程四大模組
教育部資通人才培育計畫 社群運算與巨量資料 課程四大模組
(1)「社群媒體」(Social Media) (政治大學) (2)「資料科學」 (Data Science) (政治大學) (3)「分析技術」(Analytics Technology) (高雄大學) (淡江大學) (4)「領域應用」(Domain Application) (淡江大學) (政治大學)

7. 1.「社群媒體」(Social Media) (政治大學)
探討 社群媒體和資料分析的概念，以個案方式教學

8. 2. 「資料科學」 (Data Science) (政治大學)
探討 Data Thinking 和 EDA 等， 與DSP或痞客邦合作

9. 3. 「分析技術」(Analytics Technology) (高雄大學) (淡江大學)
列舉重要的分析方法，包括社會網絡分析，文字探勘分析技術簡介。
* 社會網絡分析 (高雄大學)
* 社會網絡量測 (高雄大學)
* 社會網絡分析工具 (高雄大學)
* 文字探勘分析技術簡介 (淡江大學)

10. 4. 「領域應用」(Domain Application) (淡江大學) (政治大學)
區分 Domain Knowledge ，聚焦探討各種商業行銷和輿情分析等
* 社群媒體行銷分析 (淡江大學)
* 社群媒體情感分析 (淡江大學)

11. Social Computing

12. Social Network Analysis

13. Social Computing Social Network Analysis Link mining
Community Detection
Social Recommendation

14. Business Insights with Social Analytics

15. Analyzing the Social Web: Social Network Analysis

16. Jennifer Golbeck (2013), Analyzing the Social Web, Morgan Kaufmann
Source:

17. Devangana Khokhar (2015), Gephi Cookbook, Packt Publishing
Source:

18. Social Network Analysis (SNA) Facebook TouchGraph

19. Graph Theory

20. Graph C A D B E

21. Graph g = (V, E)

22. Vertex (Node)

23. Vertices (Nodes)

24. Edge

25. Edges

26. Arc

27. Arcs

28. Source: https://www.youtube.com/watch?v=89mxOdwPfxA
Undirected Graph C A D B E
Source:

29. Source: https://www.youtube.com/watch?v=89mxOdwPfxA
Directed Graph C A D B E
Source:

30. Measurements of Social Network Analysis

31. Exploratory Network Analysis
Source:

32. Looking for a “Simple Small Truth”? What Data Visualization Should Do?
1. Make complex things simple
2. Extract small information from large data
3. Present truth, do not deceive
Source:

33. Measurements

34. Looking for Orderness in Data
Make varying 3 cursors simultaneously to extract meaningful patterns
Source:

35. “Zoom” cursor on Quantitative Data
Global
- connectivity
- density
- centralization
Local
- communities
- bridges between communities
- local centers vs periphery
Individual
- centrality
- distances
- neighborhood
- location
- local authority vs hub
Source:

36. “Crossing” cursor on Quantitative Data
Social
- who with whom
- communities
- brokerage
- influence and power
- homophily
Semantic
- topics
- thematic clusters
Geographic
- spatial phenomena
Source:

37. “Timeline” cursor on Temporal Data
Evolution of social ties
Evolution of communities
Evolution of topics
Source:

38. Source: http://sebastien.pro/gephi-icwsm-tutorial.pdf
SNA Guideline
Source:

39. Source: https://www.youtube.com/watch?v=89mxOdwPfxA
Degree C A D B E
Source:

40. Source: https://www.youtube.com/watch?v=89mxOdwPfxA
Degree C
A: 2
B: 4
C: 2
D:1
E: 1 A D B E
Source:

41. Source: https://www.youtube.com/watch?v=89mxOdwPfxA
Density C A D B E
Source:

42. Source: https://www.youtube.com/watch?v=89mxOdwPfxA
Density
Edges (Links): 5
Total Possible Edges: 10
Density: 5/10 = 0.5 C A D B E
Source:

43. Density A E I C G H B J D F Nodes (n): 10 Edges (Links): 13
Total Possible Edges: (n * (n-1)) / 2 = (10 * 9) / 2 = 45
Density: 13/45 = 0.29

44. Diameter
radius (r)
diameter (d)

45. Diameter A E I C G H B J D F

46. Diameter Geodesic Path (Shortest Path)B J D F
A I : Diameter = 4

47. Which Node is Most Important?G H B J D F

48. Centrality
Important or prominent actors are those that are linked or involved with other actors extensively.
A person with extensive contacts (links) or communications with many other people in the organization is considered more important than a person with relatively fewer contacts.
The links can also be called ties. A central actor is one involved in many ties.
Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data”

