{"id":258,"date":"2023-03-24T11:13:33","date_gmt":"2023-03-24T18:13:33","guid":{"rendered":"https:\/\/dornsife.usc.edu\/environmental-studies\/?page_id=258"},"modified":"2026-03-24T15:45:20","modified_gmt":"2026-03-24T22:45:20","slug":"m-s-in-environmental-data-science","status":"publish","type":"page","link":"https:\/\/dornsife.usc.edu\/environmental-studies\/m-s-in-environmental-data-science\/","title":{"rendered":"M.S. in Environmental Data Science"},"content":{"rendered":"\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <p dir=\"ltr\">The application of data science to complex environmental problems has become increasingly important in evaluating large-scale environmental challenges. Data science technologies complement interdisciplinary analyses of complex environmental issues ranging over many topics such as climate change, water and air pollution, policy analysis, terrestrial and aquatic ecosystem management, biodiversity and many others. The curriculum is designed to prepare individuals to develop and use sophisticated analytical and computer-based methods to assess and protect the Earth\u2019s natural resources. The\u00a0<a href=\"https:\/\/viterbigradadmission.usc.edu\/programs\/masters\/msprograms\/data-science\/ms-in-environmental-data-science\/\" target=\"_blank\" rel=\"noopener\">USC Masters of Science in Environmental Data Science<\/a>\u00a0would provide students with the knowledge and skills to:<\/p>\n<ul>\n<li dir=\"ltr\">\n<p dir=\"ltr\">Work at the intersection of interdisciplinary fields of environmental science and data science<\/p>\n<\/li>\n<li dir=\"ltr\">\n<p dir=\"ltr\">Leverage data to form and frame relevant questions in environmental management and sustainability, identify patterns, and make actionable insights to understand and protect Earth\u2019s natural resources<\/p>\n<\/li>\n<li dir=\"ltr\">\n<p dir=\"ltr\">Understand the use of emerging technologies in data science and their application to environmental problems<\/p>\n<\/li>\n<li dir=\"ltr\">\n<p dir=\"ltr\">Efficiently integrate data science approaches and systems to analyze real-world environmental data<\/p>\n<\/li>\n<\/ul>\n<p dir=\"ltr\">The\u00a0<a href=\"https:\/\/catalogue.usc.edu\/preview_program.php?catoid=21&amp;poid=30540&amp;returnto=8930\">curriculum<\/a>\u00a0is designed to be accessible to students with any background, including students with an environmental sciences background and no computer science knowledge as well as students with a computer science knowledge and no environmental studies background. Students will acquire the necessary skills to analyze environmental data in a variety of contexts. At the start of the program, students will be offered specific opportunities in problems-based learning in partnership with the Wrigley Institute for Environmental Studies.<\/p>\n<p dir=\"ltr\">For more information, please contact <a href=\"mailto:sfraga@usc.edu\">Sean Fraga<\/a>\u00a0or the Data Science program academic advisor at\u00a0<a href=\"mailto:datasci@usc.edu\">datasci@usc.edu<\/a>.<\/p>\n<p dir=\"ltr\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-259\" src=\"https:\/\/dornsife.usc.edu\/environmental-studies\/wp-content\/uploads\/sites\/61\/2023\/01\/Screen_Shot_2020-02-03_at_10.27.53_AM-233x300.png\" alt=\"\" width=\"233\" height=\"300\" srcset=\"https:\/\/dornsife.usc.edu\/environmental-studies\/wp-content\/uploads\/sites\/61\/2023\/01\/Screen_Shot_2020-02-03_at_10.27.53_AM-233x300.png 233w, https:\/\/dornsife.usc.edu\/environmental-studies\/wp-content\/uploads\/sites\/61\/2023\/01\/Screen_Shot_2020-02-03_at_10.27.53_AM.png 1090w\" sizes=\"(max-width: 233px) 100vw, 233px\" \/><\/p>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n\n\n\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <p>The Environmental Studies Program acknowledges our presence on the ancestral and unceded territory of the Tongva\/Gabrielino\/Kizh people and their neighbors, the Chumash, Tataviam, Kitanemuk, Serrano, Cahuilla, Payomkawichum, Acjachemen, Ipai-Tipai, Kumeyaay, and Quechan peoples. Indigenous stewardship and rightful claims to these lands have never been voluntarily relinquished nor legally extinguished. We pay our respect to the members and elders of these communities \u2013 past, present, and future \u2013 who remain stewards and advocates of this region.<\/p>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":64,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-258","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>M.S. in Environmental Data Science - Environmental Studies<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dornsife.usc.edu\/environmental-studies\/m-s-in-environmental-data-science\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"M.S. in Environmental Data Science - 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