{"title":"独角兽数据科学家:最稀有的品种","authors":"Sasa Baskarada, A. Koronios","doi":"10.1108/PROG-07-2016-0053","DOIUrl":null,"url":null,"abstract":"Purpose \n \n \n \n \nMany organizations are seeking unicorn data scientists, that rarest of breeds that can do it all. They are said to be experts in many traditionally distinct disciplines, including mathematics, statistics, computer science, artificial intelligence, and more. The purpose of this paper is to describe authors’ pursuit of these elusive mythical creatures. \n \n \n \n \nDesign/methodology/approach \n \n \n \n \nQualitative data were collected through semi-structured interviews with managers/directors from nine Australian state and federal government agencies with relatively mature data science functions. \n \n \n \n \nFindings \n \n \n \n \nAlthough the authors failed to find evidence of unicorn data scientists, they are pleased to report on six key roles that are considered to be required for an effective data science team. Primary and secondary skills for each of the roles are identified and the resulting framework is then used to illustratively evaluate three data science Master-level degrees offered by Australian universities. \n \n \n \n \nResearch limitations/implications \n \n \n \n \nGiven that the findings presented in this paper have been based on a study with large government agencies with relatively mature data science functions, they may not be directly transferable to less mature, smaller, and less well-resourced agencies and firms. \n \n \n \n \nOriginality/value \n \n \n \n \nThe skills framework provides a theoretical contribution that may be applied in practice to evaluate and improve the composition of data science teams and related training programs.","PeriodicalId":49663,"journal":{"name":"Program-Electronic Library and Information Systems","volume":"51 1","pages":"65-74"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/PROG-07-2016-0053","citationCount":"32","resultStr":"{\"title\":\"Unicorn data scientist: the rarest of breeds\",\"authors\":\"Sasa Baskarada, A. Koronios\",\"doi\":\"10.1108/PROG-07-2016-0053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose \\n \\n \\n \\n \\nMany organizations are seeking unicorn data scientists, that rarest of breeds that can do it all. They are said to be experts in many traditionally distinct disciplines, including mathematics, statistics, computer science, artificial intelligence, and more. The purpose of this paper is to describe authors’ pursuit of these elusive mythical creatures. \\n \\n \\n \\n \\nDesign/methodology/approach \\n \\n \\n \\n \\nQualitative data were collected through semi-structured interviews with managers/directors from nine Australian state and federal government agencies with relatively mature data science functions. \\n \\n \\n \\n \\nFindings \\n \\n \\n \\n \\nAlthough the authors failed to find evidence of unicorn data scientists, they are pleased to report on six key roles that are considered to be required for an effective data science team. Primary and secondary skills for each of the roles are identified and the resulting framework is then used to illustratively evaluate three data science Master-level degrees offered by Australian universities. \\n \\n \\n \\n \\nResearch limitations/implications \\n \\n \\n \\n \\nGiven that the findings presented in this paper have been based on a study with large government agencies with relatively mature data science functions, they may not be directly transferable to less mature, smaller, and less well-resourced agencies and firms. \\n \\n \\n \\n \\nOriginality/value \\n \\n \\n \\n \\nThe skills framework provides a theoretical contribution that may be applied in practice to evaluate and improve the composition of data science teams and related training programs.\",\"PeriodicalId\":49663,\"journal\":{\"name\":\"Program-Electronic Library and Information Systems\",\"volume\":\"51 1\",\"pages\":\"65-74\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1108/PROG-07-2016-0053\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Program-Electronic Library and Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/PROG-07-2016-0053\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Program-Electronic Library and Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/PROG-07-2016-0053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q","JCRName":"Social Sciences","Score":null,"Total":0}
Purpose
Many organizations are seeking unicorn data scientists, that rarest of breeds that can do it all. They are said to be experts in many traditionally distinct disciplines, including mathematics, statistics, computer science, artificial intelligence, and more. The purpose of this paper is to describe authors’ pursuit of these elusive mythical creatures.
Design/methodology/approach
Qualitative data were collected through semi-structured interviews with managers/directors from nine Australian state and federal government agencies with relatively mature data science functions.
Findings
Although the authors failed to find evidence of unicorn data scientists, they are pleased to report on six key roles that are considered to be required for an effective data science team. Primary and secondary skills for each of the roles are identified and the resulting framework is then used to illustratively evaluate three data science Master-level degrees offered by Australian universities.
Research limitations/implications
Given that the findings presented in this paper have been based on a study with large government agencies with relatively mature data science functions, they may not be directly transferable to less mature, smaller, and less well-resourced agencies and firms.
Originality/value
The skills framework provides a theoretical contribution that may be applied in practice to evaluate and improve the composition of data science teams and related training programs.
期刊介绍:
■Automation of library and information services ■Storage and retrieval of all forms of electronic information ■Delivery of information to end users ■Database design and management ■Techniques for storing and distributing information ■Networking and communications technology ■The Internet ■User interface design ■Procurement of systems ■User training and support ■System evaluation