49. Social Network Analysis (SNA)
Degree Centrality
Betweenness Centrality
Closeness Centrality

50. Degree Centrality

51. Social Network Analysis: Degree CentralityH B J D F

52. Social Network Analysis: Degree Centrality
Node
Score
Standardized Score A 2
2/10 = 0.2 B C 5
5/10 = 0.5 D 3
3/10 = 0.3 E F G 4
4/10 = 0.4 H I 1
1/10 = 0.1 J A E I C G H B J D F

53. Betweenness Centrality

54. Betweenness centrality:
Connectivity
Number of shortest paths going through the actor

55. Betweenness Centrality
Where gjk = the number of shortest paths connecting jk
gjk(i) = the number that actor i is on.
Normalized Betweenness Centrality
Number of pairs of vertices excluding the vertex itself
Source:

56. Betweenness Centrality
BC: 0/1 = 0
BD: 0/1 = 0
BE: 0/1 = 0
CD: 0/1 = 0
CE: 0/1 = 0
DE: 0/1 = 0
Total: C A D B E
A: Betweenness Centrality = 0

57. Betweenness Centrality
AC: 0/1 = 0
AD: 1/1 = 1
AE: 1/1 = 1
CD: 1/1 = 1
CE: 1/1 = 1
DE: 1/1 = 1
Total: C A D B E
B: Betweenness Centrality = 5

58. Betweenness Centrality
AB: 0/1 = 0
AD: 0/1 = 0
AE: 0/1 = 0
BD: 0/1 = 0
BE: 0/1 = 0
DE: 0/1 = 0
Total: C A D B E
C: Betweenness Centrality = 0

59. Betweenness Centrality
D: 0
E: 0 A D B E

60. Which Node is Most Important?F G H B E A C I D J F H B E A C D J

61. Which Node is Most Important?F G H B E A C I D J F G H E A I D J

62. Betweenness CentralityD

63. Betweenness Centrality
BC: 0/1 = 0
BD: 0/1 = 0
BE: 0/1 = 0
CD: 0/1 = 0
CE: 0/1 = 0
DE: 0/1 = 0
Total: C A D B E
A: Betweenness Centrality = 0

64. Closeness Centrality

65. Social Network Analysis: Closeness Centrality
CA: 1
CB: 1
CD: 1
CE: 1
CF: 2
CG: 1
CH: 2
CI: 3
CJ: 3
Total=15 A E I C G H B J D F
C: Closeness Centrality = 15/9 = 1.67

66. Social Network Analysis: Closeness Centrality
GA: 2
GB: 2
GC: 1
GD: 2
GE: 1
GF: 1
GH: 1
GI: 2
GJ: 2
Total=14 A E I C G H B J D F
G: Closeness Centrality = 14/9 = 1.56

67. Social Network Analysis: Closeness Centrality
HA: 3
HB: 3
HC: 2
HD: 2
HE: 2
HF: 2
HG: 1
HI: 1
HJ: 1
Total=17 A E I C G H B J D F
H: Closeness Centrality = 17/9 = 1.89

68. Social Network Analysis: Closeness CentralityG H B J D F
G: Closeness Centrality = 14/9 = 1.56 1
C: Closeness Centrality = 15/9 = 1.67 2
H: Closeness Centrality = 17/9 = 1.89 3

69. Big Data Analytics

70. Source: https://www-01.ibm.com/software/data/bigdata/
Big Data 4 V
Source:

71. Value

72. History of Data Science
Source:

73. Big Data Technologies are Enabling a New Approach
Source:

74. Big Data Analytics and Data Mining

75. Source: http://www.amazon.com/gp/product/1466568704
Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications
Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications
Source:

76. Architecture of Big Data Analytics
Big Data Analytics Applications
Big Data Sources
Big Data Transformation
Big Data Platforms & Tools
* Internal
* External
* Multiple formats
* Multiple locations
* Multiple applications
Middleware
Hadoop
MapReduce
Pig
Hive
Jaql
Zookeeper
Hbase
Cassandra
Oozie
Avro
Mahout
Others
Queries
Transformed Data
Big Data Analytics
Raw Data
Extract Transform Load
Reports
Data Warehouse
OLAP
Traditional Format CSV, Tables
Data Mining
Source: Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications

