政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/72554

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/72554

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/72554

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/72554

Title:	以資料科學技術進行轉職行為之分析 Career Transition Analysis Using Data Science Techniques
Authors:	諶宏軍 Chen, Hung Chun
Contributors:	沈錳坤 Shan, Man Kwan 諶宏軍 Chen, Hung Chun
Keywords:	轉職資料探勘分類演算法相關係數 Career Transition Data Mining Classification Correlation Coefficient
Date:	2014
Issue Date:	2015-01-05 11:22:14 (UTC+8)
Abstract:	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.
Reference:	[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
Description:	碩士國立政治大學資訊科學學系 99971007 103
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0999710071
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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