{"title":"工程数据科学的开源主动学习课程","authors":"Z. del Rosario","doi":"10.21105/jose.00117","DOIUrl":null,"url":null,"abstract":"This work provides open-source content for an active learning curriculum in data science. The scope of the content is sufficient for a full-semester introduction to scientifically reproducible statistical computation, data wrangling, visualization, basic statistical literacy, and data-driven modeling. The content is broken into short exercises that introduce new concepts, and longer challenges that encourage students to develop those skills in an open-ended context.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Open-Source Active Learning Curriculum for Data Science in Engineering\",\"authors\":\"Z. del Rosario\",\"doi\":\"10.21105/jose.00117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work provides open-source content for an active learning curriculum in data science. The scope of the content is sufficient for a full-semester introduction to scientifically reproducible statistical computation, data wrangling, visualization, basic statistical literacy, and data-driven modeling. The content is broken into short exercises that introduce new concepts, and longer challenges that encourage students to develop those skills in an open-ended context.\",\"PeriodicalId\":75094,\"journal\":{\"name\":\"The Journal of open source education\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Journal of open source education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21105/jose.00117\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of open source education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21105/jose.00117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Open-Source Active Learning Curriculum for Data Science in Engineering
This work provides open-source content for an active learning curriculum in data science. The scope of the content is sufficient for a full-semester introduction to scientifically reproducible statistical computation, data wrangling, visualization, basic statistical literacy, and data-driven modeling. The content is broken into short exercises that introduce new concepts, and longer challenges that encourage students to develop those skills in an open-ended context.