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| 研究生： |
諶宏軍 Chen, Hung Chun |
|---|---|
| 論文名稱： |
以資料科學技術進行轉職行為之分析 Career Transition Analysis Using Data Science Techniques |
| 指導教授： |
沈錳坤
Shan, Man Kwan |
| 學位類別： |
碩士
Master |
| 系所名稱： |
理學院 - 資訊科學系碩士在職專班 Excutive Master Program of Computer Science |
| 論文出版年： | 2014 |
| 畢業學年度： | 103 |
| 語文別： | 中文 |
| 論文頁數： | 75 |
| 中文關鍵詞： | 轉職 、資料探勘 、分類演算法 、相關係數 |
| 外文關鍵詞： | Career Transition, Data Mining, Classification, Correlation Coefficient |
| 相關次數： | 點閱：123 下載：25 |
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轉職對於職涯發展來說，是非常重要的人生課題；而求職者目前在面臨轉職問題時，大多時候顯得手足無措，只能詢問親友的經驗或者憑著直覺找自己有興趣的工作；整個求職的過程就像是拿人生當賭注，運氣不好時即可能賠上美好的未來。
本篇研究使用國內某知名人力銀行的求職者資料，採用資料科學的方式，利用大量求職者的實際轉職資料來做資料分析與探勘，分析轉職高峰期、工作轉換頻率、跨職類轉職、跨產業轉職及轉職與景氣的關係，並使用J48、Naïve Bayesian Classifier、Logistic Regression、Random Forest、AdaBoost和Support Vector Machines這6種分類方法來預測轉職行為。
為了方便呈現實驗結果，本研究使用Google App Engine建立了一個轉職分析查詢系統，透過分析結果可以了解台灣各產業與各職類的轉職趨勢，而轉職預測功能也可以提供給求職者與人資人員做為參考。
Career transition is important for employees. However, most of job seekers are helpless in decision of career transition. They can only make the decision based on the experience from their friends and family members, or by intuition. The decision of job seeking is like a gamble that may lose a better future when they faced with bad luck.
This research tried to analyse and discover the behaviours of job transition from the job seeking data based on the data science approach. The job seeker’s data used in the study was obtained from the well-known job bank’s database. We analyse the behaviours of the job transition, including the peak months of transition, transition frequency, cross-job and cross-industry career transition. Moreover, we investigate the methods to predict the behavior of job transfer. Six kinds of classification algorithms were used to predict the behavior of career transfer, including the J48, Naïve Bayesian Classifier, Logistic Regression, Random Forest, AdaBoost and SVM.
We develop the web-based Career Transition Analysis System to provide users the capability for behaviour analysis and prediction of career transition based on Google App Engine. The findings in this study are helpful for industry trends and career transition forecasts for job seeker and human resource staffs.
第一章 前言 1
1.1 研究背景與動機 1
1.2 研究目的及方法 2
1.3 論文貢獻 2
1.4 論文架構 3
第二章 相關研究 4
2.1 轉職相關學術研究 4
2.2 轉職相關人力資源系統 5
2.2.1 104升學就業地圖 5
2.2.2 104職務大百科 7
2.2.3 1111 TAT轉職測評 8
第三章 研究方法 9
3.1 資料來源 9
3.2 資料前處理(Data Preprocessing) 16
3.2.1 資料清理(Data Cleaning) 16
3.2.2 資料整合(Data Integration) 17
3.2.3 資料轉換(Data Transformation) 17
3.2.4 資料縮減(Data Reduction) 18
3.2.5 解決類別不平衡問題(Solve the Class Imbalance Problem) 18
3.3 J48 23
3.4 Naïve Bayesian Classifier 24
3.5 Logistic Regression 26
3.6 Support Vector Machine 26
3.7 AdaBoost 27
3.8 Random Forest 27
第四章 實驗 28
4.1 實驗資料 28
4.2 實驗方法 28
4.3 實驗結果及分析 29
4.3.1 轉職高峰期 29
4.3.2 工作轉換頻率分析 34
4.3.3 跨職類轉職 49
4.3.4 跨產業轉職 52
4.3.5 轉職與景氣的關係 55
4.3.6 轉職預測 59
4.4 系統實作 71
第五章 結論與未來研究方向 73
5.1 結論 73
5.2 未來研究方向 73
參考文獻 74
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