{"title":"数据生命周期","authors":"Jeannette M. Wing","doi":"10.1162/99608F92.E26845B4","DOIUrl":null,"url":null,"abstract":"To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user. Data science is thus much more than data analysis, e.g., using techniques from machine learning and statistics; extracting this value takes a lot of work, before and after data analysis. Moreover, data privacy and data ethics need to be considered at each phase of the life cycle.Keywordsanalysis, collection, data life cycle, ethics, generation, interpretation, management, privacy, storage, story-telling, visualization","PeriodicalId":23712,"journal":{"name":"Volume 4 Issue 1","volume":"31 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"58","resultStr":"{\"title\":\"The Data Life Cycle\",\"authors\":\"Jeannette M. Wing\",\"doi\":\"10.1162/99608F92.E26845B4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user. Data science is thus much more than data analysis, e.g., using techniques from machine learning and statistics; extracting this value takes a lot of work, before and after data analysis. Moreover, data privacy and data ethics need to be considered at each phase of the life cycle.Keywordsanalysis, collection, data life cycle, ethics, generation, interpretation, management, privacy, storage, story-telling, visualization\",\"PeriodicalId\":23712,\"journal\":{\"name\":\"Volume 4 Issue 1\",\"volume\":\"31 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"58\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Volume 4 Issue 1\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1162/99608F92.E26845B4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 4 Issue 1","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1162/99608F92.E26845B4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user. Data science is thus much more than data analysis, e.g., using techniques from machine learning and statistics; extracting this value takes a lot of work, before and after data analysis. Moreover, data privacy and data ethics need to be considered at each phase of the life cycle.Keywordsanalysis, collection, data life cycle, ethics, generation, interpretation, management, privacy, storage, story-telling, visualization