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題名 以資料科學技術進行轉職行為之分析
Career Transition Analysis Using Data Science Techniques
作者 諶宏軍
Chen, Hung Chun
貢獻者 沈錳坤
Shan, Man Kwan
諶宏軍
Chen, Hung Chun
關鍵詞 轉職
資料探勘
分類演算法
相關係數
Career Transition
Data Mining
Classification
Correlation Coefficient
日期 2014
上傳時間 5-Jan-2015 11:22:14 (UTC+8)
摘要 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.
參考文獻 [1] 王文賢，2009，探勘公務人員職系類別轉換對陞遷之影響-以行政及技術類別資料為例，世新大學資訊管學系碩士論文。
[2] 薛冰絜，2010，影響年輕族群工作轉換意願之因素探討，國立中央大學人力資源管理研究所碩士論文。
[3] 阮金聲，2005，護理人員離職預測系統之研究，國立中正大學資訊管理所碩士論文。
[4] 吳姿嬋，2013，景氣衰退的預期與壽險從業人員的自願離職傾向，逢甲大學風險管理與保險學系碩士論文。
[5] P. Domingos, “Metacost: A General Method for Making Classifiers Cost Sensitive,” In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, San Diego, pp. 155-164, 1999.
[6] N. Japkowicz and S. Stephen, “The Class Imbalance Problem: A Systematic Study,” Intelligent Data Analysis Journal, Vol. 6, No. 5, pp. 429-450, 2002.
[7] N. V. Chawla, K. W. Bowyer, L. O. Hall and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, Vol.16, pp. 321-357, 2002.
[8] J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA, 1993.
[9] J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, pp. 81-106, 1986.
[10] G. H. John, and P. Langley, “Estimating Continuous Distributions in Bayesian Classifiers,” In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Mateo, pp. 338-345, 1995.
[11] S. Le Cessie and J. C. van Houwelingen, “Ridge Estimators in Logistic Regression,” Applied Statistics, 41, Vol.1, pp. 191-201, 1992.
[12] C. C. Chang, and C. J. Lin, A Library for Support Vector Machines. URL http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
[13] Y. Freund, and R. E. Schapire, “Experiments with a New Boosting Algorithm,” In Thirteenth International Conference on Machine Learning, San Francisco, pp. 148-156, 1996.
[14] L. Breiman,” Random Forests,” Machine Learning, Vol. 45, pp. 5-32, 2001.
[15] Weka, http://www.cs.waikato.ac.nz/ml/weka/
[16] Weka wiki introduction, http://en.wikipedia.org/wiki/We ka_(machine_learning)
[17] 景氣指標查詢系統，http://index.ndc.gov.tw/
[18] 中華民國統計資訊網－總體統計資料庫，http://ebas1.ebas.gov.tw/pxweb/Dialog/statfile9L.asp
描述 碩士
國立政治大學
資訊科學學系
99971007
103
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0999710071
資料類型 thesis
dc.contributor.advisor 沈錳坤zh_TW
dc.contributor.advisor Shan, Man Kwanen_US
dc.contributor.author (Authors) 諶宏軍zh_TW
dc.contributor.author (Authors) Chen, Hung Chunen_US
dc.creator (作者) 諶宏軍zh_TW
dc.creator (作者) Chen, Hung Chunen_US
dc.date (日期) 2014en_US
dc.date.accessioned 5-Jan-2015 11:22:14 (UTC+8)-
dc.date.available 5-Jan-2015 11:22:14 (UTC+8)-
dc.date.issued (上傳時間) 5-Jan-2015 11:22:14 (UTC+8)-
dc.identifier (Other Identifiers) G0999710071en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/72554-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 99971007zh_TW
dc.description (描述) 103zh_TW
dc.description.abstract (摘要) 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。		zh_TW
dc.description.abstract (摘要) 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.		en_US
dc.description.tableofcontents 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		zh_TW
dc.format.extent 13969637 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0999710071en_US
dc.subject (關鍵詞) 轉職zh_TW
dc.subject (關鍵詞) 資料探勘zh_TW
dc.subject (關鍵詞) 分類演算法zh_TW
dc.subject (關鍵詞) 相關係數zh_TW
dc.subject (關鍵詞) Career Transitionen_US
dc.subject (關鍵詞) Data Miningen_US
dc.subject (關鍵詞) Classificationen_US
dc.subject (關鍵詞) Correlation Coefficienten_US
dc.title (題名) 以資料科學技術進行轉職行為之分析zh_TW
dc.title (題名) Career Transition Analysis Using Data Science Techniquesen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] 王文賢，2009，探勘公務人員職系類別轉換對陞遷之影響-以行政及技術類別資料為例，世新大學資訊管學系碩士論文。
[2] 薛冰絜，2010，影響年輕族群工作轉換意願之因素探討，國立中央大學人力資源管理研究所碩士論文。
[3] 阮金聲，2005，護理人員離職預測系統之研究，國立中正大學資訊管理所碩士論文。
[4] 吳姿嬋，2013，景氣衰退的預期與壽險從業人員的自願離職傾向，逢甲大學風險管理與保險學系碩士論文。
[5] P. Domingos, “Metacost: A General Method for Making Classifiers Cost Sensitive,” In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, San Diego, pp. 155-164, 1999.
[6] N. Japkowicz and S. Stephen, “The Class Imbalance Problem: A Systematic Study,” Intelligent Data Analysis Journal, Vol. 6, No. 5, pp. 429-450, 2002.
[7] N. V. Chawla, K. W. Bowyer, L. O. Hall and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, Vol.16, pp. 321-357, 2002.
[8] J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA, 1993.
[9] J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, pp. 81-106, 1986.
[10] G. H. John, and P. Langley, “Estimating Continuous Distributions in Bayesian Classifiers,” In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Mateo, pp. 338-345, 1995.
[11] S. Le Cessie and J. C. van Houwelingen, “Ridge Estimators in Logistic Regression,” Applied Statistics, 41, Vol.1, pp. 191-201, 1992.
[12] C. C. Chang, and C. J. Lin, A Library for Support Vector Machines. URL http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
[13] Y. Freund, and R. E. Schapire, “Experiments with a New Boosting Algorithm,” In Thirteenth International Conference on Machine Learning, San Francisco, pp. 148-156, 1996.
[14] L. Breiman,” Random Forests,” Machine Learning, Vol. 45, pp. 5-32, 2001.
[15] Weka, http://www.cs.waikato.ac.nz/ml/weka/
[16] Weka wiki introduction, http://en.wikipedia.org/wiki/We ka_(machine_learning)
[17] 景氣指標查詢系統，http://index.ndc.gov.tw/
[18] 中華民國統計資訊網－總體統計資料庫，http://ebas1.ebas.gov.tw/pxweb/Dialog/statfile9L.asp		zh_TW