77. Architecture of Big Data Analytics
Big Data Analytics Applications
Big Data Sources
Big Data Transformation
Big Data Platforms & Tools
* Internal
* External
* Multiple formats
* Multiple locations
* Multiple applications
Data Mining
Big Data Analytics Applications
Middleware
Hadoop
MapReduce
Pig
Hive
Jaql
Zookeeper
Hbase
Cassandra
Oozie
Avro
Mahout
Others
Queries
Transformed Data
Big Data Analytics
Raw Data
Extract Transform Load
Reports
Data Warehouse
OLAP
Traditional Format CSV, Tables
Data Mining
Source: Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications

78. Social Big Data Mining (Hiroshi Ishikawa, 2015)
Hiroshi Ishikawa (2015), Social Big Data Mining, CRC Press
Source:

79. Architecture for Social Big Data Mining (Hiroshi Ishikawa, 2015)
Enabling Technologies
Analysts
Integrated analysis
Model Construction
Explanation by Model
Integrated analysis model
Conceptual Layer
Construction and confirmation of individual hypothesis
Description and execution of application-specific task
Natural Language Processing
Information Extraction
Anomaly Detection
Discovery of relationships among heterogeneous data
Large-scale visualization
Data Mining
Application specific task
Multivariate analysis
Logical Layer
Parallel distrusted processing
Social Data
Software
Hardware
Physical Layer
Source: Hiroshi Ishikawa (2015), Social Big Data Mining, CRC Press

80. Business Intelligence (BI) Infrastructure
Source: Kenneth C. Laudon & Jane P. Laudon (2014), Management Information Systems: Managing the Digital Firm, Thirteenth Edition, Pearson.

81. Creating Value with Big Data Analytics: Making Smarter Marketing Decisions, Peter C. Verhoef and Edwin Kooge, Routledge, 2016
Source:

82. Big Data Value Creation Model
Creating Value with Big Data Analytics: Making Smarter Marketing Decisions
Big Data Assets
Big Data Capabilities
Big Data Analytics
Big Data Value
Decision support
Value to the firm
Data
People
Insights
Systems
Data
Data
Data
Actions/ campaigns
Data
Value to the customer
Data
Models
Information based products/ solutions
Data
process
Organization
Data
Source: Peter C. Verhoef and Edwin Kooge (2016), Creating Value with Big Data Analytics: Making Smarter Marketing Decisions, Routledge

83. Digital Data Platform for Enterprises Big Data Analytics
Source:

84. A Marketing Mix Framework for Big Data Management
Source: Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for business intelligence through the lens of marketing mix.  Big Data Research,2(1),

85. A resource-based view of the impact of Big Data on competitive advantage
Source: Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing.  Journal of Business Research, 69(2),

86. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521, no (2015):

87. Source: LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521, no (2015):

88. Sebastian Raschka (2015), Python Machine Learning, Packt Publishing
Source: Sebastian Raschka (2015), Python Machine Learning, Packt Publishing
Source:
Source:

89. Sunila Gollapudi (2016), Practical Machine Learning, Packt Publishing
Source: Sunila Gollapudi (2016), Practical Machine Learning, Packt Publishing
Source:
Source:

90. Machine Learning Models
Deep Learning
Kernel
Association rules
Ensemble
Decision tree
Dimensionality reduction
Clustering
Regression Analysis
Bayesian
Instance based
Source: Sunila Gollapudi (2016), Practical Machine Learning, Packt Publishing

91. Data Scientist 資料科學家
Source:

92. Deep Learning Intelligence from Big Data
Source:

93. Data Scientist: The Sexiest Job of the 21st Century (Davenport & Patil, 2012)(HBR)
Source: Davenport, T. H., & Patil, D. J. (2012). Data Scientist. Harvard business review

94. Source: Davenport, T. H. , & Patil, D. J. (2012). Data Scientist
Source: Davenport, T. H., & Patil, D. J. (2012). Data Scientist. Harvard business review

95. Data Scientist Profile
Quantitative
Technical
Curious and Creative
Data Scientist
Skeptical
Communicative and Collaborative
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015

96. Key Roles for a Successful Analytics Project
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015

97. Key Outputs from a Successful Analytics Project
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015

98. Data Science vs. Big Data vs. Data Analytics
Source:

99. Data Science vs. Big Data vs. Data Analytics
Source:

100. What are they used?
Source:

101. Data Science What are the Skills Required?
Source:

102. Source: https://www. simplilearn

103. Source: http://mattturck

104. Summary
Social Computing
Big Data Analysis

105. References Hiroshi Ishikawa (2015), Social Big Data Mining, CRC Press
Jennifer Golbeck (2013), Analyzing the Social Web, Morgan Kaufmann
Sunila Gollapudi (2016), Practical Machine Learning, Packt Publishing
Devangana Khokhar (2015), Gephi Cookbook, Packt Publishing
EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications