博碩士論文 etd-0623114-002134 詳細資訊


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姓名 陳台樺(Tai Hua Chen) 電子郵件信箱 ctaihua.home@gmail.com
畢業系所 高階經營碩士班(EMBA)
畢業學位 碩士(Master) 畢業時期 102學年第2學期
論文名稱(中) 資料分析碩士學位課程建議之研究
論文名稱(英) COMPARISON AND RECOMMENDATION OF PROGRAM CURRICULUM FOR MASTER OF SCIENCE
IN DATA ANALYTICS
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    紙本論文:1 年後公開 (2015-07-29 公開)

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    摘要(中) 最近這幾年,由於電腦硬體和網路的成本越來越低,也由於科技的發展越來越人性化。人們逐漸習慣地會把所有資料透過軟體服務的方式分享給所有的朋友。企業為了給客戶更好的服務也會把相關的資料以數位化的方式記錄在電腦之中。這些大量的資料帶出了無限,高價值的可能性。同時,配合著高成熟度的海量資料技術。機構以及企業都希望藉由這技術在茫茫大海的資料之中取得有價值的資訊。
    因此,企業們對相關人才的需求一年比一年高。此需求的增加速度遠高於人才的產生速度。有鑑於此,很多大學在這幾年陸續的設立資料分析的碩士學位課程。大學們無不希望此學程的畢業生除了可以盡量的滿足人才的需求,另外也期望他們的所學可以對機構們有所助益。但是,由於這趨勢的發展太急太快。大學們都根據自己的經驗和觀點來設計課程。本研究的目的是希望以資料分析的技術來發展出最適當的課程組合。期望學生可以藉由這些課程的學習增強其就業的能力。
    摘要(英) The causality of workforce forecast and university new degree program establishment is obvious. According to the report of McKinsey&Company and IDC in 2011, the shortage of Big Data or Data Analytic talent would reach 190,000 for the analytical positions and 1.5 million for data-literate managers, which is about 50 to 60 percent higher than its regular supply in 2018.
    Universities have found this trend and start to catch up. Within the last few years, many universities have launched new master programs in Data Analytics, though their names are diverse to a large extent. Due to the cutting-edge nature of data analytics, there is no common course structure. This research engages in a survey of curriculums of about 80 Data Analytics master programs that we found from their universities' Web Site. We classify the courses in these curriculums into five categories, namely Data Science, Capstone, Business, Information Technology and Application, and perform an analysis on the various curriculums. Our initial study indicates most curriculum imposes a mixed course structure, involving courses from several categories. Through the detailed analysis, we provide suggested curriculums with different focuses.
    關鍵字(中)
  • 資料分析的碩士學位課程
  • 資料分析
  • 網路
  • 數位化
  • 海量資料
  • 關鍵字(英)
  • Information Technology
  • Data Analytics
  • Big Data
  • IDC
  • 論文目次 CHAPTER 1- INTRODUCTION 1
    1.1 BACKGROUND 1
    1.2 STATEMENT OF PROBLEM 4
    1.3 PURPOSE OF THE STUDY 5
    1.4 RESEARCH QUESTIONS 6
    1.5 SIGNIFICANCE OF THE STUDY 7
    1.6 RESEARCH FRAMEWORK 8
    1.7 ORGANIZATION OF THE STUDY 9
    CHAPTER 2- LITERATURE REVIEW 11
    2.1 DATA AND INFORMATION 12
    2.1.1 Data types 13
    2.2 BIG DATA FLOOD 15
    2.2.2 The concepts of Big Data 15
    2.2.3 The usage of Big Data 18
    2.2.4 Big Data talents capacities 25
    2.2.5 Big Data talents shortages 31
    2.3 DEPARTMENT DESIGN DEFINITIONS AND FRAMEWORKS 33
    2.3.6 Department Vision 35
    2.3.7 Curriculum design 35
    2.3.8 Outcomes and Competencies 37
    2.3.9 Curriculum Map 37
    2.3.10 Benchmarking 38
    CHAPTER 3- METHODOLOGY 39
    3.1 DATA COLLECTION 41
    3.2 CODING 46
    3.2.1 Competency Coding of MSDA program 49
    3.2.2 Curriculum Coding of MSDA program 54
    3.3 CORRELATION ANALYSIS 62
    3.3.3 Phi coefficient technology 63
    3.3.4 Correlation analysis procedure of MSDA program 64
    3.4 TOOLS 68
    CHAPTER 4- RESULTS 70
    4.1 STATISTICS ABOUT MSDA PROGRAM COMPETENCIES 71
    4.1.1 Program competency distribution of Technology module 72
    4.1.2 Program competency distribution of Application module 73
    4.1.3 Program competency distribution of Management Issues and Soft Skills module 74
    4.1.4 Program competency distribution of Theory module 75
    4.1.5 Program competency distribution of others module 76
    4.2 STATISTICS ABOUT MSDA PROGRAM CURRICULUMS 78
    4.2.1 Course Type distribution of Information Technology module 79
    4.2.2 Course Type distribution of Application module 80
    4.2.3 Course Type distribution of Business module 81
    4.2.4 Course Type distribution of Data Science module 83
    4.2.5 Course Type distribution of Capstone module 84
    4.3 STATISTICS ABOUT MSDA PROGRAMS 86
    4.3.1 Country distribution of MSDA degree 86
    4.3.2 Establishment Year of MSDA program 87
    4.3.3 Offering School of MSDA program 88
    4.3.4 Course duration distribution of MSDA program 89
    4.4 CORRELATION BETWEEN COMPETENCY AND CURRICULUM 91
    4.4.1 Phi correlation coefficient of competency module and curriculum module 91
    4.4.2 Phi correlation coefficient of competency code and curriculum code 92
    CHAPTER 5- CONCLUSION 100
    5.1 BLUEPRINT OF MSDA CURRICULUM 100
    5.2 COMPETENCY MODEL OF MSDA 102
    5.3 LIMITATIONS OF THE STUDY 103
    5.4 IMPLICATIONS FOR FUTURE RESEARCH 103
    REFERENCES 104
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    口試委員
  • 楊婉秀 - 召集委員
  • 徐士傑 - 委員
  • 黃三益 - 指導教授
  • 口試日期 2014-07-12 繳交日期 2014-07-29

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